What question did this study set out to answer?

This research aims to explore the prognostic implications of the spatial distribution of tumor-infiltrating lymphocytes in colorectal cancer using an AI-based method.

May 7, 2026Cancer Immunology Immunotherapy4 citationsOpen Access

Artificial intelligence-powered H&E-based quantification of spatial tumor-infiltrating lymphocyte distribution identifies prognostic immune niches in colorectal cancer

HKHyun-Hee KohYonsei University SLSeungeun Lee COChiyoon Oum

Key Points

This research aims to explore the prognostic implications of the spatial distribution of tumor-infiltrating lymphocytes in colorectal cancer using an AI-based method.
Utilized artificial intelligence to analyze T-cell densities in tumor center and invasive margin.
Assessed correlation between AI-based and pathologist TIL evaluations with inter-pathologist correlations.
Conducted univariate and multivariable Cox regression analyses for risk stratification.
In univariate analysis, bounding iTIL and bounding sTIL significantly related to recurrence-free survival (p < 0.001).
In multivariable analysis, TIL score (p = 0.001) and TSB score (p < 0.001) were independent predictors of recurrence-free survival.
TSB score remained prognostically significant in microsatellite instability-high tumors.

Abstract

PURPOSE: The prognostic significance of tumor-infiltrating lymphocytes (TILs) in colorectal cancer (CRC) is well established; however, existing approaches inadequately capture their spatial distribution. We investigated the prognostic implications of TIL spatial distribution in CRC using an artificial intelligence (AI)-based method. METHODS: T-cell densities in the tumor center and invasive margin. RESULTS: Correlations between AI-based and pathologist assessments (iTIL: r = 0.57; sTIL: r = 0.70) were comparable to inter-pathologist correlations (iTIL: r = 0.47; sTIL: r = 0.70). In univariate Cox regression analysis, bounding iTIL, bounding sTIL, and outermost sTIL were significantly associated with recurrence-free survival (RFS), whereas core iTIL was not. Composite TIL and TSB scores were developed by incorporating the prognostically significant regions. In multivariable analysis, the TIL score (p = 0.001), TSB score (p < 0.001), and Immunoscore (p < 0.001) independently predicted RFS. In microsatellite instability-high tumors, only the TSB score remained prognostically significant. CONCLUSION: AI-powered spatial analysis of TILs, particularly the TSB score, demonstrated prognostic performance comparable to conventional Immunoscore, thereby supporting the value of spatial immune profiling and AI-driven analysis of H&E-stained slides for improved risk stratification in CRC.

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