A frame semantic overview of NLP-based information extraction for cancer-related EHR notes
---
paper_title: Hierarchical attention networks for information extraction from cancer pathology reports

paper_content:

Objective ::: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufﬁciently capture syntactic and semantic contexts from free-text documents. ::: ::: ::: Materials and Methods ::: Data for our analyses were obtained from 942 deidentiﬁed pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classiﬁcation, matched to G1-G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network. ::: ::: ::: Results ::: Our results demonstrate that for both information tasks, HAN performed signiﬁcantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macro F-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460). ::: ::: ::: Conclusions ::: HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.
---
paper_title: Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

paper_content:

Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
---
paper_title: Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record

paper_content:

Background. Difficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates. Objective. To use Electronic Health Record (EHR) data to identify patients with prior CRC testing. Design. A clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests. Setting. Large academic medical center. Patients. 200 patients ≥50 years old who had completed ≥2 non-acute primary care visits within a 1-year period. Measures. Recall and precision of the NLP system, billing records, and human chart review were compared to a reference standard of human review of all available information sources. Results. For identification of all CRC t...
---
paper_title: DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

paper_content:

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR .
---
paper_title: Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports

paper_content:

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.
---
paper_title: Integrating Feature Selection and Feature Extraction Methods With Deep Learning to Predict Clinical Outcome of Breast Cancer

paper_content:

In many microarray studies, classifiers have been constructed based on gene signatures to predict clinical outcomes for various cancer sufferers. However, signatures originating from different studies often suffer from poor robustness when used in the classification of data sets independent from which they were generated from. In this paper, we present an unsupervised feature learning framework by integrating a principal component analysis algorithm and autoencoder neural network to identify different characteristics from gene expression profiles. As the foundation for the obtained features, an ensemble classifier based on the AdaBoost algorithm (PCA-AE-Ada) was constructed to predict clinical outcomes in breast cancer. During the experiments, we established an additional classifier with the same classifier learning strategy (PCA-Ada) in order to perform as a baseline to the proposed method, where the only difference is the training inputs. The area under the receiver operating characteristic curve index, Matthews correlation coefficient index, accuracy, and other evaluation parameters of the proposed method were tested on several independent breast cancer data sets and compared with representative gene signature-based algorithms including the baseline method. Experimental results demonstrate that the proposed method using deep learning techniques performs better than others.
---
paper_title: Deep learning enabled national cancer surveillance

paper_content:

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this talk I will discuss the latest deep learning technology, presenting both theoretical and practical perspectives that are relevant to natural language processing of clinical pathology reports. Using different deep learning architectures, I will present benchmark studies for various information extraction tasks and discuss their importance in supporting a comprehensive and scalable national cancer surveillance program.
---
paper_title: FrameNet: A Knowledge Base for Natural Language Processing

paper_content:

Prof. Charles J. Fillmore had a lifelong interest in lexical semantics, and this culminated in the latter part of his life in a major research project, the FrameNet Project at the International Computer Science Institute in Berkeley, California (http://framenet. icsi.berkeley.edu). This paper reports on the background of this ongoing project, its connections to Fillmore’s other research interests, and briefly outlines applications and current directions of growth for FrameNet, including FrameNets in languages other than English.
---
paper_title: Extracting timing and status descriptors for colonoscopy testing from electronic medical records

paper_content:

Colorectal cancer (CRC) screening rates are low despite confirmed benefits. The authors investigated the use of natural language processing (NLP) to identify previous colonoscopy screening in electronic records from a random sample of 200 patients at least 50 years old. The authors developed algorithms to recognize temporal expressions and ‘status indicators’, such as ‘patient refused’, or ‘test scheduled’. The new methods were added to the existing KnowledgeMap concept identifier system, and the resulting system was used to parse electronic medical records (EMR) to detect completed colonoscopies. Using as the ‘gold standard’ expert physicians’ manual review of EMR notes, the system identified timing references with a recall of 0.91 and precision of 0.95, colonoscopy status indicators with a recall of 0.82 and precision of 0.95, and references to actually completed colonoscopies with recall of 0.93 and precision of 0.95. The system was superior to using colonoscopy billing codes alone. Health services researchers and clinicians may find NLP a useful adjunct to traditional methods to detect CRC screening status. Further investigations must validate extension of NLP approaches for other types of CRC screening applications.
---
paper_title: caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research.

paper_content:

The authors report on the development of the Cancer Tissue Information Extraction System (caTIES)dan application that supports collaborative tissue banking and text mining by leveraging existing natural language processing methods and algorithms, grid communication and security frameworks, and query visualization methods. The system fills an important need for textderived clinical data in translational research such as tissue-banking and clinical trials. The design of caTIES addresses three critical issues for informatics support of translational research: (1) federation of research data sources derived from clinical systems; (2) expressive graphical interfaces for concept-based text mining; and (3) regulatory and security model for supporting multicenter collaborative research. Implementation of the system at several Cancer Centers across the country is creating a potential network of caTIES repositories that could provide millions of de-identified clinical reports to users. The system provides an end-to-end application of medical natural language processing to support multiinstitutional translational research programs.
---
paper_title: Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study

paper_content:

Background: In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correlate the treatment regimen with the prognosis to develop evidence-based radiation therapy paradigms. These data are available mainly in forms of narrative texts or table formats with heterogeneous vocabularies. Manual extraction of the related information from these data can be time consuming and labor intensive, which is not ideal for large studies. Objective: The objective of this study was to adapt the interactive information extraction platform Information and Data Extraction using Adaptive Learning (IDEAL-X) to extract treatment and prognosis data for patients with locally advanced or inoperable non–small cell lung cancer (NSCLC). Methods: We transformed patient treatment and prognosis documents into normalized structured forms using the IDEAL-X system for easy data navigation. The adaptive learning and user-customized controlled toxicity vocabularies were applied to extract categorized treatment and prognosis data, so as to generate structured output. Results: In total, we extracted data from 261 treatment and prognosis documents relating to 50 patients, with overall precision and recall more than 93% and 83%, respectively. For toxicity information extractions, which are important to study patient posttreatment side effects and quality of life, the precision and recall achieved 95.7% and 94.5% respectively. Conclusions: The IDEAL-X system is capable of extracting study data regarding NSCLC chemoradiation patients with significant accuracy and effectiveness, and therefore can be used in large-scale radiotherapy clinical data studies. [JMIR Med Inform 2018;6(1):e8]
---
paper_title: Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

paper_content:

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.
---
paper_title: Automated extraction of Biomarker information from pathology reports

paper_content:

Pathology reports are written in free-text form, which precludes efficient data gathering. We aimed to overcome this limitation and design an automated system for extracting biomarker profiles from accumulated pathology reports. We designed a new data model for representing biomarker knowledge. The automated system parses immunohistochemistry reports based on a “slide paragraph” unit defined as a set of immunohistochemistry findings obtained for the same tissue slide. Pathology reports are parsed using context-free grammar for immunohistochemistry, and using a tree-like structure for surgical pathology. The performance of the approach was validated on manually annotated pathology reports of 100 randomly selected patients managed at Seoul National University Hospital. High F-scores were obtained for parsing biomarker name and corresponding test results (0.999 and 0.998, respectively) from the immunohistochemistry reports, compared to relatively poor performance for parsing surgical pathology findings. However, applying the proposed approach to our single-center dataset revealed information on 221 unique biomarkers, which represents a richer result than biomarker profiles obtained based on the published literature. Owing to the data representation model, the proposed approach can associate biomarker profiles extracted from an immunohistochemistry report with corresponding pathology findings listed in one or more surgical pathology reports. Term variations are resolved by normalization to corresponding preferred terms determined by expanded dictionary look-up and text similarity-based search. Our proposed approach for biomarker data extraction addresses key limitations regarding data representation and can handle reports prepared in the clinical setting, which often contain incomplete sentences, typographical errors, and inconsistent formatting.
---
paper_title: Natural Language Processing in Oncology: A Review.

paper_content:

IMPORTANCE ::: Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest important clinical variables trapped in the free-text narratives within electronic medical records. ::: ::: ::: OBSERVATIONS ::: Natural language processing can be used as a tool for oncological evidence-based research and quality improvement. Oncologists interested in applying NLP for clinical research can play pivotal roles in building NLP systems and, in doing so, contribute to both oncological and clinical NLP research. Herein, we provide an introduction to NLP and its potential applications in oncology, a description of specific tools available, and a review on the state of the current technology with respect to cancer case identification, staging, and outcomes quantification. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: More automated means of leveraging unstructured data from daily clinical practice is crucial as therapeutic options and access to individual-level health information increase. Research-minded oncologists may push the avenues of evidence-based research by taking advantage of the new technologies available with clinical NLP. As continued progress is made with applying NLP toward oncological research, incremental gains will lead to large impacts, building a cost-effective infrastructure for advancing cancer care.
---
paper_title: Classifying Lung Cancer Knowledge in PubMed According to GO Terms Using Extreme Learning Machine

paper_content:

For a well-established digital library e.g., PubMed, searching in terms of a newly established ontology e.g., Gene Ontology GO is an extremely difficult task. Making such a digital library adaptive to any new ontology or to reorganize knowledge automatically is our main objective. The decomposition of the knowledge base into classes is a first step toward our main objective. In this paper, we will demonstrate an automated linking scheme for PubMed citations with GO terms using an improved version of extreme learning machine ELM type algorithms. ELM is an emergent technology, which has shown excellent performance in large data classification problems, with fast learning speeds.
---
paper_title: Effective Mapping of Biomedical Text to the UMLS Metathesaurus: The MetaMap Program

paper_content:

Abstract ::: The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.
---
paper_title: Application of Text Information Extraction System for Real-Time Cancer Case Identification in an Integrated Healthcare Organization

paper_content:

Background: Surgical pathology reports (SPR) contain rich clinical diagnosis information. The text information extraction system (TIES) is an end-to-end application leveraging natural language processing technologies and focused on the processing of pathology and/or radiology reports. Methods: We deployed the TIES system and integrated SPRs into the TIES system on a daily basis at Kaiser Permanente Southern California. The breast cancer cases diagnosed in December 2013 from the Cancer Registry (CANREG) were used to validate the performance of the TIES system. The National Cancer Institute Metathesaurus (NCIM) concept terms and codes to describe breast cancer were identified through the Unified Medical Language System Terminology Service (UTS) application. The identified NCIM codes were used to search for the coded SPRs in the back-end datastore directly. The identified cases were then compared with the breast cancer patients pulled from CANREG. Results: A total of 437 breast cancer concept terms and 14 combinations of “breast” and “cancer” terms were identified from the UTS application. A total of 249 breast cancer cases diagnosed in December 2013 was pulled from CANREG. Out of these 249 cases, 241 were successfully identified by the TIES system from a total of 457 reports. The TIES system also identified an additional 277 cases that were not part of the validation sample. Out of the 277 cases, 11% were determined as highly likely to be cases after manual examinations, and 86% were in CANREG but were diagnosed in months other than December of 2013. Conclusions: The study demonstrated that the TIES system can effectively identify potential breast cancer cases in our care setting. Identified potential cases can be easily confirmed by reviewing the corresponding annotated reports through the front-end visualization interface. The TIES system is a great tool for identifying potential various cancer cases in a timely manner and on a regular basis in support of clinical research studies.
---
paper_title: Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning

paper_content:

Objective To help cancer registrars efficiently and accurately identify reportable cancer cases. ::: ::: Material and Methods The Cancer Registry Control Panel (CRCP) was developed to detect mentions of reportable cancer cases using a pipeline built on the Unstructured Information Management Architecture – Asynchronous Scaleout (UIMA-AS) architecture containing the National Library of Medicine’s UIMA MetaMap annotator as well as a variety of rule-based UIMA annotators that primarily act to filter out concepts referring to nonreportable cancers. CRCP inspects pathology reports nightly to identify pathology records containing relevant cancer concepts and combines this with diagnosis codes from the Clinical Electronic Data Warehouse to identify candidate cancer patients using supervised machine learning. Cancer mentions are highlighted in all candidate clinical notes and then sorted in CRCP’s web interface for faster validation by cancer registrars. ::: ::: Results CRCP achieved an accuracy of 0.872 and detected reportable cancer cases with a precision of 0.843 and a recall of 0.848. CRCP increases throughput by 22.6% over a baseline (manual review) pathology report inspection system while achieving a higher precision and recall. Depending on registrar time constraints, CRCP can increase recall to 0.939 at the expense of precision by incorporating a data source information feature. ::: ::: Conclusion CRCP demonstrates accurate results when applying natural language processing features to the problem of detecting patients with cases of reportable cancer from clinical notes. We show that implementing only a portion of cancer reporting rules in the form of regular expressions is sufficient to increase the precision, recall, and speed of the detection of reportable cancer cases when combined with off-the-shelf information extraction software and machine learning.
---
paper_title: Talking About My Care: Detecting Mentions of Hormonal Therapy Adherence Behavior in an Online Breast Cancer Community.

paper_content:

Hormonal therapy adherence is challenging for many patients with hormone-receptor-positive breast cancer. Gaining intuition into their adherence behavior would assist in improving outcomes by pinpointing, and eventually addressing, why patients fail to adhere. While traditional adherence studies rely on survey-based methods or electronic medical records, online health communities provide a supplemental data source to learn about such behavior and often on a much larger scale. In this paper, we focus on an online breast cancer discussion forum and propose a framework to automatically extract hormonal therapy adherence behavior (HTAB) mentions. The framework compares medical term usage when describing when a patient is taking hormonal therapy medication and interrupting their treatment (e.g., stop/pause taking medication). We show that by using shallow neural networks, in the form of wordlvec, the learned features can be applied to build efficient HTAB mention classifiers. Through medical term comparison, we find that patients who exhibit an interruption behavior are more likely to mention depression and their care providers, while patients with continuation behavior are more likely to mention common side effects (e.g., hot flashes, nausea and osteoporosis), vitamins and exercise.
---
paper_title: Using Twitter for breast cancer prevention: an analysis of breast cancer awareness month

paper_content:

BackgroundOne in eight women will develop breast cancer in her lifetime. The best-known awareness event is breast cancer awareness month (BCAM). BCAM month outreach efforts have been associated with increased media coverage, screening mammography and online information searching. Traditional mass media coverage has been enhanced by social media. However, there is a dearth of literature about how social media is used during awareness-related events. The purpose of this research was to understand how Twitter is being used during BCAM.MethodsThis was a cross-sectional, descriptive study. We collected breast cancer- related tweets from 26 September - 12 November 2012, using Twitter’s application programming interface. We classified Twitter users into organizations, individuals, and celebrities; each tweet was classified as an original or a retweet, and inclusion of a mention, meaning a reference to another Twitter user with @username. Statistical methods included ANOVA and chi square. For content analysis, we used computational linguistics techniques, specifically the MALLET implementation of the unsupervised topic modeling algorithm Latent Dirichlet Allocation.ResultsThere were 1,351,823 tweets by 797,827 unique users. Tweets spiked dramatically the first few days then tapered off. There was an average of 1.69 tweets per user. The majority of users were individuals. Nearly all of the tweets were original. Organizations and celebrities posted more often than individuals. On average celebrities made far more impressions; they were also retweeted more often and their tweets were more likely to include mentions. Individuals were more likely to direct a tweet to a specific person. Organizations and celebrities emphasized fundraisers, early detection, and diagnoses while individuals tweeted about wearing pink.ConclusionsTweeting about breast cancer was a singular event. The majority of tweets did not promote any specific preventive behavior. Twitter is being used mostly as a one-way communication tool. To expand the reach of the message and maximize the potential for word-of-mouth marketing using Twitter, organizations need a strategic communications plan to ensure on-going social media conversations. Organizations may consider collaborating with individuals and celebrities in these conversations. Social media communication strategies that emphasize fundraising for breast cancer research seem particularly appropriate.
---
paper_title: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

paper_content:

We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at . The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text.
---
paper_title: Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

paper_content:

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.
---
paper_title: Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer

paper_content:

Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in breast cancer, distant recurrence identification still relies heavily on manual chart review. In this study, we aim to develop a model that identifies distant recurrences in breast cancer using clinical narratives and structured data from EHR. We apply MetaMap to extract features from clinical narratives and also retrieve structured clinical data from EHR. Using these features, we train a support vector machine model to identify distant recurrences in breast cancer patients. We train the model using 1,396 double-annotated subjects and validate the model using 599 double-annotated subjects. In addition, we validate the model on a set of 4,904 single-annotated subjects as a generalization test. We obtained a high area under curve (AUC) score of 0.92 (SD=0.01) in the cross-validation using the training dataset, then obtained AUC scores of 0.95 and 0.93 in the held-out test and generalization test using 599 and 4,904 samples respectively. Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.
---
paper_title: Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records.

paper_content:

