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

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.

Figure 1.

Pipeline for (a) text-only model, (b) image-only model, and (c) fusion model integrating text-based and CT volumetric information into a survival model. Sentence-BERT was used to extract embeddings from clinical report sentences, while pancreas segmentation was performed using TotalSegmentator, followed by feature extraction with PyRadiomics.

Figure 2.

Kaplan-Meier curves for risk stratification between low and high risk for the image only, text only and fusion models for the internal and external validation dataset.