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 limiteddue 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  .
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 .
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ductal adenocarcinoma
Ductal Adenocarcinoma
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, is limiteddue to multicollinearity of features and multiple testing problem, and limited performance of conventional machine learning classifiers. Deep learning architectures, such as convolutional
is limited. Convolutional
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,
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traditional techniques
these feature-based models
D
, such as object detection
R
they require large sample sizes for training which limits their development
training a CNN from scratch needs a large sample size which is not feasible in most medical imaging studies
D
the
R
datasets with small sample sizes
small datasets
A
and validated
R
in
of
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. The results showed that transfer learning approach outperformed the
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
R
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
(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
A
in medical imaging