EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell
  lung cancer radiotherapy
Hamed Hooshangnejad Xue Feng Gaofeng Huang Rui Zhang Quan Chen Kai Ding
Lung cancer is a devastating disease with the highest mortality rate among
cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, which
accounts for 87% of diagnoses, require radiation therapy. Rapid treatment
initiation significantly increases the patient's survival rate and reduces the
mortality rate. Accurate tumor segmentation is a critical step in the diagnosis
and treatment of NSCLC. Manual segmentation is time and labor-consuming and
causes delays in treatment initiation. Although many lung nodule detection
methods, including deep learning-based models, have been proposed, there is
still a long-standing problem of high false positives (FPs) with most of these
methods. Here, we developed an electronic health record (EHR) guided lung tumor
auto-segmentation called EXACT-Net (EHR-enhanced eXACtitude in Tumor
segmentation), where the extracted information from EHRs using a pre-trained
large language model (LLM), was used to remove the FPs and keep the TP nodules
only. The auto-segmentation model was trained on NSCLC patients' computed
tomography (CT), and the pre-trained LLM was used with the zero-shot learning
approach. Our approach resulted in a 250% boost in successful nodule detection
using the data from ten NSCLC patients treated in our institution.