Purpose ::: Risk stratification underlies system-wide efforts to promote the delivery of appropriate prostate cancer care. Although the elements of risk stratum are available in the electronic medical record, manual data collection is resource intensive. Therefore, we investigated the feasibility and accuracy of an automated data extraction method using natural language processing (NLP) to determine prostate cancer risk stratum. ::: ::: ::: Methods ::: Manually collected clinical stage, biopsy Gleason score, and preoperative prostate-specific antigen (PSA) values from our prospective prostatectomy database were used to categorize patients as low, intermediate, or high risk by D'Amico risk classification. NLP algorithms were developed to automate the extraction of the same data points from the electronic medical record, and risk strata were recalculated. The ability of NLP to identify elements sufficient to calculate risk (recall) was calculated, and the accuracy of NLP was compared with that of manually collected data using the weighted Cohen's κ statistic. ::: ::: ::: Results ::: Of the 2,352 patients with available data who underwent prostatectomy from 2010 to 2014, NLP identified sufficient elements to calculate risk for 1,833 (recall, 78%). NLP had a 91% raw agreement with manual risk stratification (κ = 0.92; 95% CI, 0.90 to 0.93). The κ statistics for PSA, Gleason score, and clinical stage extraction by NLP were 0.86, 0.91, and 0.89, respectively; 91.9% of extracted PSA values were within ± 1.0 ng/mL of the manually collected PSA levels. ::: ::: ::: Conclusion ::: NLP can achieve more than 90% accuracy on D'Amico risk stratification of localized prostate cancer, with adequate recall. This figure is comparable to other NLP tasks and illustrates the known trade off between recall and accuracy. Automating the collection of risk characteristics could be used to power real-time decision support tools and scale up quality measurement in cancer care.
---
paper_title: Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

paper_content:

Abstract Objective: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. Materials and Methods: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and ...
---
paper_title: Automatic Processing of Anatomic Pathology Reports in the Italian Language to Enhance the Reuse of Clinical Data.

paper_content:

Medical reports often contain a lot of relevant information in the form of free text. To reuse these unstructured texts for biomedical research, it is important to extract structured data from them. In this work, we adapted a previously developed information extraction system to the oncology domain, to process a set of anatomic pathology reports in the Italian language. The information extraction system relies on a domain ontology, which was adapted and refined in an iterative way. The final output was evaluated by a domain expert, with promising results.
---
paper_title: Using machine learning to parse breast pathology reports

paper_content:

PURPOSE ::: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. ::: ::: ::: METHODS ::: We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women's Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. ::: ::: ::: RESULTS ::: The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. ::: ::: ::: CONCLUSIONS ::: Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.
---
paper_title: Pathologic findings in reduction mammoplasty procedures identified by natural language processing of breast pathology reports: A surrogate for the population incidence of cancer and high risk lesions.

paper_content:

e13569Background: Breast reduction surgery removes a random sample of breast tissue in otherwise asymptomatic women and thus provides a method to evaluate the background incidence of breast patholo...
---
paper_title: Development of a Natural Language Processing Engine to Generate Bladder Cancer Pathology Data for Health Services Research

paper_content:

OBJECTIVE ::: To take the first step toward assembling population-based cohorts of patients with bladder cancer with longitudinal pathology data, we developed and validated a natural language processing (NLP) engine that abstracts pathology data from full-text pathology reports. ::: ::: ::: METHODS ::: Using 600 bladder pathology reports randomly selected from the Department of Veterans Affairs, we developed and validated an NLP engine to abstract data on histology, invasion (presence vs absence and depth), grade, the presence of muscularis propria, and the presence of carcinoma in situ. Our gold standard was based on an independent review of reports by 2 urologists, followed by adjudication. We assessed the NLP performance by calculating the accuracy, the positive predictive value, and the sensitivity. We subsequently applied the NLP engine to pathology reports from 10,725 patients with bladder cancer. ::: ::: ::: RESULTS ::: When comparing the NLP output to the gold standard, NLP achieved the highest accuracy (0.98) for the presence vs the absence of carcinoma in situ. Accuracy for histology, invasion (presence vs absence), grade, and the presence of muscularis propria ranged from 0.83 to 0.96. The most challenging variable was depth of invasion (accuracy 0.68), with an acceptable positive predictive value for lamina propria (0.82) and for muscularis propria (0.87) invasion. The validated engine was capable of abstracting pathologic characteristics for 99% of the patients with bladder cancer. ::: ::: ::: CONCLUSION ::: NLP had high accuracy for 5 of 6 variables and abstracted data for the vast majority of the patients. This now allows for the assembly of population-based cohorts with longitudinal pathology data.
---
paper_title: DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

paper_content:

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR .
---
paper_title: Extent of Risk-Aligned Surveillance for Cancer Recurrence Among Patients With Early-Stage Bladder Cancer

paper_content:

Importance Cancer care guidelines recommend aligning surveillance frequency with underlying cancer risk, ie, more frequent surveillance for patients at high vs low risk of cancer recurrence. Objective To assess the extent to which such risk-aligned surveillance is practiced within US Department of Veterans Affairs facilities by classifying surveillance patterns for low- vs high-risk patients with early-stage bladder cancer. Design, Setting, and Participants US national retrospective cohort study of a population-based sample of patients diagnosed with low-risk or high-risk early-stage bladder between January 1, 2005, and December 31, 2011, with follow-up through December 31, 2014. Analyses were performed March 2017 to April 2018. The study included all Veterans Affairs facilities (n = 85) where both low- and high-risk patients were treated. Exposures Low-risk vs high-risk cancer status, based on definitions from the European Association of Urology risk stratification guidelines and on data extracted from diagnostic pathology reports via validated natural language processing algorithms. Main Outcomes and Measures Adjusted cystoscopy frequency for low-risk and high-risk patients for each facility, estimated using multilevel modeling. Results The study included 1278 low-risk and 2115 high-risk patients (median [interquartile range] age, 77 [71-82] years; 99% [3368 of 3393] male). Across facilities, the adjusted frequency of surveillance cystoscopy ranged from 3.7 to 6.2 (mean, 4.8) procedures over 2 years per patient for low-risk patients and from 4.6 to 6.0 (mean, 5.4) procedures over 2 years per patient for high-risk patients. In 70 of 85 facilities, surveillance was performed at a comparable frequency for low- and high-risk patients, differing by less than 1 cystoscopy over 2 years. Surveillance frequency among high-risk patients statistically significantly exceeded surveillance among low-risk patients at only 4 facilities. Across all facilities, surveillance frequencies for low- vs high-risk patients were moderately strongly correlated (r = 0.52;P Conclusions and Relevance Patients with early-stage bladder cancer undergo cystoscopic surveillance at comparable frequencies regardless of risk. This finding highlights the need to understand barriers to risk-aligned surveillance with the goal of making it easier for clinicians to deliver it in routine practice.
---
paper_title: Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

paper_content:

The paper assesses the utility of Medtex on automating Cancer Registry notifications from narrative histology and cytology reports from the Queensland state-wide pathology information system. A corpus of 45.3 million pathology HL7 messages (including 119,581 histology and cytology reports) from a Queensland pathology repository for the year of 2009 was analysed by Medtex for cancer notification. Reports analysed by Medtex were consolidated at a patient level and compared against patients with notifiable cancers from the Queensland Oncology Repository (QOR). A stratified random sample of 1,000 patients was manually reviewed by a cancer clinical coder to analyse agreements and discrepancies. Sensitivity of 96.5% (95% confidence interval: 94.5-97.8%), specificity of 96.5% (95.3-97.4%) and positive predictive value of 83.7% (79.6-86.8%) were achieved for identifying cancer notifiable patients. Medtex achieved high sensitivity and specificity across the breadth of cancers, report types, pathology laboratories and pathologists throughout the State of Queensland. The high sensitivity also resulted in the identification of cancer patients that were not found in the QOR. High sensitivity was at the expense of positive predictive value; however, these cases may be considered as lower priority to Cancer Registries as they can be quickly reviewed. Error analysis revealed that system errors tended to be tumour stream dependent. Medtex is proving to be a promising medical text analytic system. High value cancer information can be generated through intelligent data classification and extraction on large volumes of unstructured pathology reports.
---
paper_title: Automatic Extraction of Breast Cancer Information from Clinical Reports

paper_content:

The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features. Its underlying information extraction pipeline employs a rule-based grammar approach that is integrated with semantic technologies to determine the relevant information from the patient record. The accuracy of the system, developed with nine thousand clinical documents, reaches accuracy levels of 90% for lymph node status and 69% for the structurally most complex feature, the hormone status.
---
paper_title: Machine learning to parse breast pathology reports in Chinese

paper_content:

Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics community to support automating this work via machine learning in English. Our contribution is to provide an automated approach to construct such structured databases in Chinese, and to set the stage for extraction from other languages. We collected 2104 de-identified Chinese benign and malignant breast pathology reports from Hunan Cancer Hospital. Physicians with native Chinese proficiency reviewed the reports and annotated a variety of binary and numerical pathologic entities. After excluding 78 cases with a bilateral lesion in the same report, 1216 cases were used as a training set for the algorithm, which was then refined by 405 development cases. The Natural language processing algorithm was tested by using the remaining 405 cases to evaluate the machine learning outcome. The model was used to extract 13 binary entities and 8 numerical entities. When compared to physicians with native Chinese proficiency, the model showed a per-entity accuracy from 91 to 100% for all common diagnoses on the test set. The overall accuracy of binary entities was 98% and of numerical entities was 95%. In a per-report evaluation for binary entities with more than 100 training cases, 85% of all the testing reports were completely correct and 11% had an error in 1 out of 22 entities. We have demonstrated that Chinese breast pathology reports can be automatically parsed into structured data using standard machine learning approaches. The results of our study demonstrate that techniques effective in parsing English reports can be scaled to other languages.
---
paper_title: Structured Pathology Reporting for Cancer from Free Text: Lung Cancer Case Study

paper_content:

Objective: To automatically generate structured reports for cancer, including TNM (Tumour-Node-Metastases) staging information, from free-text (non-structured) pathology reports. Method: A symbolic rule-based classification approach was proposed to identify symbols (or clinical concepts) in free-text reports that were subsumed by items specified in a structured report. Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) was used as a base ontology to provide the semantics and relationships between concepts for subsumption querying. Synthesised values from the structured report such as TNM stages were also classified by building logic from relevant structured report items. The College of American Pathologists’ (CAP) surgical lung resection cancer checklist was used to demonstrate the methodology. Results: Checklist items were identified in the free text report and used for structured reporting. The synthesised TNM staging values classified by the system were evaluated against explicitly mentioned TNM stages from 487 reports and achieved an overall accuracy of 78%, 89% and 95% for T, N and M stages respectively. Conclusion: A system to generate structured cancer case reports from free-text pathology reports using symbolic rule-based classification techniques was developed and shows promise. The approach can be easily adapted for other cancer case structured reports.
---
paper_title: Pathologic findings in reduction mammoplasty specimens: a surrogate for the population prevalence of breast cancer and high-risk lesions

paper_content:

Mammoplasty removes random samples of breast tissue from asymptomatic women providing a unique method for evaluating background prevalence of breast pathology in normal population. Our goal was to identify the rate of atypical breast lesions and cancers in women of various ages in the largest mammoplasty cohort reported to date. We analyzed pathologic reports from patients undergoing bilateral mammoplasty, using natural language processing algorithm, verified by human review. Patients with a prior history of breast cancer or atypia were excluded. A total of 4775 patients were deemed eligible. Median age was 40 (range 13–86) and was higher in patients with any incidental finding compared to patients with normal reports (52 vs. 39 years, p = 0.0001). Pathological findings were detected in 7.06% (337) of procedures. Benign high-risk lesions were found in 299 patients (6.26%). Invasive carcinoma and ductal carcinoma in situ were detected in 15 (0.31%) and 23 (0.48%) patients, respectively. The rate of atypias and cancers increased with age. The overall rate of abnormal findings in asymptomatic patients undergoing mammoplasty was 7.06%, increasing with age. As these results are based on random sample of breast tissue, they likely underestimate the prevalence of abnormal findings in asymptomatic women.
---
paper_title: Filter pruning of Convolutional Neural Networks for text classification: A case study of cancer pathology report comprehension

paper_content:

Convolutional Neural Networks (CNN) have recently demonstrated effective performance in many Natural Language Processing tasks. In this study, we explore a novel approach for pruning a CNN's convolution filters using our new data-driven utility score. We have applied this technique to an information extraction task of classifying a dataset of cancer pathology reports by cancer type, a highly imbalanced dataset. Compared to standard CNN training, our new algorithm resulted in a nearly .07 increase in the micro-averaged F1-score and a strong .22 increase in the macro-averaged F1-score using a model with nearly a third fewer network weights. We show how directly utilizing a network's interpretation of data can result in strong performance gains, particularly with severely imbalanced datasets.
---
paper_title: Creating a rule based system for text mining of Norwegian breast cancer pathology reports

paper_content:

National cancer registries collect cancer related information from multiple sources and make it available for research. Part of this information originates from pathology reports, and in this pre-study the possibility of a system for automatic extraction of information from Norwegian pathology reports is investigated. A set of 40 pathology reports describing breast cancer tissue samples has been used to develop a rule based system for information extraction. To validate the performance of this system its output has been compared to the data produced by experts doing manual encoding of the same pathology reports. On average, a precision of 80%, a recall of 98% and an F-score of 86% has been achieved, showing that such a system is indeed feasible.
---
paper_title: Deep learning enabled national cancer surveillance

paper_content:

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this talk I will discuss the latest deep learning technology, presenting both theoretical and practical perspectives that are relevant to natural language processing of clinical pathology reports. Using different deep learning architectures, I will present benchmark studies for various information extraction tasks and discuss their importance in supporting a comprehensive and scalable national cancer surveillance program.
---
paper_title: Assessing the natural language processing capabilities of IBM Watson for oncology using real Australian lung cancer cases.

paper_content:

e18229Background: Optimal treatment decisions require information from multiple sources and formats even in the context of electronic medical records. In addition, structured data such as that coll...
---
paper_title: FrameNet: A Knowledge Base for Natural Language Processing

paper_content:

Prof. Charles J. Fillmore had a lifelong interest in lexical semantics, and this culminated in the latter part of his life in a major research project, the FrameNet Project at the International Computer Science Institute in Berkeley, California (http://framenet. icsi.berkeley.edu). This paper reports on the background of this ongoing project, its connections to Fillmore’s other research interests, and briefly outlines applications and current directions of growth for FrameNet, including FrameNets in languages other than English.
---
paper_title: Abstract PR05: Using medical informatics to evaluate the risk of colorectal cancer in patients with clinically diagnosed sessile serrated polyps

paper_content:

Background : Recent research suggests that in addition to advanced adenomas, sessile serrated polyps (SSPs) may be important precursors for proximal colon cancer. In this study, we conducted the first large cohort study to evaluate the risk of colorectal cancer (CRC) in patients diagnosed with SSPs through usual care. Methods : The University of Washington Medical Center (UWMC) uses a comprehensive electronic medical records (EMR) system to track patient demographics and health-related information, including diagnoses and procedures for all patients. We used procedure codes to identify a cohort of patients receiving colonoscopies at UWMC from 2003-2013. Natural language processing of text in the final diagnosis section of the pathology report was used to characterize the type of polyps present at each colonoscopy procedure, including non-advanced conventional adenomas, advanced conventional adenomas (defined as having villous histology, high-grade dysplasia, or a diameter ≥ 10 mm), and sessile serrated polyps. These colonoscopy records were then linked to the Puget Sound Surveillance, Epidemiology, and End Results Cancer Registry (SEER) and subsequent EMR data to identify incident CRCs occurring through December 31, 2014 within this cohort. Those who lived outside of the SEER catchment area or who had prior colectomy, inflammatory bowel disease, or CRC were excluded from analyses. Cox proportional hazards regression models were used to calculate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) comparing the risk of CRC in each polyp group to those who were polyp-free at an index colonoscopy. HR estimates were adjusted for age, sex, race/ethnicity, smoking status, and body mass index. Results : From 2003 through 2013, 32,136 colonoscopies were performed at UWMC, and 17,424 colonoscopies from 14,846 patients met the study inclusion criteria. Of these patients, 8,908 were polyp-free, 4,145 had only non-advanced conventional adenomas, 927 had advanced conventional adenomas and no SSPs, 314 had ≥1 SSP and ≥1 conventional adenoma, and 552 had SSPs and no conventional adenomas at an index colonoscopy. Median follow-up time in the study cohort was 5.8 years, and 66 incident colorectal cancers occurred during the follow-up period. The risk of incident CRC in those with advanced conventional adenomas at their index colonoscopy was significantly higher than those who were polyp-free (HR=3.9; CI: 1.9-7.7). However, there was not a statistically significant difference in the risk of incident CRC between those who were polyp-free at their index colonoscopy and those who had only non-advanced conventional adenomas (HR=1.5; CI: 0.8-2.6), SSPs with conventional adenomas (HR=2.0; CI: 0.5-8.7), or SSPs without conventional adenomas (HR=1.3; CI: 0.3-5.5). Discussion : Despite recent evidence from cross-sectional studies suggesting that SSPs are high-risk precursors for a subset of colorectal cancers, our results indicate that the risk of CRC in patients with clinically-diagnosed SSPs is similar to the risk of CRC in those with non-advanced adenomas. Additional longitudinal studies of SSPs diagnosed through usual care are needed to inform guidelines for the surveillance of patients with SSPs. This abstract is also being presented as Poster B01. Citation Format: Andrea Burnett-Hartman, Polly A. Newcomb, Chan X. Zeng, Yingye Zheng, John M. Inadomi, Christine Fong, Melissa P. Upton, William M. Grady. Using medical informatics to evaluate the risk of colorectal cancer in patients with clinically diagnosed sessile serrated polyps. [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer: From Initiation to Outcomes; 2016 Sep 17-20; Tampa, FL. Philadelphia (PA): AACR; Cancer Res 2017;77(3 Suppl):Abstract nr PR05.
---
paper_title: Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm

paper_content:

