  Cox proportional hazard model (CPH) is commonly used in clinical research for
survival analysis. In quantitative medical imaging (radiomics) studies, CPH
plays an important role in feature reduction and modeling. However, the
underlying linear assumption of CPH model limits the prognostic performance. In
addition, the multicollinearity of radiomic features and multiple testing
problem further impedes the CPH models performance. In this work, using
transfer learning, a convolutional neural network (CNN) based survival model
was built and tested on preoperative CT images of resectable Pancreatic Ductal
Adenocarcinoma (PDAC) patients. The proposed CNN-based survival model
outperformed the traditional CPH-based radiomics approach in terms of
concordance index by 22%, providing a better fit for patients' survival
patterns. The proposed CNN-based survival model outperforms CPH-based radiomics
pipeline in PDAC prognosis. This approach offers a better fit for survival
patterns based on CT images and overcomes the limitations of conventional
survival models.
