Combining AI-based Biomarkers for PDAC Survival

Lisa Hensens, Sergio Sabroso-Lasa, Caroline Verbeke, Nuria Malats, The PanGenEU Consortium, Geert Litjens, Pierpaolo Vendittelli

Keywords: Segmentation, Deep learning, Pancreatic ductal adenocarcinoma, AI-based biomarkers, survival analysis

TL;DR: We extracted multiple biomarkers and combined them to predict pdac survival

Abstract: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers due to late detection and limited treatment response. This study investigates the prognostic value of combining multiple AI-based image biomarkers—tumor-stroma ratio (TSR), mitosis density, stromal cell density (via HoVer-Net), tumor-to-tissue ratio from histopathological whole-slide images (WSIs) for survival prediction in resected PDAC patients. A multi-tissue segmentation model was developed to generate tissue masks for downstream biomarker extraction. Using logistic and Cox regression models, both univariate and multivariate survival analyses were performed across four datasets. Results show that while combining biomarkers did not outperform single-biomarker models (notably TSR), mitosis density showed consistent statistical significance and may serve as a valuable prognostic feature.

Submission Number: 14