Objective Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem. ::: ::: Materials and methods SCENT employs hierarchical classification rules to identify and extract information from electronic pathology reports. Reports are analyzed and coded using a dictionary of clinical concepts and associated SNOMED codes. To assess the accuracy of SCENT, validation was conducted using manual review of pathology reports from a random sample of 400 breast and 400 prostate cancer patients diagnosed at Kaiser Permanente Southern California. Trained abstractors classified the malignancy status of each report. ::: ::: Results Classifications of SCENT were highly concordant with those of abstractors, achieving κ of 0.96 and 0.95 in the breast and prostate cancer groups, respectively. SCENT identified 51 of 54 new primary and 60 of 61 recurrent cancer cases across both groups, with only three false positives in 792 true benign cases. Measures of sensitivity, specificity, positive predictive value, and negative predictive value exceeded 94% in both cancer groups. ::: ::: Discussion Favorable validation results suggest that SCENT can be used to identify, extract, and code information from pathology report text. Consequently, SCENT has wide applicability in research and clinical care. Further assessment will be needed to validate performance with other clinical text sources, particularly those with greater linguistic variability. ::: ::: Conclusion SCENT is proof of concept for SAS-based natural language processing applications that can be easily shared between institutions and used to support clinical and epidemiologic research.
---
paper_title: Contralateral Breast Cancer Event Detection Using Nature Language Processing.

paper_content:

To facilitate the identification of contralateral breast cancer events for large cohort study, we proposed and implemented a new method based on features extracted from narrative text in progress notes and features from numbers of pathology reports for each side of breast cancer. Our method collects medical concepts and their combinations to detect contralateral events in progress notes. In addition, the numbers of pathology reports generated for either left or right side of breast cancer were derived as additional features. We experimented with support vector machine using the derived features to detect contralateral events. In the cross-validation and held-out tests, the area under curve score is 0.93 and 0.89 respectively. This method can be replicated due to the simplicity of feature generation.
---
paper_title: Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

paper_content:

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.
---
paper_title: Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records.

paper_content:

Purpose ::: Risk stratification underlies system-wide efforts to promote the delivery of appropriate prostate cancer care. Although the elements of risk stratum are available in the electronic medical record, manual data collection is resource intensive. Therefore, we investigated the feasibility and accuracy of an automated data extraction method using natural language processing (NLP) to determine prostate cancer risk stratum. ::: ::: ::: Methods ::: Manually collected clinical stage, biopsy Gleason score, and preoperative prostate-specific antigen (PSA) values from our prospective prostatectomy database were used to categorize patients as low, intermediate, or high risk by D'Amico risk classification. NLP algorithms were developed to automate the extraction of the same data points from the electronic medical record, and risk strata were recalculated. The ability of NLP to identify elements sufficient to calculate risk (recall) was calculated, and the accuracy of NLP was compared with that of manually collected data using the weighted Cohen's κ statistic. ::: ::: ::: Results ::: Of the 2,352 patients with available data who underwent prostatectomy from 2010 to 2014, NLP identified sufficient elements to calculate risk for 1,833 (recall, 78%). NLP had a 91% raw agreement with manual risk stratification (κ = 0.92; 95% CI, 0.90 to 0.93). The κ statistics for PSA, Gleason score, and clinical stage extraction by NLP were 0.86, 0.91, and 0.89, respectively; 91.9% of extracted PSA values were within ± 1.0 ng/mL of the manually collected PSA levels. ::: ::: ::: Conclusion ::: NLP can achieve more than 90% accuracy on D'Amico risk stratification of localized prostate cancer, with adequate recall. This figure is comparable to other NLP tasks and illustrates the known trade off between recall and accuracy. Automating the collection of risk characteristics could be used to power real-time decision support tools and scale up quality measurement in cancer care.
---
paper_title: Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

paper_content:

Abstract Objective: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. Materials and Methods: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and ...
---
paper_title: A natural language processing algorithm to measure quality prostate cancer care.

paper_content:

232Background: Electronic health records (EHRs) are a widely adopted but underutilized source of data for systematic assessment of healthcare quality. Barriers for use of this data source include its vast complexity, lack of structure, and the lack of use of standardized vocabulary and terminology by clinicians. This project aims to develop generalizable algorithms to extract useful knowledge regarding prostate cancer quality metrics from EHRs. Methods: We used EHR ICD-9/10 codes to identify prostate cancer patients receiving care at our academic medical center. Patients were confirmed in the California Cancer Registry (CCR), which provided data on tumor characteristics, treatment data, treatment outcomes and survival. We focused on three potential pretreatment process quality measures, which included documentation within 6 months prior to initial treatment of prostate-specific antigen (PSA), digital rectal exam (DRE) performance, and Gleason score. Each quality metric was defined using target terms and c...
---
paper_title: Automatic Processing of Anatomic Pathology Reports in the Italian Language to Enhance the Reuse of Clinical Data.

paper_content:

Medical reports often contain a lot of relevant information in the form of free text. To reuse these unstructured texts for biomedical research, it is important to extract structured data from them. In this work, we adapted a previously developed information extraction system to the oncology domain, to process a set of anatomic pathology reports in the Italian language. The information extraction system relies on a domain ontology, which was adapted and refined in an iterative way. The final output was evaluated by a domain expert, with promising results.
---
paper_title: Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record

paper_content:

Background. Difficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates. Objective. To use Electronic Health Record (EHR) data to identify patients with prior CRC testing. Design. A clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests. Setting. Large academic medical center. Patients. 200 patients ≥50 years old who had completed ≥2 non-acute primary care visits within a 1-year period. Measures. Recall and precision of the NLP system, billing records, and human chart review were compared to a reference standard of human review of all available information sources. Results. For identification of all CRC t...
---
paper_title: DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

paper_content:

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR .
---
paper_title: Extent of Risk-Aligned Surveillance for Cancer Recurrence Among Patients With Early-Stage Bladder Cancer

paper_content:

Importance Cancer care guidelines recommend aligning surveillance frequency with underlying cancer risk, ie, more frequent surveillance for patients at high vs low risk of cancer recurrence. Objective To assess the extent to which such risk-aligned surveillance is practiced within US Department of Veterans Affairs facilities by classifying surveillance patterns for low- vs high-risk patients with early-stage bladder cancer. Design, Setting, and Participants US national retrospective cohort study of a population-based sample of patients diagnosed with low-risk or high-risk early-stage bladder between January 1, 2005, and December 31, 2011, with follow-up through December 31, 2014. Analyses were performed March 2017 to April 2018. The study included all Veterans Affairs facilities (n = 85) where both low- and high-risk patients were treated. Exposures Low-risk vs high-risk cancer status, based on definitions from the European Association of Urology risk stratification guidelines and on data extracted from diagnostic pathology reports via validated natural language processing algorithms. Main Outcomes and Measures Adjusted cystoscopy frequency for low-risk and high-risk patients for each facility, estimated using multilevel modeling. Results The study included 1278 low-risk and 2115 high-risk patients (median [interquartile range] age, 77 [71-82] years; 99% [3368 of 3393] male). Across facilities, the adjusted frequency of surveillance cystoscopy ranged from 3.7 to 6.2 (mean, 4.8) procedures over 2 years per patient for low-risk patients and from 4.6 to 6.0 (mean, 5.4) procedures over 2 years per patient for high-risk patients. In 70 of 85 facilities, surveillance was performed at a comparable frequency for low- and high-risk patients, differing by less than 1 cystoscopy over 2 years. Surveillance frequency among high-risk patients statistically significantly exceeded surveillance among low-risk patients at only 4 facilities. Across all facilities, surveillance frequencies for low- vs high-risk patients were moderately strongly correlated (r = 0.52;P Conclusions and Relevance Patients with early-stage bladder cancer undergo cystoscopic surveillance at comparable frequencies regardless of risk. This finding highlights the need to understand barriers to risk-aligned surveillance with the goal of making it easier for clinicians to deliver it in routine practice.
---
paper_title: Automatic Extraction of Breast Cancer Information from Clinical Reports

paper_content:

The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features. Its underlying information extraction pipeline employs a rule-based grammar approach that is integrated with semantic technologies to determine the relevant information from the patient record. The accuracy of the system, developed with nine thousand clinical documents, reaches accuracy levels of 90% for lymph node status and 69% for the structurally most complex feature, the hormone status.
---
paper_title: Validation of natural language processing (NLP) for automated ascertainment of EGFR and ALK tests in SEER cases of non-small cell lung cancer (NSCLC).

paper_content:

6528Background: The Surveillance, Epidemiology, and End Results (SEER) registries lack information on the Epidermal Growth Factor Receptor (EGFR) mutation and Anaplastic Lymphoma Kinase (ALK) gene ...
---
paper_title: Development and Validation of an Automated Method to Identify Patients Undergoing Radical Cystectomy for Bladder Cancer Using Natural Language Processing

paper_content:

Abstract Introduction Measurement for quality improvement relies on accurate case identification and characterization. With electronic health records now widely deployed, natural language processing, the use of software to transform text into structured data, may enrich quality measurement. Accordingly we evaluated the application of natural language processing to radical cystectomy procedures for patients with bladder cancer. Methods From a sample of 497 procedures performed from March 2013 to October 2014 we identified radical cystectomy for primary bladder cancer using the approaches of 1) a natural language processing enhanced algorithm, 2) an administrative claims based algorithm and 3) manual chart review. We also characterized treatment with robotic surgery and continent urinary diversion. Using chart review as the reference standard we calculated the observed agreement (kappa statistic), sensitivity, specificity, positive predictive value and negative predictive value for natural language processing and administrative claims. Results We confirmed 84 radical cystectomies were performed for bladder cancer, with 50.0% robotic and 38.6% continent diversions. The natural language processing enhanced and claims based algorithms demonstrated 99.8% (κ=0.993, 95% CI 0.979–1.000) and 98.6% (κ=0.951, 95% CI 0.915–0.987) agreement with manual review, respectively. Both approaches accurately characterized robotic vs open surgery, with natural language processing enhanced algorithms showing 98.8% (κ=0.976, 95% CI 0.930–1.000) and claims based 90.5% (κ=0.810, 95% CI 0.686–0.933) agreement. For urinary diversion natural language processing enhanced algorithms correctly specified 96.4% of cases (κ=0.924, 95% CI 0.839–1.000) compared with 83.3% (κ=0.655, 95% CI 0.491–0.819). Conclusions Natural language processing enhanced and claims based algorithms accurately identified radical cystectomy cases at our institution. However, natural language processing appears to better classify specific aspects of cystectomy surgery, highlighting a potential advantage of this emerging methodology.
---
paper_title: Automated Extraction of Grade, Stage, and Quality Information From Transurethral Resection of Bladder Tumor Pathology Reports Using Natural Language Processing

paper_content:

PurposeBladder cancer is initially diagnosed and staged with a transurethral resection of bladder tumor (TURBT). Patient survival is dependent on appropriate sampling of layers of the bladder, but pathology reports are dictated as free text, making large-scale data extraction for quality improvement challenging. We sought to automate extraction of stage, grade, and quality information from TURBT pathology reports using natural language processing (NLP).MethodsPatients undergoing TURBT were retrospectively identified using the Northwestern Enterprise Data Warehouse. An NLP algorithm was then created to extract information from free-text pathology reports and was iteratively improved using a training set of manually reviewed TURBTs. NLP accuracy was then validated using another set of manually reviewed TURBTs, and reliability was calculated using Cohen’s κ.ResultsOf 3,042 TURBTs identified from 2006 to 2016, 39% were classified as benign, 35% as Ta, 11% as T1, 4% as T2, and 10% as isolated carcinoma in situ...
---
paper_title: Assessing the natural language processing capabilities of IBM Watson for oncology using real Australian lung cancer cases.

paper_content:

e18229Background: Optimal treatment decisions require information from multiple sources and formats even in the context of electronic medical records. In addition, structured data such as that coll...
---
paper_title: Extracting timing and status descriptors for colonoscopy testing from electronic medical records

paper_content:

Colorectal cancer (CRC) screening rates are low despite confirmed benefits. The authors investigated the use of natural language processing (NLP) to identify previous colonoscopy screening in electronic records from a random sample of 200 patients at least 50 years old. The authors developed algorithms to recognize temporal expressions and ‘status indicators’, such as ‘patient refused’, or ‘test scheduled’. The new methods were added to the existing KnowledgeMap concept identifier system, and the resulting system was used to parse electronic medical records (EMR) to detect completed colonoscopies. Using as the ‘gold standard’ expert physicians’ manual review of EMR notes, the system identified timing references with a recall of 0.91 and precision of 0.95, colonoscopy status indicators with a recall of 0.82 and precision of 0.95, and references to actually completed colonoscopies with recall of 0.93 and precision of 0.95. The system was superior to using colonoscopy billing codes alone. Health services researchers and clinicians may find NLP a useful adjunct to traditional methods to detect CRC screening status. Further investigations must validate extension of NLP approaches for other types of CRC screening applications.
---
paper_title: Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study

paper_content:

Background: In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correlate the treatment regimen with the prognosis to develop evidence-based radiation therapy paradigms. These data are available mainly in forms of narrative texts or table formats with heterogeneous vocabularies. Manual extraction of the related information from these data can be time consuming and labor intensive, which is not ideal for large studies. Objective: The objective of this study was to adapt the interactive information extraction platform Information and Data Extraction using Adaptive Learning (IDEAL-X) to extract treatment and prognosis data for patients with locally advanced or inoperable non–small cell lung cancer (NSCLC). Methods: We transformed patient treatment and prognosis documents into normalized structured forms using the IDEAL-X system for easy data navigation. The adaptive learning and user-customized controlled toxicity vocabularies were applied to extract categorized treatment and prognosis data, so as to generate structured output. Results: In total, we extracted data from 261 treatment and prognosis documents relating to 50 patients, with overall precision and recall more than 93% and 83%, respectively. For toxicity information extractions, which are important to study patient posttreatment side effects and quality of life, the precision and recall achieved 95.7% and 94.5% respectively. Conclusions: The IDEAL-X system is capable of extracting study data regarding NSCLC chemoradiation patients with significant accuracy and effectiveness, and therefore can be used in large-scale radiotherapy clinical data studies. [JMIR Med Inform 2018;6(1):e8]
---
paper_title: Using Natural Language Processing to Extract Abnormal Results From Cancer Screening Reports

paper_content:

OBJECTIVES ::: Numerous studies show that follow-up of abnormal cancer screening results, such as mammography and Papanicolaou (Pap) smears, is frequently not performed in a timely manner. A contributing factor is that abnormal results may go unrecognized because they are buried in free-text documents in electronic medical records (EMRs), and, as a result, patients are lost to follow-up. By identifying abnormal results from free-text reports in EMRs and generating alerts to clinicians, natural language processing (NLP) technology has the potential for improving patient care. The goal of the current study was to evaluate the performance of NLP software for extracting abnormal results from free-text mammography and Pap smear reports stored in an EMR. ::: ::: ::: METHODS ::: A sample of 421 and 500 free-text mammography and Pap reports, respectively, were manually reviewed by a physician, and the results were categorized for each report. We tested the performance of NLP to extract results from the reports. The 2 assessments (criterion standard versus NLP) were compared to determine the precision, recall, and accuracy of NLP. ::: ::: ::: RESULTS ::: When NLP was compared with manual review for mammography reports, the results were as follows: precision, 98% (96%-99%); recall, 100% (98%-100%); and accuracy, 98% (96%-99%). For Pap smear reports, the precision, recall, and accuracy of NLP were all 100%. ::: ::: ::: CONCLUSIONS ::: Our study developed NLP models that accurately extract abnormal results from mammography and Pap smear reports. Plans include using NLP technology to generate real-time alerts and reminders for providers to facilitate timely follow-up of abnormal results.
---
paper_title: Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

paper_content:

Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute’s Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating centers to report structured data across the cancer screening continuum. We evaluate the accuracy of automated information extraction of imaging findings from radiology reports, which are available as unstructured text. We present prevalence estimates of imaging findings for breast imaging received by women who obtained care in a primary care network participating in PROSPR (n = 139,953 radiology reports) and compared automatically extracted data elements to a “gold standard” based on manual review for a validation sample of 941 randomly selected radiology reports, including mammograms, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI). The prevalence of imaging findings vary by data element and modality (e.g., suspicious calcification noted in 2.6 % of screening mammograms, 12.1 % of diagnostic mammograms, and 9.4 % of tomosynthesis exams). In the validation sample, the accuracy of identifying imaging findings, including suspicious calcifications, masses, and architectural distortion (on mammogram and tomosynthesis); masses, cysts, non-mass enhancement, and enhancing foci (on MRI); and masses and cysts (on ultrasound), range from 0.8 to1.0 for recall, precision, and F-measure. Information extraction tools can be used for accurate documentation of imaging findings as structured data elements from text reports for a variety of breast imaging modalities. These data can be used to populate screening registries to help elucidate more effective breast cancer screening processes.
---
paper_title: Validation of natural language processing to extract breast cancer pathology procedures and results

paper_content:

