Improving Prognostic Value of CT Deep Radiomic Features in Pancreatic Ductal Adenocarcinoma Using Transfer Learning

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone, is limited due to multicollinearity of features and multiple testing problem, and limited performance of conventional machine learning classifiers. Deep learning architectures, such as convolutional neural networks (CNNs), have been shown to outperform traditional techniques in computer vision tasks, such as object detection. However, they require large sample sizes for training which limits their development. As an alternative solution, CNN-based transfer learning has shown the potential for achieving reasonable performance using datasets with small sample sizes. In this work, we developed a CNN-based transfer learning approach for prognostication in PDAC patients for overall survival. The results showed that transfer learning approach outperformed the traditional radiomics model on PDAC data. A transfer learning approach may fill the gap between radiomics and deep learning analytics for cancer prognosis and improve performance beyond what CNNs can achieve using small datasets.