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

基于人工智能的肿瘤-基质比率定量分析揭示了其在结直肠癌中的预后价值和基质驱动的免疫抑制:一项国际验证研究

阅读:1

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 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-026-07681-6.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。