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Purpose <p>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> Methods <p>A total of 202 patients with stage II–III CRC were included. TIL densities in intratumoral (iTIL) and stromal (sTIL) regions were quantified using AI-based analysis of hematoxylin and eosin (H&amp;E)–stained images. Based on proximity to the tumor–stromal border (TSB), TILs were subclassified into core iTIL, bounding iTIL, bounding sTIL, and outermost sTIL. Immunoscore was calculated from CD3<sup>+</sup> and CD8<sup>+</sup> T-cell densities in the tumor center and invasive margin.</p> Results <p>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 (<i>p</i> = 0.001), TSB score (<i>p</i> &lt; 0.001), and Immunoscore (<i>p</i> &lt; 0.001) independently predicted RFS. In microsatellite instability–high tumors, only the TSB score remained prognostically significant.</p> Conclusion <p>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&amp;E-stained slides for improved risk stratification in CRC.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Hyun-Hee Koh,
  • Seungeun Lee,
  • Chiyoon Oum,
  • Sanghoon Song,
  • Soo Ick Cho,
  • Sérgio Pereira,
  • Chang Ho Ahn,
  • Jun Yong Kim,
  • Milim Kim,
  • Minsun Jung

摘要

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

A total of 202 patients with stage II–III CRC were included. TIL densities in intratumoral (iTIL) and stromal (sTIL) regions were quantified using AI-based analysis of hematoxylin and eosin (H&E)–stained images. Based on proximity to the tumor–stromal border (TSB), TILs were subclassified into core iTIL, bounding iTIL, bounding sTIL, and outermost sTIL. Immunoscore was calculated from CD3+ and CD8+ 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.