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Background <p>Colorectal cancer (CRC) exhibits high heterogeneity, affecting variable outcomes and response to therapy. Tumor stroma drives progression and immunosuppression. Although tumor–stroma ratio (TSR) is a validated prognostic marker, TSR remains subjective and poorly reproducible. Artificial intelligence (AI) enables standardized TSR quantification on hematoxylin and eosin (HE) whole-slide images (WSI), supporting clinical integration and personalized therapy.</p> Methods <p>A total of 3411 CRC patients (Cohorts 1–3) were included for survival analysis. HE-stained WSIs were processed using tumor detection and tissue segmentation models to automatically calculate TSR-AI, classified as low, intermediate, or high. Prognostic value for overall survival (OS) and disease-free survival (DFS) was assessed, along with correlations to immune infiltration. Stromal-immune interactions were further validated using spatial transcriptomics data from publicly available CRC samples profiled with Visium HD platform.</p> Results <p>TSR-AI strongly correlated with reference TSR from CK-stained WSIs (Pearson’s <i>r</i> = 0.93, 95% confidence intervals (CI) 0.90–0.94) and with standardized pathologist assessments (<i>p</i> &lt; 0.05). Patients with TSR-AI-low had significantly prolonged OS compared with TSR-AI-high, with unadjusted hazard ratios of 2.44 (95% CI 1.61–3.70, <i>p</i> &lt; 0.001) in Cohort 1, 3.29 (2.29–4.72, <i>p</i> &lt; 0.001) in Cohort 2, and 2.98 (2.07–4.28, <i>p</i> &lt; 0.001) in Cohort 3; similar trends were observed for DFS. TSR-AI-high was associated with reduced immune cell infiltration. Spatial transcriptomics further revealed stromal-immune interactions, with stroma-high tumors showing elevated cancer-associated fibroblast signatures and enrichment of profibrotic transforming growth factor-β signaling.</p> Conclusion <p>TSR-AI enables automated, objective, reproducible, and whole-slide quantification of TSR from routine HE-stained WSIs. TSR-AI provides robust prognostic information beyond TNM staging and may inform decisions on postoperative adjuvant therapy. Large-cohort analysis further confirms stroma as a key driver of an immunosuppressive tumor microenvironment in CRC.</p> Clinical trial number <p>Not applicable.</p>

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Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Huifen Ye,
  • Ke Zhao,
  • Yanfen Cui,
  • Zhenhui Li,
  • Huan Zhang,
  • Min-Er Zhong,
  • Chuanwen Fan,
  • Haitao Huang,
  • Nicholas J. Hawkins,
  • Robyn L. Ward,
  • Xiao-Feng Sun,
  • Jinming Song,
  • Zaiyi Liu,
  • Jitendra Jonnagaddala,
  • Tong Tong,
  • Su Yao

摘要

Background

Colorectal cancer (CRC) exhibits high heterogeneity, affecting variable outcomes and response to therapy. Tumor stroma drives progression and immunosuppression. Although tumor–stroma ratio (TSR) is a validated prognostic marker, TSR remains subjective and poorly reproducible. Artificial intelligence (AI) enables standardized TSR quantification on hematoxylin and eosin (HE) whole-slide images (WSI), supporting clinical integration and personalized therapy.

Methods

A total of 3411 CRC patients (Cohorts 1–3) were included for survival analysis. HE-stained WSIs were processed using tumor detection and tissue segmentation models to automatically calculate TSR-AI, classified as low, intermediate, or high. Prognostic value for overall survival (OS) and disease-free survival (DFS) was assessed, along with correlations to immune infiltration. Stromal-immune interactions were further validated using spatial transcriptomics data from publicly available CRC samples profiled with Visium HD platform.

Results

TSR-AI strongly correlated with reference TSR from CK-stained WSIs (Pearson’s r = 0.93, 95% confidence intervals (CI) 0.90–0.94) and with standardized pathologist assessments (p < 0.05). Patients with TSR-AI-low had significantly prolonged OS compared with TSR-AI-high, with unadjusted hazard ratios of 2.44 (95% CI 1.61–3.70, p < 0.001) in Cohort 1, 3.29 (2.29–4.72, p < 0.001) in Cohort 2, and 2.98 (2.07–4.28, p < 0.001) in Cohort 3; similar trends were observed for DFS. TSR-AI-high was associated with reduced immune cell infiltration. Spatial transcriptomics further revealed stromal-immune interactions, with stroma-high tumors showing elevated cancer-associated fibroblast signatures and enrichment of profibrotic transforming growth factor-β signaling.

Conclusion

TSR-AI enables automated, objective, reproducible, and whole-slide quantification of TSR from routine HE-stained WSIs. TSR-AI provides robust prognostic information beyond TNM staging and may inform decisions on postoperative adjuvant therapy. Large-cohort analysis further confirms stroma as a key driver of an immunosuppressive tumor microenvironment in CRC.

Clinical trial number

Not applicable.