ArticleJournal of translational medicine2026
Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Deep learning based on CD3 histological slides for prediction of colon cancer outcome: analysis of three international stage III colon cancer cohorts.Oncoimmunology · 2026Article
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
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Abstract
backgroundColorectal 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.
methodsA 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.
resultsTSR-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.
conclusionTSR-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.
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