Background: Pathology reports typically require manual review to abstract research data. We developed a natural language processing (NLP) system to automatically interpret free-text breast pathology reports with limited assistance from manual abstraction. Methods: We used an iterative approach of machine learning algorithms and constructed groups of related findings to identify breast-related procedures and results from free-text pathology reports. We evaluated the NLP system using an all-or-nothing approach to determine which reports could be processed entirely using NLP and which reports needed manual review beyond NLP. We divided 3234 reports for development (2910, 90%), and evaluation (324, 10%) purposes using manually reviewed pathology data as our gold standard. Results: NLP correctly coded 12.7% of the evaluation set, flagged 49.1% of reports for manual review, incorrectly coded 30.8%, and correctly omitted 7.4% from the evaluation set due to irrelevancy (i.e. not breast-related). Common procedures and results were identified correctly (e.g. invasive ductal with 95.5% precision and 94.0% sensitivity), but entire reports were flagged for manual review because of rare findings and substantial variation in pathology report text. Conclusions: The NLP system we developed did not perform sufficiently for abstracting entire breast pathology reports. The all-or-nothing approach resulted in too broad of a scope of work and limited our flexibility to identify breast pathology procedures and results. Our NLP system was also limited by the lack of the gold standard data on rare findings and wide variation in pathology text. Focusing on individual, common elements and improving pathology text report standardization may improve performance.
---
paper_title: Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

paper_content:

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.
---
paper_title: Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

paper_content:

Abstract Objective: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. Materials and Methods: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and ...
---
paper_title: Automatic Processing of Anatomic Pathology Reports in the Italian Language to Enhance the Reuse of Clinical Data.

paper_content:

Medical reports often contain a lot of relevant information in the form of free text. To reuse these unstructured texts for biomedical research, it is important to extract structured data from them. In this work, we adapted a previously developed information extraction system to the oncology domain, to process a set of anatomic pathology reports in the Italian language. The information extraction system relies on a domain ontology, which was adapted and refined in an iterative way. The final output was evaluated by a domain expert, with promising results.
---
paper_title: Using machine learning to parse breast pathology reports

paper_content:

PURPOSE ::: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. ::: ::: ::: METHODS ::: We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women's Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. ::: ::: ::: RESULTS ::: The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. ::: ::: ::: CONCLUSIONS ::: Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.
---
paper_title: Facilitating cancer research using natural language processing of pathology reports.

paper_content:

Many ongoing clinical research projects, such as projects involving studies associated with cancer, involve manual capture of information in surgical pathology reports so that the information can be used to determine the eligibility of recruited patients for the study and to provide other information, such as cancer prognosis. Natural language processing (NLP) systems offer an alternative to automated coding, but pathology reports have certain features that are difficult for NLP systems. This paper describes how a preprocessor was integrated with an existing NLP system (MedLEE) in order to reduce modification to the NLP system and to improve performance. The work was done in conjunction with an ongoing clinical research project that assesses disparities and risks of developing breast cancer for minority women. An evaluation of the system was performed using manually coded data from the research project's database as a gold standard. The evaluation outcome showed that the extended NLP system had a sensitivity of 90.6% and a precision of 91.6%. Results indicated that this system performed satisfactorily for capturing information for the cancer research project.
---
paper_title: DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

paper_content:

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR .
---
paper_title: Extent of Risk-Aligned Surveillance for Cancer Recurrence Among Patients With Early-Stage Bladder Cancer

paper_content:

Importance Cancer care guidelines recommend aligning surveillance frequency with underlying cancer risk, ie, more frequent surveillance for patients at high vs low risk of cancer recurrence. Objective To assess the extent to which such risk-aligned surveillance is practiced within US Department of Veterans Affairs facilities by classifying surveillance patterns for low- vs high-risk patients with early-stage bladder cancer. Design, Setting, and Participants US national retrospective cohort study of a population-based sample of patients diagnosed with low-risk or high-risk early-stage bladder between January 1, 2005, and December 31, 2011, with follow-up through December 31, 2014. Analyses were performed March 2017 to April 2018. The study included all Veterans Affairs facilities (n = 85) where both low- and high-risk patients were treated. Exposures Low-risk vs high-risk cancer status, based on definitions from the European Association of Urology risk stratification guidelines and on data extracted from diagnostic pathology reports via validated natural language processing algorithms. Main Outcomes and Measures Adjusted cystoscopy frequency for low-risk and high-risk patients for each facility, estimated using multilevel modeling. Results The study included 1278 low-risk and 2115 high-risk patients (median [interquartile range] age, 77 [71-82] years; 99% [3368 of 3393] male). Across facilities, the adjusted frequency of surveillance cystoscopy ranged from 3.7 to 6.2 (mean, 4.8) procedures over 2 years per patient for low-risk patients and from 4.6 to 6.0 (mean, 5.4) procedures over 2 years per patient for high-risk patients. In 70 of 85 facilities, surveillance was performed at a comparable frequency for low- and high-risk patients, differing by less than 1 cystoscopy over 2 years. Surveillance frequency among high-risk patients statistically significantly exceeded surveillance among low-risk patients at only 4 facilities. Across all facilities, surveillance frequencies for low- vs high-risk patients were moderately strongly correlated (r = 0.52;P Conclusions and Relevance Patients with early-stage bladder cancer undergo cystoscopic surveillance at comparable frequencies regardless of risk. This finding highlights the need to understand barriers to risk-aligned surveillance with the goal of making it easier for clinicians to deliver it in routine practice.
---
paper_title: Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

paper_content:

The paper assesses the utility of Medtex on automating Cancer Registry notifications from narrative histology and cytology reports from the Queensland state-wide pathology information system. A corpus of 45.3 million pathology HL7 messages (including 119,581 histology and cytology reports) from a Queensland pathology repository for the year of 2009 was analysed by Medtex for cancer notification. Reports analysed by Medtex were consolidated at a patient level and compared against patients with notifiable cancers from the Queensland Oncology Repository (QOR). A stratified random sample of 1,000 patients was manually reviewed by a cancer clinical coder to analyse agreements and discrepancies. Sensitivity of 96.5% (95% confidence interval: 94.5-97.8%), specificity of 96.5% (95.3-97.4%) and positive predictive value of 83.7% (79.6-86.8%) were achieved for identifying cancer notifiable patients. Medtex achieved high sensitivity and specificity across the breadth of cancers, report types, pathology laboratories and pathologists throughout the State of Queensland. The high sensitivity also resulted in the identification of cancer patients that were not found in the QOR. High sensitivity was at the expense of positive predictive value; however, these cases may be considered as lower priority to Cancer Registries as they can be quickly reviewed. Error analysis revealed that system errors tended to be tumour stream dependent. Medtex is proving to be a promising medical text analytic system. High value cancer information can be generated through intelligent data classification and extraction on large volumes of unstructured pathology reports.
---
paper_title: Machine learning to parse breast pathology reports in Chinese

paper_content:

Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics community to support automating this work via machine learning in English. Our contribution is to provide an automated approach to construct such structured databases in Chinese, and to set the stage for extraction from other languages. We collected 2104 de-identified Chinese benign and malignant breast pathology reports from Hunan Cancer Hospital. Physicians with native Chinese proficiency reviewed the reports and annotated a variety of binary and numerical pathologic entities. After excluding 78 cases with a bilateral lesion in the same report, 1216 cases were used as a training set for the algorithm, which was then refined by 405 development cases. The Natural language processing algorithm was tested by using the remaining 405 cases to evaluate the machine learning outcome. The model was used to extract 13 binary entities and 8 numerical entities. When compared to physicians with native Chinese proficiency, the model showed a per-entity accuracy from 91 to 100% for all common diagnoses on the test set. The overall accuracy of binary entities was 98% and of numerical entities was 95%. In a per-report evaluation for binary entities with more than 100 training cases, 85% of all the testing reports were completely correct and 11% had an error in 1 out of 22 entities. We have demonstrated that Chinese breast pathology reports can be automatically parsed into structured data using standard machine learning approaches. The results of our study demonstrate that techniques effective in parsing English reports can be scaled to other languages.
---
paper_title: Automated extraction and normalization of findings from cancer-related free-text radiology reports.

paper_content:

We describe the performance of a particular natural language processing system that uses knowledge vectors to extract findings from radiology reports. LifeCode® (A-Life Medical, Inc.) has been successfully coding reports for billing purposes for several years. In this study, we describe the use of LifeCode® to code all findings within a set of 500 cancer-related radiology reports against a test set in which all findings were manually tagged. The system was trained with 1400 reports prior to running the test set. Results: LifeCode® had a recall of 84.5% and precision of 95.7% in the coding of cancer-related radiology report findings. Conclusion: Despite the use of a modest sized training set and minimal training iterations, when applied to cancer-related reports the system achieved recall and precision measures comparable to other reputable natural language processors in this domain.
---
paper_title: Abstract P5-08-20: A real-world evidence study to define the prevalence of endocrine therapy-naïve hormone receptor-positive locally advanced or metastatic breast cancer in the US

paper_content:

BACKGROUND The recently completed FALCON trial (NCT01602380) compared the efficacy of the selective estrogen receptor degrader (SERD) fulvestrant 500 mg with anastrozole in postmenopausal women with hormone receptor (HR)-positive locally advanced or metastatic breast cancer (LA/MBC) who had received no prior endocrine therapy (ET). To better understand the size of the US population to which the results of the FALCON trial are applicable, this study estimated the proportion of postmenopausal patients with HR-positive, human epidermal growth factor receptor (HER)2-negative LA/MBC who had received no prior ET, using data from a US medical record database. METHODS This observational study retrospectively analyzed data from the Optum Electronic Health Record Database, obtained from provider groups across the US. Women over 40 years of age with breast cancer diagnoses (January 2008–March 2015) were included, provided they had at least 12 months of recorded medical history prior to index, and at least one recorded physician office visit in that time. Incident (newly diagnosed) and prevalent (previous diagnosis of early or advanced breast cancer) cases were identified. Free-text clinical notes were reviewed using natural language processing to identify a target patient population of postmenopausal women with HR-positive, HER2-negative LA/MBC who had received no prior ET (similar to the FALCON entry criteria); additionally, diagnostic codes or treatment history were also used to identify HR status in the absence of confirmation from the free-text notes. The results of this analysis were extrapolated to estimate the size of the target population at a national level, using statistics from the National Cancer Institute9s Surveillance, Epidemiology and End Results (SEER) database. Results are presented descriptively. RESULTS Overall, 63,962 women with breast cancer were identified, of whom 11,831 had discernible information on menopausal status, HER2 status, HR status, and disease stage. Of these, 1,923 patients were identified with postmenopausal, HR-positive, HER2-negative (or unknown) LA/MBC. Within this population of patients, 70.7% (1,360/1,923) had not previously received ET (88.5% [920/1,040] incident cases; 49.8% [440/883] prevalent cases), representing 11.5% (1,360/11,831) of the total breast cancer population with known menopausal status, HER2 status, HR status, and cancer disease stage information. The proportion of patients with postmenopausal, HR-positive, HER2-negative LA/MBC who had received no prior ET in this sample was extrapolated using US national estimates of the size of the postmenopausal, HR-positive, HER2-negative LA/MBC population taken from SEER. This approach suggests a 5-year limited-duration prevalence of postmenopausal patients with HR-positive, HER2-negative LA/MBC who have received no prior ET of approximately 50,000 cases and an annual incidence of about 15,000 patients. CONCLUSIONS These real-world data provide an estimate of the number of postmenopausal patients with HR-positive, HER2-negative, LA/MBC in the US who have not previously received ET. This population corresponds to the recently completed Phase 3 FALCON trial of fulvestrant compared with anastrozole. Citation Format: Nunes AP, Green E, Dalvi T, Lewis J, Jones N, Seeger JD. A real-world evidence study to define the prevalence of endocrine therapy-naive hormone receptor-positive locally advanced or metastatic breast cancer in the US [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-08-20.
---
paper_title: Assessing the natural language processing capabilities of IBM Watson for oncology using real Australian lung cancer cases.

paper_content:

e18229Background: Optimal treatment decisions require information from multiple sources and formats even in the context of electronic medical records. In addition, structured data such as that coll...
---
paper_title: caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research.

paper_content:

The authors report on the development of the Cancer Tissue Information Extraction System (caTIES)dan application that supports collaborative tissue banking and text mining by leveraging existing natural language processing methods and algorithms, grid communication and security frameworks, and query visualization methods. The system fills an important need for textderived clinical data in translational research such as tissue-banking and clinical trials. The design of caTIES addresses three critical issues for informatics support of translational research: (1) federation of research data sources derived from clinical systems; (2) expressive graphical interfaces for concept-based text mining; and (3) regulatory and security model for supporting multicenter collaborative research. Implementation of the system at several Cancer Centers across the country is creating a potential network of caTIES repositories that could provide millions of de-identified clinical reports to users. The system provides an end-to-end application of medical natural language processing to support multiinstitutional translational research programs.
---
paper_title: Automated extraction of Biomarker information from pathology reports

paper_content:

Pathology reports are written in free-text form, which precludes efficient data gathering. We aimed to overcome this limitation and design an automated system for extracting biomarker profiles from accumulated pathology reports. We designed a new data model for representing biomarker knowledge. The automated system parses immunohistochemistry reports based on a “slide paragraph” unit defined as a set of immunohistochemistry findings obtained for the same tissue slide. Pathology reports are parsed using context-free grammar for immunohistochemistry, and using a tree-like structure for surgical pathology. The performance of the approach was validated on manually annotated pathology reports of 100 randomly selected patients managed at Seoul National University Hospital. High F-scores were obtained for parsing biomarker name and corresponding test results (0.999 and 0.998, respectively) from the immunohistochemistry reports, compared to relatively poor performance for parsing surgical pathology findings. However, applying the proposed approach to our single-center dataset revealed information on 221 unique biomarkers, which represents a richer result than biomarker profiles obtained based on the published literature. Owing to the data representation model, the proposed approach can associate biomarker profiles extracted from an immunohistochemistry report with corresponding pathology findings listed in one or more surgical pathology reports. Term variations are resolved by normalization to corresponding preferred terms determined by expanded dictionary look-up and text similarity-based search. Our proposed approach for biomarker data extraction addresses key limitations regarding data representation and can handle reports prepared in the clinical setting, which often contain incomplete sentences, typographical errors, and inconsistent formatting.
---
paper_title: Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm

paper_content:

Objective Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem. ::: ::: Materials and methods SCENT employs hierarchical classification rules to identify and extract information from electronic pathology reports. Reports are analyzed and coded using a dictionary of clinical concepts and associated SNOMED codes. To assess the accuracy of SCENT, validation was conducted using manual review of pathology reports from a random sample of 400 breast and 400 prostate cancer patients diagnosed at Kaiser Permanente Southern California. Trained abstractors classified the malignancy status of each report. ::: ::: Results Classifications of SCENT were highly concordant with those of abstractors, achieving κ of 0.96 and 0.95 in the breast and prostate cancer groups, respectively. SCENT identified 51 of 54 new primary and 60 of 61 recurrent cancer cases across both groups, with only three false positives in 792 true benign cases. Measures of sensitivity, specificity, positive predictive value, and negative predictive value exceeded 94% in both cancer groups. ::: ::: Discussion Favorable validation results suggest that SCENT can be used to identify, extract, and code information from pathology report text. Consequently, SCENT has wide applicability in research and clinical care. Further assessment will be needed to validate performance with other clinical text sources, particularly those with greater linguistic variability. ::: ::: Conclusion SCENT is proof of concept for SAS-based natural language processing applications that can be easily shared between institutions and used to support clinical and epidemiologic research.
---
paper_title: Validation of natural language processing to extract breast cancer pathology procedures and results

paper_content:

Background: Pathology reports typically require manual review to abstract research data. We developed a natural language processing (NLP) system to automatically interpret free-text breast pathology reports with limited assistance from manual abstraction. Methods: We used an iterative approach of machine learning algorithms and constructed groups of related findings to identify breast-related procedures and results from free-text pathology reports. We evaluated the NLP system using an all-or-nothing approach to determine which reports could be processed entirely using NLP and which reports needed manual review beyond NLP. We divided 3234 reports for development (2910, 90%), and evaluation (324, 10%) purposes using manually reviewed pathology data as our gold standard. Results: NLP correctly coded 12.7% of the evaluation set, flagged 49.1% of reports for manual review, incorrectly coded 30.8%, and correctly omitted 7.4% from the evaluation set due to irrelevancy (i.e. not breast-related). Common procedures and results were identified correctly (e.g. invasive ductal with 95.5% precision and 94.0% sensitivity), but entire reports were flagged for manual review because of rare findings and substantial variation in pathology report text. Conclusions: The NLP system we developed did not perform sufficiently for abstracting entire breast pathology reports. The all-or-nothing approach resulted in too broad of a scope of work and limited our flexibility to identify breast pathology procedures and results. Our NLP system was also limited by the lack of the gold standard data on rare findings and wide variation in pathology text. Focusing on individual, common elements and improving pathology text report standardization may improve performance.
---
paper_title: Automatic Processing of Anatomic Pathology Reports in the Italian Language to Enhance the Reuse of Clinical Data.

paper_content:

Medical reports often contain a lot of relevant information in the form of free text. To reuse these unstructured texts for biomedical research, it is important to extract structured data from them. In this work, we adapted a previously developed information extraction system to the oncology domain, to process a set of anatomic pathology reports in the Italian language. The information extraction system relies on a domain ontology, which was adapted and refined in an iterative way. The final output was evaluated by a domain expert, with promising results.
---
paper_title: Using machine learning to parse breast pathology reports

paper_content:

