Artificial intelligence-powered H&E-based quantification of spatial tumor-infiltrating lymphocyte distribution identifies prognostic immune niches in colorectal cancer
Clinical Snapshot
PICO Framework
| P — Population | 202 patients with stage II-III colorectal cancer |
| I — Intervention | AI-based spatial analysis of tumor-infiltrating lymphocytes (TILs) from H&E-stained tissue sections |
| C — Comparator | Conventional pathologist assessment and Immunoscore |
| O — Outcomes | Recurrence-free survival, prognostic performance of spatial TIL distribution patterns |
Bottom Line
This prognostic cohort study of 202 stage II-III colorectal cancer patients demonstrates that spatial analysis of tumor-infiltrating lymphocytes using standard H&E slides can predict recurrence-free survival. The novel tumor-stromal border (TSB) score showed independent prognostic value comparable to the established Immunoscore, particularly in microsatellite instability-high tumors. While the approach shows promise for improving risk stratification using routine pathology specimens, the study lacks external validation and has a relatively small sample size. The commercial involvement and single-center design limit immediate clinical adoption. Australian pathologists should await validation studies before incorporating this technology into routine practice, though the use of standard H&E staining makes it potentially feasible for implementation in existing laboratory workflows.
Key Findings
P Value: TIL score p=0.001, TSB score p<0.001, Immunoscore p<0.001
Effect Size: TIL score, TSB score, and Immunoscore all independently predictive
Primary Outcome: Recurrence-free survival
Nnt Or Sensitivity: AI-pathologist correlation: iTIL r=0.57, sTIL r=0.70
Confidence Interval: Not reported in abstract
Clinical Application
Requires H&E slides and specialized software, potentially implementable in pathology laboratories Could complement existing pathology workflows in Australian hospitals; may require TGA approval for diagnostic software; relevant for RACGP cancer surveillance guidelines Patients with stage II-III colorectal cancer undergoing surgical resection
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: 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.
References
- 1.Koh, H. H., Lee, S., Oum, C., Song, S., Cho, S. I., Pereira, S., Ahn, C. H., Kim, J. Y., Kim, M., & Jung, M. (2026). Artificial intelligence-powered H&E-based quantification of spatial tumor-infiltrating lymphocyte distribution identifies prognostic immune niches in colorectal cancer. Cancer Immunology, Immunotherapy. https://doi.org/10.3390/cancers12102902
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