Abstract 3514: A deep learning model for detection and characterization of tertiary lymphoid structures in H&E-stained images from pancreatic ductal adenocarcinoma

Abstract Tertiary lymphoid structures (TLSs) are ectopic aggregates of a number of immune cells in nonlymphoid tissues under chronically inflamed environments and cancer. Emergence evidence suggested that TLSs were found in diverse cancers and TLSs have been referred as an independently predictive biomarker for immunotherapy response, especially immune checkpoint inhibitors. Moreover, the density and mature status of TLSs often positively associated with the favorable outcomes in cancers. Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related mortality with low survival rate in the whole world. Here, we developed a multi-resolution machine learning model for detection of TLSs from hema-toxylin and eosin (H 95% CI, 0. 44 - 0. 90; P = 0. 010). We developed a machine learning model that can accurately detect and classify TLSs in PDAC, and also demonstrated the prognosis value of predictive TLSs in H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 3514.