PURPOSE ::: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. ::: ::: ::: METHODS ::: We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women's Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. ::: ::: ::: RESULTS ::: The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. ::: ::: ::: CONCLUSIONS ::: Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.
---
paper_title: Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution

paper_content:

Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health record(EHR)s are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource-intense and not readily scalable. Informatics-based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer(BC) receptor status phenotypes from structured and unstructured EHR data using rule-based algorithms, including natural language processing(NLP). Overall, use of NLP increased BC receptor status coverage by 39.2% from 69.1% with structured medication information alone. Using all available EHR data, estrogen receptor-positive BC cases were ascertained with high precision(P = 0.976) and recall(R = 0.987) compared to gold standard chart-reviewed patients. However, status negation(R = 0.591), decreased 40.2% when relying on structured medications alone. Using multiple EHR data types (and thorough understanding perspectives offered) are necessary to derive robust EHR-based precision medicine phenotypes. ::: ::: This article is protected by copyright. All rights reserved
---
paper_title: Pathologic findings in reduction mammoplasty procedures identified by natural language processing of breast pathology reports: A surrogate for the population incidence of cancer and high risk lesions.

paper_content:

e13569Background: Breast reduction surgery removes a random sample of breast tissue in otherwise asymptomatic women and thus provides a method to evaluate the background incidence of breast patholo...
---
paper_title: Development of a Natural Language Processing Engine to Generate Bladder Cancer Pathology Data for Health Services Research

paper_content:

OBJECTIVE ::: To take the first step toward assembling population-based cohorts of patients with bladder cancer with longitudinal pathology data, we developed and validated a natural language processing (NLP) engine that abstracts pathology data from full-text pathology reports. ::: ::: ::: METHODS ::: Using 600 bladder pathology reports randomly selected from the Department of Veterans Affairs, we developed and validated an NLP engine to abstract data on histology, invasion (presence vs absence and depth), grade, the presence of muscularis propria, and the presence of carcinoma in situ. Our gold standard was based on an independent review of reports by 2 urologists, followed by adjudication. We assessed the NLP performance by calculating the accuracy, the positive predictive value, and the sensitivity. We subsequently applied the NLP engine to pathology reports from 10,725 patients with bladder cancer. ::: ::: ::: RESULTS ::: When comparing the NLP output to the gold standard, NLP achieved the highest accuracy (0.98) for the presence vs the absence of carcinoma in situ. Accuracy for histology, invasion (presence vs absence), grade, and the presence of muscularis propria ranged from 0.83 to 0.96. The most challenging variable was depth of invasion (accuracy 0.68), with an acceptable positive predictive value for lamina propria (0.82) and for muscularis propria (0.87) invasion. The validated engine was capable of abstracting pathologic characteristics for 99% of the patients with bladder cancer. ::: ::: ::: CONCLUSION ::: NLP had high accuracy for 5 of 6 variables and abstracted data for the vast majority of the patients. This now allows for the assembly of population-based cohorts with longitudinal pathology data.
---
paper_title: Facilitating cancer research using natural language processing of pathology reports.

paper_content:

Many ongoing clinical research projects, such as projects involving studies associated with cancer, involve manual capture of information in surgical pathology reports so that the information can be used to determine the eligibility of recruited patients for the study and to provide other information, such as cancer prognosis. Natural language processing (NLP) systems offer an alternative to automated coding, but pathology reports have certain features that are difficult for NLP systems. This paper describes how a preprocessor was integrated with an existing NLP system (MedLEE) in order to reduce modification to the NLP system and to improve performance. The work was done in conjunction with an ongoing clinical research project that assesses disparities and risks of developing breast cancer for minority women. An evaluation of the system was performed using manually coded data from the research project's database as a gold standard. The evaluation outcome showed that the extended NLP system had a sensitivity of 90.6% and a precision of 91.6%. Results indicated that this system performed satisfactorily for capturing information for the cancer research project.
---
paper_title: Deep learning analytics for diagnostic support of breast cancer disease management

paper_content:

Breast cancer continues to be one of the leading causes of cancer death among women. Mammogram is the standard of care for screening and diagnosis of breast cancer. The American College of Radiology developed the Breast Imaging Reporting and Data System (BI-RADS) lexicon to standardize mammographic reporting to assess cancer risk and facilitate biopsy decision-making. However, because substantial inter-observer variability remains in the application of the BI-RADS lexicon, including inappropriate term usage and missing data, current biopsy decision-making accuracy using the unstructured free text or semi-structured reports varies greatly. Hence, incorporating novel and accurate technique into breast cancer decision-making data is critical. Here, we combined natural language processing and deep learning methods to develop an analytic model that targets well-characterized and defined specific breast suspicious patient subgroups rather than a broad heterogeneous group for diagnostic support of breast cancer management.
---
paper_title: DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

paper_content:

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR .
---
paper_title: Extent of Risk-Aligned Surveillance for Cancer Recurrence Among Patients With Early-Stage Bladder Cancer

paper_content:

Importance Cancer care guidelines recommend aligning surveillance frequency with underlying cancer risk, ie, more frequent surveillance for patients at high vs low risk of cancer recurrence. Objective To assess the extent to which such risk-aligned surveillance is practiced within US Department of Veterans Affairs facilities by classifying surveillance patterns for low- vs high-risk patients with early-stage bladder cancer. Design, Setting, and Participants US national retrospective cohort study of a population-based sample of patients diagnosed with low-risk or high-risk early-stage bladder between January 1, 2005, and December 31, 2011, with follow-up through December 31, 2014. Analyses were performed March 2017 to April 2018. The study included all Veterans Affairs facilities (n = 85) where both low- and high-risk patients were treated. Exposures Low-risk vs high-risk cancer status, based on definitions from the European Association of Urology risk stratification guidelines and on data extracted from diagnostic pathology reports via validated natural language processing algorithms. Main Outcomes and Measures Adjusted cystoscopy frequency for low-risk and high-risk patients for each facility, estimated using multilevel modeling. Results The study included 1278 low-risk and 2115 high-risk patients (median [interquartile range] age, 77 [71-82] years; 99% [3368 of 3393] male). Across facilities, the adjusted frequency of surveillance cystoscopy ranged from 3.7 to 6.2 (mean, 4.8) procedures over 2 years per patient for low-risk patients and from 4.6 to 6.0 (mean, 5.4) procedures over 2 years per patient for high-risk patients. In 70 of 85 facilities, surveillance was performed at a comparable frequency for low- and high-risk patients, differing by less than 1 cystoscopy over 2 years. Surveillance frequency among high-risk patients statistically significantly exceeded surveillance among low-risk patients at only 4 facilities. Across all facilities, surveillance frequencies for low- vs high-risk patients were moderately strongly correlated (r = 0.52;P Conclusions and Relevance Patients with early-stage bladder cancer undergo cystoscopic surveillance at comparable frequencies regardless of risk. This finding highlights the need to understand barriers to risk-aligned surveillance with the goal of making it easier for clinicians to deliver it in routine practice.
---
paper_title: Machine learning to parse breast pathology reports in Chinese

paper_content:

Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics community to support automating this work via machine learning in English. Our contribution is to provide an automated approach to construct such structured databases in Chinese, and to set the stage for extraction from other languages. We collected 2104 de-identified Chinese benign and malignant breast pathology reports from Hunan Cancer Hospital. Physicians with native Chinese proficiency reviewed the reports and annotated a variety of binary and numerical pathologic entities. After excluding 78 cases with a bilateral lesion in the same report, 1216 cases were used as a training set for the algorithm, which was then refined by 405 development cases. The Natural language processing algorithm was tested by using the remaining 405 cases to evaluate the machine learning outcome. The model was used to extract 13 binary entities and 8 numerical entities. When compared to physicians with native Chinese proficiency, the model showed a per-entity accuracy from 91 to 100% for all common diagnoses on the test set. The overall accuracy of binary entities was 98% and of numerical entities was 95%. In a per-report evaluation for binary entities with more than 100 training cases, 85% of all the testing reports were completely correct and 11% had an error in 1 out of 22 entities. We have demonstrated that Chinese breast pathology reports can be automatically parsed into structured data using standard machine learning approaches. The results of our study demonstrate that techniques effective in parsing English reports can be scaled to other languages.
---
paper_title: Pathologic findings in reduction mammoplasty specimens: a surrogate for the population prevalence of breast cancer and high-risk lesions

paper_content:

Mammoplasty removes random samples of breast tissue from asymptomatic women providing a unique method for evaluating background prevalence of breast pathology in normal population. Our goal was to identify the rate of atypical breast lesions and cancers in women of various ages in the largest mammoplasty cohort reported to date. We analyzed pathologic reports from patients undergoing bilateral mammoplasty, using natural language processing algorithm, verified by human review. Patients with a prior history of breast cancer or atypia were excluded. A total of 4775 patients were deemed eligible. Median age was 40 (range 13–86) and was higher in patients with any incidental finding compared to patients with normal reports (52 vs. 39 years, p = 0.0001). Pathological findings were detected in 7.06% (337) of procedures. Benign high-risk lesions were found in 299 patients (6.26%). Invasive carcinoma and ductal carcinoma in situ were detected in 15 (0.31%) and 23 (0.48%) patients, respectively. The rate of atypias and cancers increased with age. The overall rate of abnormal findings in asymptomatic patients undergoing mammoplasty was 7.06%, increasing with age. As these results are based on random sample of breast tissue, they likely underestimate the prevalence of abnormal findings in asymptomatic women.
---
paper_title: Abstract P5-08-20: A real-world evidence study to define the prevalence of endocrine therapy-naïve hormone receptor-positive locally advanced or metastatic breast cancer in the US

paper_content:

BACKGROUND The recently completed FALCON trial (NCT01602380) compared the efficacy of the selective estrogen receptor degrader (SERD) fulvestrant 500 mg with anastrozole in postmenopausal women with hormone receptor (HR)-positive locally advanced or metastatic breast cancer (LA/MBC) who had received no prior endocrine therapy (ET). To better understand the size of the US population to which the results of the FALCON trial are applicable, this study estimated the proportion of postmenopausal patients with HR-positive, human epidermal growth factor receptor (HER)2-negative LA/MBC who had received no prior ET, using data from a US medical record database. METHODS This observational study retrospectively analyzed data from the Optum Electronic Health Record Database, obtained from provider groups across the US. Women over 40 years of age with breast cancer diagnoses (January 2008–March 2015) were included, provided they had at least 12 months of recorded medical history prior to index, and at least one recorded physician office visit in that time. Incident (newly diagnosed) and prevalent (previous diagnosis of early or advanced breast cancer) cases were identified. Free-text clinical notes were reviewed using natural language processing to identify a target patient population of postmenopausal women with HR-positive, HER2-negative LA/MBC who had received no prior ET (similar to the FALCON entry criteria); additionally, diagnostic codes or treatment history were also used to identify HR status in the absence of confirmation from the free-text notes. The results of this analysis were extrapolated to estimate the size of the target population at a national level, using statistics from the National Cancer Institute9s Surveillance, Epidemiology and End Results (SEER) database. Results are presented descriptively. RESULTS Overall, 63,962 women with breast cancer were identified, of whom 11,831 had discernible information on menopausal status, HER2 status, HR status, and disease stage. Of these, 1,923 patients were identified with postmenopausal, HR-positive, HER2-negative (or unknown) LA/MBC. Within this population of patients, 70.7% (1,360/1,923) had not previously received ET (88.5% [920/1,040] incident cases; 49.8% [440/883] prevalent cases), representing 11.5% (1,360/11,831) of the total breast cancer population with known menopausal status, HER2 status, HR status, and cancer disease stage information. The proportion of patients with postmenopausal, HR-positive, HER2-negative LA/MBC who had received no prior ET in this sample was extrapolated using US national estimates of the size of the postmenopausal, HR-positive, HER2-negative LA/MBC population taken from SEER. This approach suggests a 5-year limited-duration prevalence of postmenopausal patients with HR-positive, HER2-negative LA/MBC who have received no prior ET of approximately 50,000 cases and an annual incidence of about 15,000 patients. CONCLUSIONS These real-world data provide an estimate of the number of postmenopausal patients with HR-positive, HER2-negative, LA/MBC in the US who have not previously received ET. This population corresponds to the recently completed Phase 3 FALCON trial of fulvestrant compared with anastrozole. Citation Format: Nunes AP, Green E, Dalvi T, Lewis J, Jones N, Seeger JD. A real-world evidence study to define the prevalence of endocrine therapy-naive hormone receptor-positive locally advanced or metastatic breast cancer in the US [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-08-20.
---
paper_title: Pattern-based information extraction from pathology reports for cancer registration

paper_content:

OBJECTIVE ::: To evaluate precision and recall rates for the automatic extraction of information from free-text pathology reports. To assess the impact that implementation of pattern-based methods would have on cancer registration completeness. ::: ::: ::: METHOD ::: Over 300,000 electronic pathology reports were scanned for the extraction of Gleason score, Clark level and Breslow depth, by a number of Perl routines progressively enhanced by a trial-and-error method. An additional test set of 915 reports potentially containing Gleason score was used for evaluation. ::: ::: ::: RESULTS ::: Values for recall and precision of over 98 and 99%, respectively, were easily reached. Potential increase in cancer staging completeness of up to 32% was proved. ::: ::: ::: CONCLUSIONS ::: In cancer registration, simple pattern matching applied to free-text documents can be effectively used to improve completeness and accuracy of pathology information.
---
paper_title: Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

paper_content:

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.
---
paper_title: Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records.

paper_content:

Purpose ::: Risk stratification underlies system-wide efforts to promote the delivery of appropriate prostate cancer care. Although the elements of risk stratum are available in the electronic medical record, manual data collection is resource intensive. Therefore, we investigated the feasibility and accuracy of an automated data extraction method using natural language processing (NLP) to determine prostate cancer risk stratum. ::: ::: ::: Methods ::: Manually collected clinical stage, biopsy Gleason score, and preoperative prostate-specific antigen (PSA) values from our prospective prostatectomy database were used to categorize patients as low, intermediate, or high risk by D'Amico risk classification. NLP algorithms were developed to automate the extraction of the same data points from the electronic medical record, and risk strata were recalculated. The ability of NLP to identify elements sufficient to calculate risk (recall) was calculated, and the accuracy of NLP was compared with that of manually collected data using the weighted Cohen's κ statistic. ::: ::: ::: Results ::: Of the 2,352 patients with available data who underwent prostatectomy from 2010 to 2014, NLP identified sufficient elements to calculate risk for 1,833 (recall, 78%). NLP had a 91% raw agreement with manual risk stratification (κ = 0.92; 95% CI, 0.90 to 0.93). The κ statistics for PSA, Gleason score, and clinical stage extraction by NLP were 0.86, 0.91, and 0.89, respectively; 91.9% of extracted PSA values were within ± 1.0 ng/mL of the manually collected PSA levels. ::: ::: ::: Conclusion ::: NLP can achieve more than 90% accuracy on D'Amico risk stratification of localized prostate cancer, with adequate recall. This figure is comparable to other NLP tasks and illustrates the known trade off between recall and accuracy. Automating the collection of risk characteristics could be used to power real-time decision support tools and scale up quality measurement in cancer care.
---
paper_title: Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

paper_content:

Abstract Objective: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. Materials and Methods: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and ...
---
paper_title: An ICT infrastructure to integrate clinical and molecular data in oncology research

paper_content:

BackgroundThe ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface.MethodsOnco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system.Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services.ResultsOnco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts.ConclusionsOnco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module.
---
paper_title: Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record

paper_content:

Background. Difficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates. Objective. To use Electronic Health Record (EHR) data to identify patients with prior CRC testing. Design. A clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests. Setting. Large academic medical center. Patients. 200 patients ≥50 years old who had completed ≥2 non-acute primary care visits within a 1-year period. Measures. Recall and precision of the NLP system, billing records, and human chart review were compared to a reference standard of human review of all available information sources. Results. For identification of all CRC t...
---
paper_title: The ONCO-I2b2 Project: Integrating Biobank Information and Clinical Data to Support Translational Research in Oncology

paper_content:

The University of Pavia and the IRCCS Fondazione Salvatore Maugeri of Pavia (FSM), has recently started an IT initiative to support clinical research in oncology, called ONCO-i2b2. ONCO-i2b2, funded by the Lombardia region, grounds on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) NIH project. Using i2b2 and new software modules purposely designed, data coming from multiple sources are integrated and jointly queried. The core of the integration process stands in retrieving and merging data from the biobank management software and from the FSM hospital information system. The integration process is based on a ontology of the problem domain and on open-source software integration modules. A Natural Language Processing module has been implemented, too. This module automatically extracts clinical information of oncology patients from unstructured medical records. The system currently manages more than two thousands patients and will be further implemented and improved in the next two years.
---
paper_title: Development of a Natural Language Processing Engine to Generate Bladder Cancer Pathology Data for Health Services Research

paper_content:

