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Purpose Tumor-infiltrating lymphocytes (TILs) have significant prognosticvalues in cancers. However, very few automated, deep-learning-based TIL scoringalgorithms have been developed for colorectal cancers (CRC). Methods Wedeveloped an automated, multiscale LinkNet workflow for quantifyingcellular-level TILs for CRC tumors using H&E-stained images. The predictiveperformance of the automatic TIL scores (TIL) for disease progression andoverall survival was evaluate using two international datasets, including 554CRC patients from The Cancer Genome Atlas (TCGA) and 1130 CRC patients fromMolecular and Cellular Oncology (MCO). Results The LinkNet model provided anoutstanding precision (0.9508), recall (0.9185), and overall F1 score (0.9347).Clear dose-response relationships were observed between TILs and risk ofdisease progression or death decreased in both TCGA and MCO cohorts. Bothunivariate and multivariate Cox regression analyses for the TCGA datademonstrated that patients with high TILs had significant (approx. 75%)reduction of risk for disease progression. In both MCO and TCGA studies, theTIL-high group was significantly associated with improved overall survival inunivariate analysis (30% and 54% reduction in risk, respectively). However,potential confounding was observed in the MCO dataset. The favorable effects ofhigh TILs were consistently observed in different subgroups according to knowrisk factors. Conclusion A deep-learning workflow for automatic TILquantification based on LinkNet was successfully developed.

Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers