Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports
This work addresses the critical problem of improving patient outcomes for pancreatic cancer through early intervention, though it appears incremental as it builds on existing deep learning methods applied to medical data.
The study tackled early detection and prognosis modeling for pancreatic ductal adenocarcinoma by developing a deep learning fusion model that integrates radiology reports and CT imaging to predict PDAC risk, achieving a C-index of 0.6750 on an internal dataset and 0.6435 on an external dataset for 5-year survival risk estimation.
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.