OBJECTIVE ::: To take the first step toward assembling population-based cohorts of patients with bladder cancer with longitudinal pathology data, we developed and validated a natural language processing (NLP) engine that abstracts pathology data from full-text pathology reports. ::: ::: ::: METHODS ::: Using 600 bladder pathology reports randomly selected from the Department of Veterans Affairs, we developed and validated an NLP engine to abstract data on histology, invasion (presence vs absence and depth), grade, the presence of muscularis propria, and the presence of carcinoma in situ. Our gold standard was based on an independent review of reports by 2 urologists, followed by adjudication. We assessed the NLP performance by calculating the accuracy, the positive predictive value, and the sensitivity. We subsequently applied the NLP engine to pathology reports from 10,725 patients with bladder cancer. ::: ::: ::: RESULTS ::: When comparing the NLP output to the gold standard, NLP achieved the highest accuracy (0.98) for the presence vs the absence of carcinoma in situ. Accuracy for histology, invasion (presence vs absence), grade, and the presence of muscularis propria ranged from 0.83 to 0.96. The most challenging variable was depth of invasion (accuracy 0.68), with an acceptable positive predictive value for lamina propria (0.82) and for muscularis propria (0.87) invasion. The validated engine was capable of abstracting pathologic characteristics for 99% of the patients with bladder cancer. ::: ::: ::: CONCLUSION ::: NLP had high accuracy for 5 of 6 variables and abstracted data for the vast majority of the patients. This now allows for the assembly of population-based cohorts with longitudinal pathology data.
---
paper_title: Facilitating cancer research using natural language processing of pathology reports.

paper_content:

Many ongoing clinical research projects, such as projects involving studies associated with cancer, involve manual capture of information in surgical pathology reports so that the information can be used to determine the eligibility of recruited patients for the study and to provide other information, such as cancer prognosis. Natural language processing (NLP) systems offer an alternative to automated coding, but pathology reports have certain features that are difficult for NLP systems. This paper describes how a preprocessor was integrated with an existing NLP system (MedLEE) in order to reduce modification to the NLP system and to improve performance. The work was done in conjunction with an ongoing clinical research project that assesses disparities and risks of developing breast cancer for minority women. An evaluation of the system was performed using manually coded data from the research project's database as a gold standard. The evaluation outcome showed that the extended NLP system had a sensitivity of 90.6% and a precision of 91.6%. Results indicated that this system performed satisfactorily for capturing information for the cancer research project.
---
paper_title: Extent of Risk-Aligned Surveillance for Cancer Recurrence Among Patients With Early-Stage Bladder Cancer

paper_content:

Importance Cancer care guidelines recommend aligning surveillance frequency with underlying cancer risk, ie, more frequent surveillance for patients at high vs low risk of cancer recurrence. Objective To assess the extent to which such risk-aligned surveillance is practiced within US Department of Veterans Affairs facilities by classifying surveillance patterns for low- vs high-risk patients with early-stage bladder cancer. Design, Setting, and Participants US national retrospective cohort study of a population-based sample of patients diagnosed with low-risk or high-risk early-stage bladder between January 1, 2005, and December 31, 2011, with follow-up through December 31, 2014. Analyses were performed March 2017 to April 2018. The study included all Veterans Affairs facilities (n = 85) where both low- and high-risk patients were treated. Exposures Low-risk vs high-risk cancer status, based on definitions from the European Association of Urology risk stratification guidelines and on data extracted from diagnostic pathology reports via validated natural language processing algorithms. Main Outcomes and Measures Adjusted cystoscopy frequency for low-risk and high-risk patients for each facility, estimated using multilevel modeling. Results The study included 1278 low-risk and 2115 high-risk patients (median [interquartile range] age, 77 [71-82] years; 99% [3368 of 3393] male). Across facilities, the adjusted frequency of surveillance cystoscopy ranged from 3.7 to 6.2 (mean, 4.8) procedures over 2 years per patient for low-risk patients and from 4.6 to 6.0 (mean, 5.4) procedures over 2 years per patient for high-risk patients. In 70 of 85 facilities, surveillance was performed at a comparable frequency for low- and high-risk patients, differing by less than 1 cystoscopy over 2 years. Surveillance frequency among high-risk patients statistically significantly exceeded surveillance among low-risk patients at only 4 facilities. Across all facilities, surveillance frequencies for low- vs high-risk patients were moderately strongly correlated (r = 0.52;P Conclusions and Relevance Patients with early-stage bladder cancer undergo cystoscopic surveillance at comparable frequencies regardless of risk. This finding highlights the need to understand barriers to risk-aligned surveillance with the goal of making it easier for clinicians to deliver it in routine practice.
---
paper_title: Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

paper_content:

The paper assesses the utility of Medtex on automating Cancer Registry notifications from narrative histology and cytology reports from the Queensland state-wide pathology information system. A corpus of 45.3 million pathology HL7 messages (including 119,581 histology and cytology reports) from a Queensland pathology repository for the year of 2009 was analysed by Medtex for cancer notification. Reports analysed by Medtex were consolidated at a patient level and compared against patients with notifiable cancers from the Queensland Oncology Repository (QOR). A stratified random sample of 1,000 patients was manually reviewed by a cancer clinical coder to analyse agreements and discrepancies. Sensitivity of 96.5% (95% confidence interval: 94.5-97.8%), specificity of 96.5% (95.3-97.4%) and positive predictive value of 83.7% (79.6-86.8%) were achieved for identifying cancer notifiable patients. Medtex achieved high sensitivity and specificity across the breadth of cancers, report types, pathology laboratories and pathologists throughout the State of Queensland. The high sensitivity also resulted in the identification of cancer patients that were not found in the QOR. High sensitivity was at the expense of positive predictive value; however, these cases may be considered as lower priority to Cancer Registries as they can be quickly reviewed. Error analysis revealed that system errors tended to be tumour stream dependent. Medtex is proving to be a promising medical text analytic system. High value cancer information can be generated through intelligent data classification and extraction on large volumes of unstructured pathology reports.
---
paper_title: Automatic Extraction of Breast Cancer Information from Clinical Reports

paper_content:

The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features. Its underlying information extraction pipeline employs a rule-based grammar approach that is integrated with semantic technologies to determine the relevant information from the patient record. The accuracy of the system, developed with nine thousand clinical documents, reaches accuracy levels of 90% for lymph node status and 69% for the structurally most complex feature, the hormone status.
---
paper_title: Machine learning to parse breast pathology reports in Chinese

paper_content:

Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics community to support automating this work via machine learning in English. Our contribution is to provide an automated approach to construct such structured databases in Chinese, and to set the stage for extraction from other languages. We collected 2104 de-identified Chinese benign and malignant breast pathology reports from Hunan Cancer Hospital. Physicians with native Chinese proficiency reviewed the reports and annotated a variety of binary and numerical pathologic entities. After excluding 78 cases with a bilateral lesion in the same report, 1216 cases were used as a training set for the algorithm, which was then refined by 405 development cases. The Natural language processing algorithm was tested by using the remaining 405 cases to evaluate the machine learning outcome. The model was used to extract 13 binary entities and 8 numerical entities. When compared to physicians with native Chinese proficiency, the model showed a per-entity accuracy from 91 to 100% for all common diagnoses on the test set. The overall accuracy of binary entities was 98% and of numerical entities was 95%. In a per-report evaluation for binary entities with more than 100 training cases, 85% of all the testing reports were completely correct and 11% had an error in 1 out of 22 entities. We have demonstrated that Chinese breast pathology reports can be automatically parsed into structured data using standard machine learning approaches. The results of our study demonstrate that techniques effective in parsing English reports can be scaled to other languages.
---
paper_title: Automated extraction and normalization of findings from cancer-related free-text radiology reports.

paper_content:

We describe the performance of a particular natural language processing system that uses knowledge vectors to extract findings from radiology reports. LifeCode® (A-Life Medical, Inc.) has been successfully coding reports for billing purposes for several years. In this study, we describe the use of LifeCode® to code all findings within a set of 500 cancer-related radiology reports against a test set in which all findings were manually tagged. The system was trained with 1400 reports prior to running the test set. Results: LifeCode® had a recall of 84.5% and precision of 95.7% in the coding of cancer-related radiology report findings. Conclusion: Despite the use of a modest sized training set and minimal training iterations, when applied to cancer-related reports the system achieved recall and precision measures comparable to other reputable natural language processors in this domain.
---
paper_title: Creating a rule based system for text mining of Norwegian breast cancer pathology reports

paper_content:

National cancer registries collect cancer related information from multiple sources and make it available for research. Part of this information originates from pathology reports, and in this pre-study the possibility of a system for automatic extraction of information from Norwegian pathology reports is investigated. A set of 40 pathology reports describing breast cancer tissue samples has been used to develop a rule based system for information extraction. To validate the performance of this system its output has been compared to the data produced by experts doing manual encoding of the same pathology reports. On average, a precision of 80%, a recall of 98% and an F-score of 86% has been achieved, showing that such a system is indeed feasible.
---
paper_title: Validation of natural language processing (NLP) for automated ascertainment of EGFR and ALK tests in SEER cases of non-small cell lung cancer (NSCLC).

paper_content:

6528Background: The Surveillance, Epidemiology, and End Results (SEER) registries lack information on the Epidermal Growth Factor Receptor (EGFR) mutation and Anaplastic Lymphoma Kinase (ALK) gene ...
---
paper_title: Extracting timing and status descriptors for colonoscopy testing from electronic medical records

paper_content:

Colorectal cancer (CRC) screening rates are low despite confirmed benefits. The authors investigated the use of natural language processing (NLP) to identify previous colonoscopy screening in electronic records from a random sample of 200 patients at least 50 years old. The authors developed algorithms to recognize temporal expressions and ‘status indicators’, such as ‘patient refused’, or ‘test scheduled’. The new methods were added to the existing KnowledgeMap concept identifier system, and the resulting system was used to parse electronic medical records (EMR) to detect completed colonoscopies. Using as the ‘gold standard’ expert physicians’ manual review of EMR notes, the system identified timing references with a recall of 0.91 and precision of 0.95, colonoscopy status indicators with a recall of 0.82 and precision of 0.95, and references to actually completed colonoscopies with recall of 0.93 and precision of 0.95. The system was superior to using colonoscopy billing codes alone. Health services researchers and clinicians may find NLP a useful adjunct to traditional methods to detect CRC screening status. Further investigations must validate extension of NLP approaches for other types of CRC screening applications.
---
paper_title: Identification of Patients with Family History of Pancreatic Cancer - Investigation of an NLP System Portability

paper_content:

In this study we have developed a rule-based natural language processing (NLP) system to identify patients with family history of pancreatic cancer. The algorithm was developed in a Unstructured Information Management Architecture (UIMA) framework and consisted of section segmentation, relation discovery, and negation detection. The system was evaluated on data from two institutions. The family history identification precision was consistent across the institutions shifting from 88.9% on Indiana University (IU) dataset to 87.8% on Mayo Clinic dataset. Customizing the algorithm on the the Mayo Clinic data, increased its precision to 88.1%. The family member relation discovery achieved precision, recall, and F-measure of 75.3%, 91.6% and 82.6% respectively. Negation detection resulted in precision of 99.1%. The results show that rule-based NLP approaches for specific information extraction tasks are portable across institutions; however customization of the algorithm on the new dataset improves its performance.
---
paper_title: Using Natural Language Processing to Extract Abnormal Results From Cancer Screening Reports

paper_content:

OBJECTIVES ::: Numerous studies show that follow-up of abnormal cancer screening results, such as mammography and Papanicolaou (Pap) smears, is frequently not performed in a timely manner. A contributing factor is that abnormal results may go unrecognized because they are buried in free-text documents in electronic medical records (EMRs), and, as a result, patients are lost to follow-up. By identifying abnormal results from free-text reports in EMRs and generating alerts to clinicians, natural language processing (NLP) technology has the potential for improving patient care. The goal of the current study was to evaluate the performance of NLP software for extracting abnormal results from free-text mammography and Pap smear reports stored in an EMR. ::: ::: ::: METHODS ::: A sample of 421 and 500 free-text mammography and Pap reports, respectively, were manually reviewed by a physician, and the results were categorized for each report. We tested the performance of NLP to extract results from the reports. The 2 assessments (criterion standard versus NLP) were compared to determine the precision, recall, and accuracy of NLP. ::: ::: ::: RESULTS ::: When NLP was compared with manual review for mammography reports, the results were as follows: precision, 98% (96%-99%); recall, 100% (98%-100%); and accuracy, 98% (96%-99%). For Pap smear reports, the precision, recall, and accuracy of NLP were all 100%. ::: ::: ::: CONCLUSIONS ::: Our study developed NLP models that accurately extract abnormal results from mammography and Pap smear reports. Plans include using NLP technology to generate real-time alerts and reminders for providers to facilitate timely follow-up of abnormal results.
---
paper_title: Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm

paper_content:

Objective Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem. ::: ::: Materials and methods SCENT employs hierarchical classification rules to identify and extract information from electronic pathology reports. Reports are analyzed and coded using a dictionary of clinical concepts and associated SNOMED codes. To assess the accuracy of SCENT, validation was conducted using manual review of pathology reports from a random sample of 400 breast and 400 prostate cancer patients diagnosed at Kaiser Permanente Southern California. Trained abstractors classified the malignancy status of each report. ::: ::: Results Classifications of SCENT were highly concordant with those of abstractors, achieving κ of 0.96 and 0.95 in the breast and prostate cancer groups, respectively. SCENT identified 51 of 54 new primary and 60 of 61 recurrent cancer cases across both groups, with only three false positives in 792 true benign cases. Measures of sensitivity, specificity, positive predictive value, and negative predictive value exceeded 94% in both cancer groups. ::: ::: Discussion Favorable validation results suggest that SCENT can be used to identify, extract, and code information from pathology report text. Consequently, SCENT has wide applicability in research and clinical care. Further assessment will be needed to validate performance with other clinical text sources, particularly those with greater linguistic variability. ::: ::: Conclusion SCENT is proof of concept for SAS-based natural language processing applications that can be easily shared between institutions and used to support clinical and epidemiologic research.
---
paper_title: Pattern-based information extraction from pathology reports for cancer registration

paper_content:

OBJECTIVE ::: To evaluate precision and recall rates for the automatic extraction of information from free-text pathology reports. To assess the impact that implementation of pattern-based methods would have on cancer registration completeness. ::: ::: ::: METHOD ::: Over 300,000 electronic pathology reports were scanned for the extraction of Gleason score, Clark level and Breslow depth, by a number of Perl routines progressively enhanced by a trial-and-error method. An additional test set of 915 reports potentially containing Gleason score was used for evaluation. ::: ::: ::: RESULTS ::: Values for recall and precision of over 98 and 99%, respectively, were easily reached. Potential increase in cancer staging completeness of up to 32% was proved. ::: ::: ::: CONCLUSIONS ::: In cancer registration, simple pattern matching applied to free-text documents can be effectively used to improve completeness and accuracy of pathology information.
---
paper_title: Validation of natural language processing to extract breast cancer pathology procedures and results

paper_content:

Background: Pathology reports typically require manual review to abstract research data. We developed a natural language processing (NLP) system to automatically interpret free-text breast pathology reports with limited assistance from manual abstraction. Methods: We used an iterative approach of machine learning algorithms and constructed groups of related findings to identify breast-related procedures and results from free-text pathology reports. We evaluated the NLP system using an all-or-nothing approach to determine which reports could be processed entirely using NLP and which reports needed manual review beyond NLP. We divided 3234 reports for development (2910, 90%), and evaluation (324, 10%) purposes using manually reviewed pathology data as our gold standard. Results: NLP correctly coded 12.7% of the evaluation set, flagged 49.1% of reports for manual review, incorrectly coded 30.8%, and correctly omitted 7.4% from the evaluation set due to irrelevancy (i.e. not breast-related). Common procedures and results were identified correctly (e.g. invasive ductal with 95.5% precision and 94.0% sensitivity), but entire reports were flagged for manual review because of rare findings and substantial variation in pathology report text. Conclusions: The NLP system we developed did not perform sufficiently for abstracting entire breast pathology reports. The all-or-nothing approach resulted in too broad of a scope of work and limited our flexibility to identify breast pathology procedures and results. Our NLP system was also limited by the lack of the gold standard data on rare findings and wide variation in pathology text. Focusing on individual, common elements and improving pathology text report standardization may improve performance.
---
paper_title: Tumor reference resolution and characteristic extraction in radiology reports for liver cancer stage prediction

paper_content:

Display Omitted Reference resolution is essential for understanding radiology report free text.Largest size and tumor count prediction improves with reference resolution.Different applications have different tolerances for reference resolution errors. BackgroundAnaphoric references occur ubiquitously in clinical narrative text. However, the problem, still very much an open challenge, is typically less aggressively focused on in clinical text domain applications. Furthermore, existing research on reference resolution is often conducted disjointly from real-world motivating tasks. ObjectiveIn this paper, we present our machine-learning system that automatically performs reference resolution and a rule-based system to extract tumor characteristics, with component-based and end-to-end evaluations. Specifically, our goal was to build an algorithm that takes in tumor templates and outputs tumor characteristic, e.g. tumor number and largest tumor sizes, necessary for identifying patient liver cancer stage phenotypes. ResultsOur reference resolution system reached a modest performance of 0.66 F1 for the averaged MUC, B-cubed, and CEAF scores for coreference resolution and 0.43 F1 for particularization relations. However, even this modest performance was helpful to increase the automatic tumor characteristics annotation substantially over no reference resolution. ConclusionExperiments revealed the benefit of reference resolution even for relatively simple tumor characteristics variables such as largest tumor size. However we found that different overall variables had different tolerances to reference resolution upstream errors, highlighting the need to characterize systems by end-to-end evaluations.
---
paper_title: Hierarchical attention networks for information extraction from cancer pathology reports

paper_content:

