Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with most cases diagnosed at stage IV and a five-year overall survival rate below 5%. Early detection and prognosis modeling are crucial for improving patient outcomes and guiding early intervention strategies. In this study, we implemented and evaluated deep learning fusion models that integrate radiology reports and CT imaging to predict PDAC risk. The DeepSurv model achieved a concordance index (C-index) of 0.6773 (95% CI: 0.6484, 0.7061) and 0.6596 (95% CI: 0.6260, 0.6937) on the internal and external dataset, respectively, for 5-year survival risk estimation. Kaplan-Meier analysis demonstrated significant separation (p<0.0001) between the low and high risk groups predicted by the fusion model. These findings highlight the potential of deep learning-based survival models in leveraging clinical and imaging data for pancreatic cancer.