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 developed and evaluated a deep learning fusion model that integrates radiology reports and CT imaging to predict PDAC risk. The model achieved a concordance index (C-index) of 0.6750 (95% CI: 0.6429, 0.7121) and 0.6435 (95% CI: 0.6055, 0.6789) 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.
View on arXiv@article{le2025_2504.00232,
title={ Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports },
author={ David Le and Ramon Correa-Medero and Amara Tariq and Bhavik Patel and Motoyo Yano and Imon Banerjee },
journal={arXiv preprint arXiv:2504.00232},
year={ 2025 }
}