Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning.

ABSTRACT: The density of tumor-infiltrating lymphocytes (TILs) serves as a valuable indicator for predicting anti-tumor responses, but its broad impact across various types of cancers remains underexplored. We introduce TILScout, a pan-cancer deep-learning approach to compute patch-level TIL scores from whole slide images (WSIs). TILScout achieved accuracies of 0.9787 and 0.9628, and AUCs of 0.9988 and 0.9934 in classifying WSI patches into three categories-TIL-positive, TIL-negative, and other/necrotic-on validation and independent test sets, respectively, surpassing previous studies. The biological significance of TILScout-derived TIL scores across 28 cancers was validated through comprehensive functional and correlational analyses. A consistent decrease in TIL scores with an increase in cancer st

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11923303 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature