Developing a Novel Image Marker to Predict the Responses of Neoadjuvant
  Chemotherapy (NACT) for Ovarian Cancer Patients
Ke Zhang Neman Abdoli Patrik Gilley Youkabed Sadri Xuxin Chen Theresa C. Thai Lauren Dockery Kathleen Moore Robert S. Mannel Yuchen Qiu
Objective: Neoadjuvant chemotherapy (NACT) is one kind of treatment for
advanced stage ovarian cancer patients. However, due to the nature of tumor
heterogeneity, the patients' responses to NACT varies significantly among
different subgroups. To address this clinical challenge, the purpose of this
study is to develop a novel image marker to achieve high accuracy response
prediction of the NACT at an early stage. Methods: For this purpose, we first
computed a total of 1373 radiomics features to quantify the tumor
characteristics, which can be grouped into three categories: geometric,
intensity, and texture features. Second, all these features were optimized by
principal component analysis algorithm to generate a compact and informative
feature cluster. Using this cluster as the input, an SVM based classifier was
developed and optimized to create a final marker, indicating the likelihood of
the patient being responsive to the NACT treatment. To validate this scheme, a
total of 42 ovarian cancer patients were retrospectively collected. A nested
leave-one-out cross-validation was adopted for model performance assessment.
Results: The results demonstrate that the new method yielded an AUC (area under
the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the
model achieved overall accuracy of 76.2%, positive predictive value of 70%, and
negative predictive value of 78.1%. Conclusion: This study provides meaningful
information for the development of radiomics based image markers in NACT
response prediction.