Objective ::: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufﬁciently capture syntactic and semantic contexts from free-text documents. ::: ::: ::: Materials and Methods ::: Data for our analyses were obtained from 942 deidentiﬁed pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classiﬁcation, matched to G1-G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network. ::: ::: ::: Results ::: Our results demonstrate that for both information tasks, HAN performed signiﬁcantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macro F-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460). ::: ::: ::: Conclusions ::: HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.
---
paper_title: Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

paper_content:

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.
---
paper_title: Classifying tumor event attributes in radiology reports

paper_content:

Radiology reports contain vital diagnostic information that characterizes patient disease progression. However, information from reports is represented in free text, which is difficult to query against for secondary use. Automatic extraction of important information, such as tumor events using natural language processing, offers possibilities in improved clinical decision support, cohort identification, and retrospective evidence-based research for cancer patients. The goal of this work was to classify tumor event attributes: negation, temporality, and malignancy, using biomedical ontology and linguistically enriched features. We report our results on an annotated corpus of 101 hepatocellular carcinoma patient radiology reports, and show that the improved classification improves overall template structuring. Classification performances for negation identification, past temporality classification, and malignancy classification were at 0.94, 0.62, and 0.77 F1, respectively. Incorporating the attributes into full templates led to an improvement of 0.72 F1 for tumor-related events over a baseline of 0.65 F1. Improvement of negation, malignancy, and temporality classifications led to significant improvements in template extraction for the majority of categories. We present our machine-learning approach to identifying these several tumor event attributes from radiology reports, as well as highlight challenges and areas for improvement.
---
paper_title: Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

paper_content:

Abstract Objective: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. Materials and Methods: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and ...
---
paper_title: Pathologic findings in reduction mammoplasty procedures identified by natural language processing of breast pathology reports: A surrogate for the population incidence of cancer and high risk lesions.

paper_content:

e13569Background: Breast reduction surgery removes a random sample of breast tissue in otherwise asymptomatic women and thus provides a method to evaluate the background incidence of breast patholo...
---
paper_title: DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

paper_content:

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR .
---
paper_title: Machine learning to parse breast pathology reports in Chinese

paper_content:

Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics community to support automating this work via machine learning in English. Our contribution is to provide an automated approach to construct such structured databases in Chinese, and to set the stage for extraction from other languages. We collected 2104 de-identified Chinese benign and malignant breast pathology reports from Hunan Cancer Hospital. Physicians with native Chinese proficiency reviewed the reports and annotated a variety of binary and numerical pathologic entities. After excluding 78 cases with a bilateral lesion in the same report, 1216 cases were used as a training set for the algorithm, which was then refined by 405 development cases. The Natural language processing algorithm was tested by using the remaining 405 cases to evaluate the machine learning outcome. The model was used to extract 13 binary entities and 8 numerical entities. When compared to physicians with native Chinese proficiency, the model showed a per-entity accuracy from 91 to 100% for all common diagnoses on the test set. The overall accuracy of binary entities was 98% and of numerical entities was 95%. In a per-report evaluation for binary entities with more than 100 training cases, 85% of all the testing reports were completely correct and 11% had an error in 1 out of 22 entities. We have demonstrated that Chinese breast pathology reports can be automatically parsed into structured data using standard machine learning approaches. The results of our study demonstrate that techniques effective in parsing English reports can be scaled to other languages.
---
paper_title: Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports

paper_content:

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.
---
paper_title: Pathologic findings in reduction mammoplasty specimens: a surrogate for the population prevalence of breast cancer and high-risk lesions

paper_content:

Mammoplasty removes random samples of breast tissue from asymptomatic women providing a unique method for evaluating background prevalence of breast pathology in normal population. Our goal was to identify the rate of atypical breast lesions and cancers in women of various ages in the largest mammoplasty cohort reported to date. We analyzed pathologic reports from patients undergoing bilateral mammoplasty, using natural language processing algorithm, verified by human review. Patients with a prior history of breast cancer or atypia were excluded. A total of 4775 patients were deemed eligible. Median age was 40 (range 13–86) and was higher in patients with any incidental finding compared to patients with normal reports (52 vs. 39 years, p = 0.0001). Pathological findings were detected in 7.06% (337) of procedures. Benign high-risk lesions were found in 299 patients (6.26%). Invasive carcinoma and ductal carcinoma in situ were detected in 15 (0.31%) and 23 (0.48%) patients, respectively. The rate of atypias and cancers increased with age. The overall rate of abnormal findings in asymptomatic patients undergoing mammoplasty was 7.06%, increasing with age. As these results are based on random sample of breast tissue, they likely underestimate the prevalence of abnormal findings in asymptomatic women.
---
paper_title: Tumor information extraction in radiology reports for hepatocellular carcinoma patients

paper_content:

Hepatocellular carcinoma (HCC) is a deadly disease affecting the liver for which there are many available therapies. Targeting treatments towards specific patient groups necessitates defining patients by stage of disease. Criteria for such stagings include information on tumor number, size, and anatomic location, typically only found in narrative clinical text in the electronic medical record (EMR). Natural language processing (NLP) offers an automatic and scale-able means to extract this information, which can further evidence-based research. In this paper, we created a corpus of 101 radiology reports annotated for tumor information. Afterwards we applied machine learning algorithms to extract tumor information. Our inter-annotator partial match agreement scored at 0.93 and 0.90 F1 for entities and relations, respectively. Based on the annotated corpus, our sequential labeling entity extraction achieved 0.87 F1 partial match, and our maximum entropy classification relation extraction achieved scores 0.89 and 0. 74 F1 with gold and system entities, respectively.
---
paper_title: Automated Extraction of Grade, Stage, and Quality Information From Transurethral Resection of Bladder Tumor Pathology Reports Using Natural Language Processing

paper_content:

PurposeBladder cancer is initially diagnosed and staged with a transurethral resection of bladder tumor (TURBT). Patient survival is dependent on appropriate sampling of layers of the bladder, but pathology reports are dictated as free text, making large-scale data extraction for quality improvement challenging. We sought to automate extraction of stage, grade, and quality information from TURBT pathology reports using natural language processing (NLP).MethodsPatients undergoing TURBT were retrospectively identified using the Northwestern Enterprise Data Warehouse. An NLP algorithm was then created to extract information from free-text pathology reports and was iteratively improved using a training set of manually reviewed TURBTs. NLP accuracy was then validated using another set of manually reviewed TURBTs, and reliability was calculated using Cohen’s κ.ResultsOf 3,042 TURBTs identified from 2006 to 2016, 39% were classified as benign, 35% as Ta, 11% as T1, 4% as T2, and 10% as isolated carcinoma in situ...
---
paper_title: Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm

paper_content:

Objective Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem. ::: ::: Materials and methods SCENT employs hierarchical classification rules to identify and extract information from electronic pathology reports. Reports are analyzed and coded using a dictionary of clinical concepts and associated SNOMED codes. To assess the accuracy of SCENT, validation was conducted using manual review of pathology reports from a random sample of 400 breast and 400 prostate cancer patients diagnosed at Kaiser Permanente Southern California. Trained abstractors classified the malignancy status of each report. ::: ::: Results Classifications of SCENT were highly concordant with those of abstractors, achieving κ of 0.96 and 0.95 in the breast and prostate cancer groups, respectively. SCENT identified 51 of 54 new primary and 60 of 61 recurrent cancer cases across both groups, with only three false positives in 792 true benign cases. Measures of sensitivity, specificity, positive predictive value, and negative predictive value exceeded 94% in both cancer groups. ::: ::: Discussion Favorable validation results suggest that SCENT can be used to identify, extract, and code information from pathology report text. Consequently, SCENT has wide applicability in research and clinical care. Further assessment will be needed to validate performance with other clinical text sources, particularly those with greater linguistic variability. ::: ::: Conclusion SCENT is proof of concept for SAS-based natural language processing applications that can be easily shared between institutions and used to support clinical and epidemiologic research.
---
paper_title: Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

paper_content:

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases.
---
paper_title: Symbolic rule-based classification of lung cancer stages from free-text pathology reports

paper_content:

Objective To classify automatically lung tumor–node–metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. ::: ::: Design By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine–clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. ::: ::: Measurements The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. ::: ::: Results Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches. ::: ::: Conclusion A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.
---
paper_title: An ICT infrastructure to integrate clinical and molecular data in oncology research

paper_content:

BackgroundThe ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface.MethodsOnco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system.Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services.ResultsOnco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts.ConclusionsOnco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module.
---
paper_title: Automatic Extraction of Breast Cancer Information from Clinical Reports

paper_content:

The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features. Its underlying information extraction pipeline employs a rule-based grammar approach that is integrated with semantic technologies to determine the relevant information from the patient record. The accuracy of the system, developed with nine thousand clinical documents, reaches accuracy levels of 90% for lymph node status and 69% for the structurally most complex feature, the hormone status.
---
paper_title: Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

paper_content:

Abstract Objective: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. Materials and Methods: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and ...
---
paper_title: A natural language processing algorithm to measure quality prostate cancer care.

paper_content:

232Background: Electronic health records (EHRs) are a widely adopted but underutilized source of data for systematic assessment of healthcare quality. Barriers for use of this data source include its vast complexity, lack of structure, and the lack of use of standardized vocabulary and terminology by clinicians. This project aims to develop generalizable algorithms to extract useful knowledge regarding prostate cancer quality metrics from EHRs. Methods: We used EHR ICD-9/10 codes to identify prostate cancer patients receiving care at our academic medical center. Patients were confirmed in the California Cancer Registry (CCR), which provided data on tumor characteristics, treatment data, treatment outcomes and survival. We focused on three potential pretreatment process quality measures, which included documentation within 6 months prior to initial treatment of prostate-specific antigen (PSA), digital rectal exam (DRE) performance, and Gleason score. Each quality metric was defined using target terms and c...
---
paper_title: Facilitating cancer research using natural language processing of pathology reports.

paper_content:

Many ongoing clinical research projects, such as projects involving studies associated with cancer, involve manual capture of information in surgical pathology reports so that the information can be used to determine the eligibility of recruited patients for the study and to provide other information, such as cancer prognosis. Natural language processing (NLP) systems offer an alternative to automated coding, but pathology reports have certain features that are difficult for NLP systems. This paper describes how a preprocessor was integrated with an existing NLP system (MedLEE) in order to reduce modification to the NLP system and to improve performance. The work was done in conjunction with an ongoing clinical research project that assesses disparities and risks of developing breast cancer for minority women. An evaluation of the system was performed using manually coded data from the research project's database as a gold standard. The evaluation outcome showed that the extended NLP system had a sensitivity of 90.6% and a precision of 91.6%. Results indicated that this system performed satisfactorily for capturing information for the cancer research project.
---
paper_title: Automatic Extraction of Breast Cancer Information from Clinical Reports

paper_content:

The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features. Its underlying information extraction pipeline employs a rule-based grammar approach that is integrated with semantic technologies to determine the relevant information from the patient record. The accuracy of the system, developed with nine thousand clinical documents, reaches accuracy levels of 90% for lymph node status and 69% for the structurally most complex feature, the hormone status.
---
paper_title: Automated extraction and normalization of findings from cancer-related free-text radiology reports.

paper_content:

We describe the performance of a particular natural language processing system that uses knowledge vectors to extract findings from radiology reports. LifeCode® (A-Life Medical, Inc.) has been successfully coding reports for billing purposes for several years. In this study, we describe the use of LifeCode® to code all findings within a set of 500 cancer-related radiology reports against a test set in which all findings were manually tagged. The system was trained with 1400 reports prior to running the test set. Results: LifeCode® had a recall of 84.5% and precision of 95.7% in the coding of cancer-related radiology report findings. Conclusion: Despite the use of a modest sized training set and minimal training iterations, when applied to cancer-related reports the system achieved recall and precision measures comparable to other reputable natural language processors in this domain.
---
paper_title: Validation of natural language processing (NLP) for automated ascertainment of EGFR and ALK tests in SEER cases of non-small cell lung cancer (NSCLC).

paper_content:

6528Background: The Surveillance, Epidemiology, and End Results (SEER) registries lack information on the Epidermal Growth Factor Receptor (EGFR) mutation and Anaplastic Lymphoma Kinase (ALK) gene ...
---
paper_title: caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research.

paper_content:

The authors report on the development of the Cancer Tissue Information Extraction System (caTIES)dan application that supports collaborative tissue banking and text mining by leveraging existing natural language processing methods and algorithms, grid communication and security frameworks, and query visualization methods. The system fills an important need for textderived clinical data in translational research such as tissue-banking and clinical trials. The design of caTIES addresses three critical issues for informatics support of translational research: (1) federation of research data sources derived from clinical systems; (2) expressive graphical interfaces for concept-based text mining; and (3) regulatory and security model for supporting multicenter collaborative research. Implementation of the system at several Cancer Centers across the country is creating a potential network of caTIES repositories that could provide millions of de-identified clinical reports to users. The system provides an end-to-end application of medical natural language processing to support multiinstitutional translational research programs.
---
paper_title: Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study

paper_content:

Background: In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correlate the treatment regimen with the prognosis to develop evidence-based radiation therapy paradigms. These data are available mainly in forms of narrative texts or table formats with heterogeneous vocabularies. Manual extraction of the related information from these data can be time consuming and labor intensive, which is not ideal for large studies. Objective: The objective of this study was to adapt the interactive information extraction platform Information and Data Extraction using Adaptive Learning (IDEAL-X) to extract treatment and prognosis data for patients with locally advanced or inoperable non–small cell lung cancer (NSCLC). Methods: We transformed patient treatment and prognosis documents into normalized structured forms using the IDEAL-X system for easy data navigation. The adaptive learning and user-customized controlled toxicity vocabularies were applied to extract categorized treatment and prognosis data, so as to generate structured output. Results: In total, we extracted data from 261 treatment and prognosis documents relating to 50 patients, with overall precision and recall more than 93% and 83%, respectively. For toxicity information extractions, which are important to study patient posttreatment side effects and quality of life, the precision and recall achieved 95.7% and 94.5% respectively. Conclusions: The IDEAL-X system is capable of extracting study data regarding NSCLC chemoradiation patients with significant accuracy and effectiveness, and therefore can be used in large-scale radiotherapy clinical data studies. [JMIR Med Inform 2018;6(1):e8]
---
paper_title: Using Natural Language Processing to Extract Abnormal Results From Cancer Screening Reports

paper_content:

OBJECTIVES ::: Numerous studies show that follow-up of abnormal cancer screening results, such as mammography and Papanicolaou (Pap) smears, is frequently not performed in a timely manner. A contributing factor is that abnormal results may go unrecognized because they are buried in free-text documents in electronic medical records (EMRs), and, as a result, patients are lost to follow-up. By identifying abnormal results from free-text reports in EMRs and generating alerts to clinicians, natural language processing (NLP) technology has the potential for improving patient care. The goal of the current study was to evaluate the performance of NLP software for extracting abnormal results from free-text mammography and Pap smear reports stored in an EMR. ::: ::: ::: METHODS ::: A sample of 421 and 500 free-text mammography and Pap reports, respectively, were manually reviewed by a physician, and the results were categorized for each report. We tested the performance of NLP to extract results from the reports. The 2 assessments (criterion standard versus NLP) were compared to determine the precision, recall, and accuracy of NLP. ::: ::: ::: RESULTS ::: When NLP was compared with manual review for mammography reports, the results were as follows: precision, 98% (96%-99%); recall, 100% (98%-100%); and accuracy, 98% (96%-99%). For Pap smear reports, the precision, recall, and accuracy of NLP were all 100%. ::: ::: ::: CONCLUSIONS ::: Our study developed NLP models that accurately extract abnormal results from mammography and Pap smear reports. Plans include using NLP technology to generate real-time alerts and reminders for providers to facilitate timely follow-up of abnormal results.
---
paper_title: PS2-26: Coordinating Heterogeneous Data and Mixed Collection Methods to Support Population-Based Cancer Screening Research

paper_content:

Background/Aims ::: The central goal of Population-Based Research Optimizing Screening through Personalized Regimens (PROSPR), a recently-funded NCI initiative, is to develop multi-site, transdisciplinary research to improve the screening process for breast, colon, and cervical cancer. To support this goal, we aim to collect, document, and manage data for the entire colorectal cancer (CRC) screening process at Group Health (GH), an integrated health system and PROSPR Research Center. We describe the data sources, types, and collection methods being used to assemble the breadth of relevant information on patients, providers, tests, pathology, treatment, and outcomes this effort requires.
---
paper_title: Toward Electronic Surveillance of Invasive Mold Diseases in Hematology-Oncology Patients: An Expert System Combining Natural Language Processing of Chest Computed Tomography Reports, Microbiology, and Antifungal Drug Data

paper_content:

