Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study.
[BACKGROUND] Colorectal cancer (CRC) exhibits high heterogeneity, affecting variable outcomes and response to therapy.
- 95% CI 1.61–3.70
APA
Ye H, Zhao K, et al. (2026). Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study.. Journal of translational medicine, 24(1), 269. https://doi.org/10.1186/s12967-026-07681-6
MLA
Ye H, et al.. "Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study.." Journal of translational medicine, vol. 24, no. 1, 2026, pp. 269.
PMID
41580788
Abstract
[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 = 0.93, 95% confidence intervals (CI) 0.90–0.94) and with standardized pathologist assessments ( < 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, < 0.001) in Cohort 1, 3.29 (2.29–4.72, < 0.001) in Cohort 2, and 2.98 (2.07–4.28, < 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.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12967-026-07681-6.
[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 = 0.93, 95% confidence intervals (CI) 0.90–0.94) and with standardized pathologist assessments ( < 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, < 0.001) in Cohort 1, 3.29 (2.29–4.72, < 0.001) in Cohort 2, and 2.98 (2.07–4.28, < 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.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12967-026-07681-6.
같은 제1저자의 인용 많은 논문 (5)
- Clinical Efficacy of 830 nm LED Photobiomodulation Therapy on Postoperative Blepharoplasty Complications.
- Construction and validation of a predictive model for hypothermia complication during endoscopic thyroidectomy for thyroid cancer.
- Structural and functional insights into targeting hTERT G-quadruplex by levo-Tetrahydropalmatine in the non-small cell lung cancer.
- Bidirectional Mendelian Randomization and Multi-Omics Uncover Causal Serum Metabolites and Neuro-Related Mechanistic Pathways in Acute Myeloid Leukemia.
- Sintilimab combined with AVD for the treatment of composite Hodgkin lymphoma and follicular lymphoma: a case report and literature review.