ArticleNPJ precision oncology2024
Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- Review
- The Genetic Landscape of Colorectal Cancer: From Molecular Alterations to Therapeutic Decision Pathways.Cancers · 2026Review
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Mutation enrichment in targeted panels flags immunotherapy-responsive POLE-driven hypermutated microsatellite-stable colorectal cancers.NPJ precision oncology · 2026Article
- The two souls of pathology-ready for biomarker complexity.Virchows Archiv : an international journal of pathology · 2026Article
- Review
- Emerging role of DNA polymerase epsilon non-exonuclease domain mutations in colorectal cancer: From sequence variants to clinical implications.World journal of gastroenterology · 2026Article
- Case Report: Cerebral metastasis following standard therapy in BRAF/KRAS wild-type colorectal cancer: an unusual presentation of disease progression.Frontiers in oncology · 2026Article
- ADPv2: A hierarchical histological tissue type-annotated dataset for potential biomarker discovery of colorectal disease.Journal of pathology informatics · 2026Article
- H&E-based MSI/MMR testing with AI in colorectal cancer: a multi-centred blinded evaluation.NPJ digital medicine · 2025Article
- Deep multimodal fusion of patho-radiomic and clinical data for enhanced survival prediction for colorectal cancer patients.NPJ digital medicine · 2025Article
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- Consensus Molecular Subtypes (CMS) Classification: a progress towards Subtype-Driven treatments in colorectal cancer.World journal of surgical oncology · 2025Review
- HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer.Nature communications · 2025Article
- Synergistic H&E and IHC image analysis by AI predicts cancer biomarkers and survival outcomes in colorectal and breast cancer.Communications medicine · 2025Article
- Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images.NPJ digital medicine · 2025Article
- Epidemiology, pathogenesis, biology and evolving management of MSI-H/dMMR cancers.Nature reviews. Clinical oncology · 2025Review
- Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology.NPJ digital medicine · 2025Article
- Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer.Journal of personalized medicine · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as "positive" by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.
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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.