PurposeProspective epidemiologic surveillance of invasive mold disease (IMD) in hematology patients is hampered by the absence of a reliable laboratory prompt. This study develops an expert system for electronic surveillance of IMD that combines probabilities using natural language processing (NLP) of computed tomography (CT) reports with microbiology and antifungal drug data to improve prediction of IMD.MethodsMicrobiology indicators and antifungal drug–dispensing data were extracted from hospital information systems at three tertiary hospitals for 123 hematology-oncology patients. Of this group, 64 case patients had 26 probable/proven IMD according to international definitions, and 59 patients were uninfected controls. Derived probabilities from NLP combined with medical expertise identified patients at high likelihood of IMD, with remaining patients processed by a machine-learning classifier trained on all available features.ResultsCompared with the baseline text classifier, the expert system that inco...
---
paper_title: Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

paper_content:

Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute’s Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating centers to report structured data across the cancer screening continuum. We evaluate the accuracy of automated information extraction of imaging findings from radiology reports, which are available as unstructured text. We present prevalence estimates of imaging findings for breast imaging received by women who obtained care in a primary care network participating in PROSPR (n = 139,953 radiology reports) and compared automatically extracted data elements to a “gold standard” based on manual review for a validation sample of 941 randomly selected radiology reports, including mammograms, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI). The prevalence of imaging findings vary by data element and modality (e.g., suspicious calcification noted in 2.6 % of screening mammograms, 12.1 % of diagnostic mammograms, and 9.4 % of tomosynthesis exams). In the validation sample, the accuracy of identifying imaging findings, including suspicious calcifications, masses, and architectural distortion (on mammogram and tomosynthesis); masses, cysts, non-mass enhancement, and enhancing foci (on MRI); and masses and cysts (on ultrasound), range from 0.8 to1.0 for recall, precision, and F-measure. Information extraction tools can be used for accurate documentation of imaging findings as structured data elements from text reports for a variety of breast imaging modalities. These data can be used to populate screening registries to help elucidate more effective breast cancer screening processes.
---
paper_title: Validation of natural language processing to extract breast cancer pathology procedures and results

paper_content:

Background: Pathology reports typically require manual review to abstract research data. We developed a natural language processing (NLP) system to automatically interpret free-text breast pathology reports with limited assistance from manual abstraction. Methods: We used an iterative approach of machine learning algorithms and constructed groups of related findings to identify breast-related procedures and results from free-text pathology reports. We evaluated the NLP system using an all-or-nothing approach to determine which reports could be processed entirely using NLP and which reports needed manual review beyond NLP. We divided 3234 reports for development (2910, 90%), and evaluation (324, 10%) purposes using manually reviewed pathology data as our gold standard. Results: NLP correctly coded 12.7% of the evaluation set, flagged 49.1% of reports for manual review, incorrectly coded 30.8%, and correctly omitted 7.4% from the evaluation set due to irrelevancy (i.e. not breast-related). Common procedures and results were identified correctly (e.g. invasive ductal with 95.5% precision and 94.0% sensitivity), but entire reports were flagged for manual review because of rare findings and substantial variation in pathology report text. Conclusions: The NLP system we developed did not perform sufficiently for abstracting entire breast pathology reports. The all-or-nothing approach resulted in too broad of a scope of work and limited our flexibility to identify breast pathology procedures and results. Our NLP system was also limited by the lack of the gold standard data on rare findings and wide variation in pathology text. Focusing on individual, common elements and improving pathology text report standardization may improve performance.
---
paper_title: Deep learning analytics for diagnostic support of breast cancer disease management

paper_content:

Breast cancer continues to be one of the leading causes of cancer death among women. Mammogram is the standard of care for screening and diagnosis of breast cancer. The American College of Radiology developed the Breast Imaging Reporting and Data System (BI-RADS) lexicon to standardize mammographic reporting to assess cancer risk and facilitate biopsy decision-making. However, because substantial inter-observer variability remains in the application of the BI-RADS lexicon, including inappropriate term usage and missing data, current biopsy decision-making accuracy using the unstructured free text or semi-structured reports varies greatly. Hence, incorporating novel and accurate technique into breast cancer decision-making data is critical. Here, we combined natural language processing and deep learning methods to develop an analytic model that targets well-characterized and defined specific breast suspicious patient subgroups rather than a broad heterogeneous group for diagnostic support of breast cancer management.
---
paper_title: Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

paper_content:

Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute’s Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating centers to report structured data across the cancer screening continuum. We evaluate the accuracy of automated information extraction of imaging findings from radiology reports, which are available as unstructured text. We present prevalence estimates of imaging findings for breast imaging received by women who obtained care in a primary care network participating in PROSPR (n = 139,953 radiology reports) and compared automatically extracted data elements to a “gold standard” based on manual review for a validation sample of 941 randomly selected radiology reports, including mammograms, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI). The prevalence of imaging findings vary by data element and modality (e.g., suspicious calcification noted in 2.6 % of screening mammograms, 12.1 % of diagnostic mammograms, and 9.4 % of tomosynthesis exams). In the validation sample, the accuracy of identifying imaging findings, including suspicious calcifications, masses, and architectural distortion (on mammogram and tomosynthesis); masses, cysts, non-mass enhancement, and enhancing foci (on MRI); and masses and cysts (on ultrasound), range from 0.8 to1.0 for recall, precision, and F-measure. Information extraction tools can be used for accurate documentation of imaging findings as structured data elements from text reports for a variety of breast imaging modalities. These data can be used to populate screening registries to help elucidate more effective breast cancer screening processes.
---
paper_title: Deep learning analytics for diagnostic support of breast cancer disease management

paper_content:

Breast cancer continues to be one of the leading causes of cancer death among women. Mammogram is the standard of care for screening and diagnosis of breast cancer. The American College of Radiology developed the Breast Imaging Reporting and Data System (BI-RADS) lexicon to standardize mammographic reporting to assess cancer risk and facilitate biopsy decision-making. However, because substantial inter-observer variability remains in the application of the BI-RADS lexicon, including inappropriate term usage and missing data, current biopsy decision-making accuracy using the unstructured free text or semi-structured reports varies greatly. Hence, incorporating novel and accurate technique into breast cancer decision-making data is critical. Here, we combined natural language processing and deep learning methods to develop an analytic model that targets well-characterized and defined specific breast suspicious patient subgroups rather than a broad heterogeneous group for diagnostic support of breast cancer management.
---
paper_title: Automatic Extraction of Breast Cancer Information from Clinical Reports

paper_content:

The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features. Its underlying information extraction pipeline employs a rule-based grammar approach that is integrated with semantic technologies to determine the relevant information from the patient record. The accuracy of the system, developed with nine thousand clinical documents, reaches accuracy levels of 90% for lymph node status and 69% for the structurally most complex feature, the hormone status.
---
paper_title: Using Natural Language Processing to Extract Abnormal Results From Cancer Screening Reports

paper_content:

OBJECTIVES ::: Numerous studies show that follow-up of abnormal cancer screening results, such as mammography and Papanicolaou (Pap) smears, is frequently not performed in a timely manner. A contributing factor is that abnormal results may go unrecognized because they are buried in free-text documents in electronic medical records (EMRs), and, as a result, patients are lost to follow-up. By identifying abnormal results from free-text reports in EMRs and generating alerts to clinicians, natural language processing (NLP) technology has the potential for improving patient care. The goal of the current study was to evaluate the performance of NLP software for extracting abnormal results from free-text mammography and Pap smear reports stored in an EMR. ::: ::: ::: METHODS ::: A sample of 421 and 500 free-text mammography and Pap reports, respectively, were manually reviewed by a physician, and the results were categorized for each report. We tested the performance of NLP to extract results from the reports. The 2 assessments (criterion standard versus NLP) were compared to determine the precision, recall, and accuracy of NLP. ::: ::: ::: RESULTS ::: When NLP was compared with manual review for mammography reports, the results were as follows: precision, 98% (96%-99%); recall, 100% (98%-100%); and accuracy, 98% (96%-99%). For Pap smear reports, the precision, recall, and accuracy of NLP were all 100%. ::: ::: ::: CONCLUSIONS ::: Our study developed NLP models that accurately extract abnormal results from mammography and Pap smear reports. Plans include using NLP technology to generate real-time alerts and reminders for providers to facilitate timely follow-up of abnormal results.
---
paper_title: Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

paper_content:

Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute’s Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating centers to report structured data across the cancer screening continuum. We evaluate the accuracy of automated information extraction of imaging findings from radiology reports, which are available as unstructured text. We present prevalence estimates of imaging findings for breast imaging received by women who obtained care in a primary care network participating in PROSPR (n = 139,953 radiology reports) and compared automatically extracted data elements to a “gold standard” based on manual review for a validation sample of 941 randomly selected radiology reports, including mammograms, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI). The prevalence of imaging findings vary by data element and modality (e.g., suspicious calcification noted in 2.6 % of screening mammograms, 12.1 % of diagnostic mammograms, and 9.4 % of tomosynthesis exams). In the validation sample, the accuracy of identifying imaging findings, including suspicious calcifications, masses, and architectural distortion (on mammogram and tomosynthesis); masses, cysts, non-mass enhancement, and enhancing foci (on MRI); and masses and cysts (on ultrasound), range from 0.8 to1.0 for recall, precision, and F-measure. Information extraction tools can be used for accurate documentation of imaging findings as structured data elements from text reports for a variety of breast imaging modalities. These data can be used to populate screening registries to help elucidate more effective breast cancer screening processes.
---
paper_title: Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

paper_content:

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.
---
paper_title: Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

paper_content:

Abstract Objective: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. Materials and Methods: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and ...
---
paper_title: DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

paper_content:

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR .
---
paper_title: Longitudinal analysis of pain in patients with metastatic prostate cancer using natural language processing of medical record text

paper_content:

Objectives To test the feasibility of using text mining to depict meaningfully the experience of pain in patients with metastatic prostate cancer, to identify novel pain phenotypes, and to propose methods for longitudinal visualization of pain status. ::: ::: Materials and methods Text from 4409 clinical encounters for 33 men enrolled in a 15-year longitudinal clinical/molecular autopsy study of metastatic prostate cancer (Project to ELIminate lethal CANcer) was subjected to natural language processing (NLP) using Unified Medical Language System-based terms. A four-tiered pain scale was developed, and logistic regression analysis identified factors that correlated with experience of severe pain during each month. ::: ::: Results NLP identified 6387 pain and 13 827 drug mentions in the text. Graphical displays revealed the pain ‘landscape’ described in the textual records and confirmed dramatically increasing levels of pain in the last years of life in all but two patients, all of whom died from metastatic cancer. Severe pain was associated with receipt of opioids (OR=6.6, p<0.0001) and palliative radiation (OR=3.4, p=0.0002). Surprisingly, no severe or controlled pain was detected in two of 33 subjects' clinical records. Additionally, the NLP algorithm proved generalizable in an evaluation using a separate data source (889 Informatics for Integrating Biology and the Bedside (i2b2) discharge summaries). ::: ::: Discussion Patterns in the pain experience, undetectable without the use of NLP to mine the longitudinal clinical record, were consistent with clinical expectations, suggesting that meaningful NLP-based pain status monitoring is feasible. Findings in this initial cohort suggest that ‘outlier’ pain phenotypes useful for probing the molecular basis of cancer pain may exist. ::: ::: Limitations The results are limited by a small cohort size and use of proprietary NLP software. ::: ::: Conclusions We have established the feasibility of tracking longitudinal patterns of pain by text mining of free text clinical records. These methods may be useful for monitoring pain management and identifying novel cancer phenotypes.
---
paper_title: Pattern-based information extraction from pathology reports for cancer registration

paper_content:

OBJECTIVE ::: To evaluate precision and recall rates for the automatic extraction of information from free-text pathology reports. To assess the impact that implementation of pattern-based methods would have on cancer registration completeness. ::: ::: ::: METHOD ::: Over 300,000 electronic pathology reports were scanned for the extraction of Gleason score, Clark level and Breslow depth, by a number of Perl routines progressively enhanced by a trial-and-error method. An additional test set of 915 reports potentially containing Gleason score was used for evaluation. ::: ::: ::: RESULTS ::: Values for recall and precision of over 98 and 99%, respectively, were easily reached. Potential increase in cancer staging completeness of up to 32% was proved. ::: ::: ::: CONCLUSIONS ::: In cancer registration, simple pattern matching applied to free-text documents can be effectively used to improve completeness and accuracy of pathology information.
---
paper_title: Deep learning analytics for diagnostic support of breast cancer disease management

paper_content:

Breast cancer continues to be one of the leading causes of cancer death among women. Mammogram is the standard of care for screening and diagnosis of breast cancer. The American College of Radiology developed the Breast Imaging Reporting and Data System (BI-RADS) lexicon to standardize mammographic reporting to assess cancer risk and facilitate biopsy decision-making. However, because substantial inter-observer variability remains in the application of the BI-RADS lexicon, including inappropriate term usage and missing data, current biopsy decision-making accuracy using the unstructured free text or semi-structured reports varies greatly. Hence, incorporating novel and accurate technique into breast cancer decision-making data is critical. Here, we combined natural language processing and deep learning methods to develop an analytic model that targets well-characterized and defined specific breast suspicious patient subgroups rather than a broad heterogeneous group for diagnostic support of breast cancer management.
---
paper_title: Assessing the natural language processing capabilities of IBM Watson for oncology using real Australian lung cancer cases.

paper_content:

e18229Background: Optimal treatment decisions require information from multiple sources and formats even in the context of electronic medical records. In addition, structured data such as that coll...
---
paper_title: Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

paper_content:

Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute’s Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating centers to report structured data across the cancer screening continuum. We evaluate the accuracy of automated information extraction of imaging findings from radiology reports, which are available as unstructured text. We present prevalence estimates of imaging findings for breast imaging received by women who obtained care in a primary care network participating in PROSPR (n = 139,953 radiology reports) and compared automatically extracted data elements to a “gold standard” based on manual review for a validation sample of 941 randomly selected radiology reports, including mammograms, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI). The prevalence of imaging findings vary by data element and modality (e.g., suspicious calcification noted in 2.6 % of screening mammograms, 12.1 % of diagnostic mammograms, and 9.4 % of tomosynthesis exams). In the validation sample, the accuracy of identifying imaging findings, including suspicious calcifications, masses, and architectural distortion (on mammogram and tomosynthesis); masses, cysts, non-mass enhancement, and enhancing foci (on MRI); and masses and cysts (on ultrasound), range from 0.8 to1.0 for recall, precision, and F-measure. Information extraction tools can be used for accurate documentation of imaging findings as structured data elements from text reports for a variety of breast imaging modalities. These data can be used to populate screening registries to help elucidate more effective breast cancer screening processes.
---
paper_title: DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

paper_content:

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR .
---

Title: A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

Section 1: Introduction
Description 1: This section introduces the increasing availability of unstructured clinical data in EHRs, the need for extracting detailed information for cancer research, and the focus of the paper on organizing the extracted information into frames using the linguistic theory of frame semantics.

Section 2: Materials and Methods
Description 2: This section details the study's goals, the search process for relevant papers, and the criteria for inclusion and exclusion of articles related to NLP methods for extracting cancer-related information from EHR notes.

Section 3: Screening Process
Description 3: This section outlines the process used to screen and select relevant articles for the review, including the stages of title/abstract screening and full-text review, along with the criteria applied to exclude irrelevant articles.

Section 4: Frame Construction
Description 4: This section describes how frames were constructed for each relevant article, the methods used to infer frame elements from NLP systems, and the iterative process for creating a common set of frames.

Section 5: Frame Descriptions
Description 5: This section explains the components of each constructed frame, including frame names, definitions, and elements, along with identifying the corresponding articles that extract each frame element.

Section 6: Frame Relations
Description 6: This section discusses the relationships between frames, such as 'parent-child', 'an element of', and 'associated with', and provides examples of these relations in the context of cancer information extraction.

Section 7: Most Extracted Frame Elements
Description 7: This section identifies the most commonly extracted frame elements across the reviewed papers and highlights the distribution of articles that focus on these elements.

Section 8: Papers Extracting Multiple Frames
Description 8: This section provides an overview of notable papers that extract varied types of cancer information corresponding to multiple frames and discusses common elements across different frames.

Section 9: Significant Cancer Types Based on Elements Extracted
Description 9: This section summarizes the extracted frame elements for the most commonly studied cancer types (e.g., breast, bladder, and prostate cancer) and notes the number of articles related to each cancer type.

Section 10: Frames Related to Cancer Quality Measures
Description 10: This section covers frames created for identifying quality measures information for cancer patient management and their potential applications.

Section 11: Frames Related to Image Findings
Description 11: This section reviews papers that extracted data elements from imaging findings, particularly related to breast cancer screening, and discusses the elements and findings extracted.

Section 12: Discussion
Description 12: This section synthesizes key findings from the review, highlights the redundancy and gaps in cancer information extraction efforts, and emphasizes the need for a general-purpose cancer NLP resource.

Section 13: Impact of Frame Semantics
Description 13: This section discusses the benefits and challenges of organizing cancer information using frame semantics and evaluates the utility of the proposed frames for NLP development and EHR text interpretation.

Section 14: Limitations
Description 14: This section acknowledges the limitations of the review, including possible omissions of relevant papers, inaccuracies in frame representation, and inconsistencies in frame assignment.

Section 15: Conclusions
Description 15: This section provides a summary of the review, describes its contributions to the cancer information extraction domain, and suggests how developers can use the findings to create a general-purpose cancer frame resource and NLP system.