ArticleNPJ precision oncology2025
Deepath-MSI: a clinic-ready deep learning model for microsatellite instability detection in colorectal cancer using whole-slide imaging.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Review
- Artificial Intelligence-Based Prediction of Molecular Alterations in Colorectal Cancer Using Routine H&E Whole-Slide Images.International journal of molecular sciences · 2026Review
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Review
- Deepath-SCC: a deep learning model for accurate tissue origin identification in squamous cell carcinoma.NPJ precision oncology · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Lynch Syndrome as a Spectrum of Four Distinct Genetic Disorders: Toward Genotype-Guided Precision Management in the NGS Era.Cancers · 2026Review
- Immunotherapy for microsatellite-stable colorectal cancer: overcoming resistance and exploring novel therapeutic strategies.Annals of coloproctology · 2026Review
- Next-Generation Sequencing-Based Detection ofGenes · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Deficient mismatch repair/microsatellite instability-high colorectal cancer: current treatment paradigms, limitations and future perspectives.BMJ oncology · 2026Review
- Comprehensive machine learning analysis of a radiomics-based model for predicting microsatellite instability in right Colon Cancer.Frontiers in oncology · 2026Article
- Beyond prediction: AI as a mechanistic microscope and digital twin for colorectal cancer immunotherapy.Frontiers in immunology · 2026Review
- Impact of the COVID-19 Pandemic on Colorectal Cancer Surgery: Surgical Outcomes and Tumor Characteristics in a Multicenter Retrospective Cohort.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Microsatellite instability (MSI) is crucial for immunotherapy selection and Lynch syndrome diagnosis in colorectal cancer. Despite recent advances in deep learning algorithms using whole-slide images, achieving clinically acceptable specificity remains challenging. In this large-scale multicenter study, we developed Deepath-MSI, a feature-based multiple instances learning model specifically designed for sensitive and specific MSI prediction, using 5070 whole-slide images from seven diverse cohorts. Deepath-MSI achieved an AUROC of 0.98 in the test set. At a predetermined sensitivity threshold of 95%, the model demonstrated 92% specificity and 92% overall accuracy. In a real-world validation cohort, performance remained consistent with 95% sensitivity and 91% specificity. Deepath-MSI could transform clinical practice by serving as an effective pre-screening tool, substantially reducing the need for costly and labor-intensive molecular testing while maintaining high sensitivity for detecting MSI-positive cases. Implementation could streamline diagnostic workflows, reduce healthcare costs, and improve treatment decision timelines.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.