Abstract 2743: Deploying artificial intelligence driven digital pathology for real world clinical decision-making in pancreatic cancer.

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, driven by limited therapeutic options and the absence of widely implemented, clinically actionable biomarkers. Transcriptomic subtyping, particularly the classical (CL) versus basal-like (BL) Moffitt classification, offers prognostic and predictive value: CL tumors show improved outcomes and greater sensitivity to 5 fluorouracil based regimens (e.g., FOLFIRINOX). However, BL tumors exhibit poor outcomes across treatment regimens and may benefit from clinical trial prioritization or intensified oversight. But routine use of RNA-based subtyping is hindered by cost, turnaround time, and restricted access to commercial assays such as Purity Independent Subtyping of Tumors (PurIST). To overcome these barriers, we developed a deep learning model that infers PDAC molecular subtypes directly from hematoxylin and eosin (H accuracy: 77%; specificity: 80%; sensitivity: 72%), comparable to existing image-based PurIST subtyping literature (AUC 0.83-0.86). Our ongoing work with larger multi-institutional datasets aims to further enhance accuracy and generalizability. This proof of concept establishes the feasibility of AI-driven digital pathology for rapid, scalable Moffitt PDAC molecular subtyping directly from WSIs of routine H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2743.