Abstract 83: AI-powered analysis of pancreatic ductal adenocarcinoma tissues to study the tumor immune ecosystem and identify novel classifiers.

Abstract Background: Digital pathology and artificial intelligence (AI) are emerging as powerful tools in immuno-oncology, enabling enhanced diagnostic and prognostic workflows. The current trend regards the application of AI on histopathological images to extract relevant features beyond human visual perception. Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, for which lack of efficient biomarkers to stratify patients and grade the response to therapy may play a significant impact. Aim: This project presents a computational pipeline integrating machine and deep learning approaches to characterize the PDAC tumor immune ecosystem and extract features with potential clinical relevance. Methods: Whole-slide images from 53 PDAC patients, including those treated and untreated with neoadjuvant chemotherapy (NAT), were analyzed. Slides were stained with H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 83.