{"id":4963,"date":"2026-05-14T14:47:57","date_gmt":"2026-05-14T05:47:57","guid":{"rendered":"https:\/\/www.lunit.io\/en\/?post_type=publication&p=4963"},"modified":"2026-05-14T14:49:10","modified_gmt":"2026-05-14T05:49:10","slug":"artificial-intelligence-powered-he-based-quantification-of-spatial-tumor-infiltrating-lymphocyte-distribution-identifies-prognostic-immune-niches-in-colorectal-cancer","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/artificial-intelligence-powered-he-based-quantification-of-spatial-tumor-infiltrating-lymphocyte-distribution-identifies-prognostic-immune-niches-in-colorectal-cancer\/","title":{"rendered":"Artificial intelligence-powered H&E-based quantification of spatial tumor-infiltrating lymphocyte distribution identifies prognostic immune niches in colorectal cancer"},"content":{"rendered":"

Abstract<\/b><\/span><\/h2>\n

Purpose<\/h3>\n

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.<\/p>\n

Methods<\/h3>\n

A total of 202 patients with stage II\u2013III CRC were included. TIL densities in intratumoral (iTIL) and stromal (sTIL) regions were quantified using AI-based analysis of hematoxylin and eosin (H&E)\u2013stained images. Based on proximity to the tumor\u2013stromal border (TSB), TILs were subclassified into core iTIL, bounding iTIL, bounding sTIL, and outermost sTIL. Immunoscore was calculated from CD3+<\/sup>\u00a0and CD8+<\/sup>\u00a0T-cell densities in the tumor center and invasive margin.<\/p>\n

Results<\/h3>\n

Correlations between AI-based and pathologist assessments (iTIL: r\u2009=\u20090.57; sTIL: r\u2009=\u20090.70) were comparable to inter-pathologist correlations (iTIL: r\u2009=\u20090.47; sTIL: r\u2009=\u20090.70). 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<\/i>\u2009=\u20090.001), TSB score (p<\/i>\u2009<\u20090.001), and Immunoscore (p<\/i>\u2009<\u20090.001) independently predicted RFS. In microsatellite instability\u2013high tumors, only the TSB score remained prognostically significant.<\/p>\n

Conclusion<\/h3>\n

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.<\/p>\n

 <\/p>\n

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