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Abstract Tumor infiltrating lymphocytes (TILs) per high-power field (HPF) as measured by expert pathologists is a useful, independent, validated prognostic marker in colorectal cancer (CRC). Artificial intelligence (AI) techniques of deep learning (DL) can predict TILs directly from routinely collected, digitized hematoxylin and eosin (H2 vs. ≥2 TILs per HPF. Using longitudinal data from the population-based Molecular Epidemiology of Colorectal Cancer Study (median follow-up = 95 months), survival analyses were performed using non-parametric and Cox-proportional hazards models, with and without adjustment for age, sex, ethnicity, stage, and molecularly measured microsatellite instability (MSI). Two or more AI-predicted TILs per HPF were significantly associated with 5-year CRC-specific survival (p=0. 0000037), 5-year overall survival (p=0. 00021), and overall survival (p=0. 000067). In a Cox proportional hazards model adjusting for age, sex, ethnicity, stage, and MSI, ≥2 AI-predicted TILs per HPF was significantly associated with improved 5-year CRC-specific survival, with a hazard ratio (HR) = 0. 62, (95% confidence interval; 0. 43, 0. 91), p=0. 01. Our new AI-driven deep learning model, which we call HopeSTIL, provides a highly efficient algorithm for analyzing digital pathology images of H Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (7Suppl): Abstract nr LB384.
Gruber et al. (Fri,) studied this question.