ArticleEBioMedicine2020
Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.
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Who cites it
78 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A scoping review of TSR analysis in colorectal cancer: implications for automated solutions.Oncology reviews · 2025Pooled it
- Trial
- Impact of stromal maturity and proportion on prognosis and immune landscape in colorectal cancer.Annals of medicine · 2026Article
- AI-Guided Ferroptosis Biomarker Discovery and Routine Laboratory-Based Machine Learning for Predicting Nodal Metastasis in Colon Cancer.Metabolites · 2026Article
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Tumor stroma-immune interactions shape the immunosuppressive microenvironment and predict response to neoadjuvant chemoradiotherapy plus immunotherapy in rectal cancer.Journal for immunotherapy of cancer · 2026Observational
- Deep learning-based prediction of lymph node metastasis and occult tumor cells in gastric cancer using histopathological images: a retrospective study.British journal of cancer · 2026Article
- Added value of the tumor-stroma ratio to the PREDICT model for breast cancer.Breast (Edinburgh, Scotland) · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- 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 · 2026Article
- The Heterogeneity and Function of Stromal Cells in the Tumor Microenvironment.Research (Washington, D.C.) · 2026Review
- A Deep Learning-Generated Mixed Tumor-Stroma Ratio for Prognostic Stratification and Multi-omics Profiling in Bladder Cancer.Research (Washington, D.C.) · 2026Article
- Macrophage morphology in the tumor microenvironment predicts metachronous liver metastasis in gastric cancer: establishment and validation of a predictive model.Frontiers in immunology · 2026Article
- Disease-Free Survival of Patients with Stage II Stroma-Rich Colorectal Adenocarcinomas with Microsatellite Stability.International journal of molecular sciences · 2025Article
- Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study.Annals of medicine · 2025Article
- Automated tumor stroma ratio assessment in colorectal cancer using hybrid deep learning approach.Scientific reports · 2025Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Pathology-interpretable radiomic model for predicting clinical outcome in patients with osteosarcoma: a retrospective, multicenter study.BMC medicine · 2025Article
- AI-based tumor-stroma ratio quantification algorithm: comprehensive evaluation of prognostic role in primary colorectal cancer.Virchows Archiv : an international journal of pathology · 2025Article
18 more citing papers are in PubMed but not listed here.
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10 authors.
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Abstract
backgroundAn artificial intelligence method could accelerate the clinical implementation of tumour-stroma ratio (TSR), which has prognostic relevance in colorectal cancer (CRC). We, therefore, developed a deep learning model for the fully automated TSR quantification on routine haematoxylin and eosin (HE) stained whole-slide images (WSI) and further investigated its prognostic validity for patient stratification.
methodsWe trained a convolutional neural network (CNN) model using transfer learning, with its nine-class tissue classification performance evaluated in two independent test sets. Patch-level segmentation on WSI HE slides was performed using the model, with TSR subsequently derived. A discovery (N=499) and validation cohort (N=315) were used to evaluate the prognostic value of TSR for overall survival (OS).
findingsThe CNN-quantified TSR was a prognostic factor, independently of other clinicopathologic characteristics, with stroma-high associated with reduced OS in the discovery (HR 1.72, 95% CI 1.24-2.37, P=0.001) and validation cohort (2.08, 1.26-3.42, 0.004). Integrating TSR into a Cox model with other risk factors showed improved prognostic capability.
interpretationWe developed a deep learning model to quantify TSR based on histologic WSI of CRC and demonstrated its prognostic validity for patient stratification for OS in two independent CRC patient cohorts. This fully automatic approach allows for the objective and standardised application while reducing pathologists' workload. Thus, it can potentially be of significant aid in clinical prognosis prediction and decision-making.
fundingNational Key Research and Development Program of China, National Science Fund for Distinguished Young Scholar, and National Science Foundation for Young Scientists of China.
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