  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. Convolutional neural networks (CNNs) have been shown
to outperform these feature-based models in computer vision tasks. However,
training a CNN from scratch needs a large sample size which is not feasible in
most medical imaging studies. As an alternative solution, CNN-based transfer
learning has shown potential for achieving reasonable performance using small
datasets. In this work, we developed and validated a CNN-based transfer
learning approach for prognostication of PDAC patients for overall survival
using two independent resectable PDAC cohorts. The proposed deep transfer
learning model for prognostication of PDAC achieved the area under the receiver
operating characteristic curve of 0.74, which was significantly higher than
that of the traditional radiomics model (0.56) as well as a CNN model trained
from scratch (0.50). These results suggest that deep transfer learning may
significantly improve prognosis performance using small datasets in medical
imaging.
