Abstract
BackgroundCox 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 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.ResultsThe proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index and index of prediction accuracy, providing a better fit for patients’ survival patterns.ConclusionsThe 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.
Highlights
Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis
Another LASSOCPH model (Model 2) was trained using transfer learning features extracted from Cohort 2
Using three features selected by LASSO-CPH, this model yielded concordance index (CI) and index of prediction accuracy (IPA) of 0.603 and 4.40%, respectively when validated in Cohort 3
Summary
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. A typical radiomics study involves image acquisition, feature extraction, feature analysis, and predictive modeling for a clinical outcome such as patient survival [1]. Efforts have been made to standardize quantitative imaging features (radiomic features) by implementing open source libraries such as PyRadiomics [2]. These feature banks contain thousands of hand-crafted formulas, designed to extract the distribution or texture information from medical images. The prognostic features are usually determined using Cox proportional hazard model (CPH) [4].
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