Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports
Journal:
arXiv
Published Date:
Mar 31, 2025
Abstract
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.
