ArticleInternational journal of molecular sciences2023
Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Significance of Tumor-Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors.Medicina (Kaunas, Lithuania) · 2023Pooled it
- An Open Source Automated Tumor Infiltrating Lymphocytes Algorithm for Prognosis in Primary Small Bowel Adenocarcinoma Using Routine Hematoxylin and Eosin Stained Sections.Cancer medicine · 2026Article
- Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Automated tumor stroma ratio assessment in colorectal cancer using hybrid deep learning approach.Scientific reports · 2025Article
- Quantification of the immunohistochemical staining of fibroblast activation protein in intrathoracic solitary fibrous tumors using QuPath.Surgery today · 2025Article
- Polychromatic Polarization Microscopy Differentiates Collagen Fiber Signatures in Benign Pancreatic Tissue and Pancreatic Ductal Adenocarcinoma.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Ensemble machine learning models for predicting bone metastasis in bladder cancer.Frontiers in oncology · 2025Article
- Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma.European radiology · 2024Article
- Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection.Biomedicines · 2024Article
- Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology.Frontiers in oncology · 2024Review
- Article
- Article
- Computational pathology in bladder cancer: A scoping review.Bladder cancer (Amsterdam, Netherlands)Review
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Authors and funding
14 authors.
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Abstract
Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR. We retrospectively analyzed data from two independent cohorts to verify the prognostic significance of ML-based TSR in MIBC patients. WSIs from 133 MIBC patients were used as the discovery set to identify the optimal association of TSR with patient survival outcomes. Furthermore, we performed validation in an independent external cohort consisting of 261 MIBC patients. We demonstrated a significant prognostic association of ML-based TSR with survival outcomes in MIBC patients (
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