ArticleThe Lancet. Digital health2021
Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study.
Article in The Lancet. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 71 papers, 1 of them a synthesis that pooled it.
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it, 117 citations in OpenAlex.
- Deep learning prediction of MSI/dMMR, EBV, HER2, PD-L1, and CLDN18.2 status directly from hematoxylin and eosin-stained slides in gastric and gastroesophageal junction adenocarcinoma: a systematic review.Frontiers in oncology · 2026Pooled it
- Molecular classification and precision therapy in gastric cancer: Current advances and future perspectives (Review).Oncology reports · 2026Review
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- Benchmarking foundation models as feature extractors for weakly supervised computational pathology.Nature biomedical engineering · 2026Article
- A deep learning-based digital biopsy for predicting early recurrence in gastric cancer.Nature communications · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Review
- World Endoscopy Organization Position Statements for Artificial Intelligence in Endoscopic Diagnosis of Gastric Epithelial Neoplasia.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Integrative Genomic and AI Approaches to Lung Cancer and Implications for Disease Prevention in Former Smokers.International journal of molecular sciences · 2026Review
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- Deep learning-based radiomics and pathomics for decoding tumor microenvironment and predicting immunotherapy outcomes in gastric cancer.Frontiers in immunology · 2026Review
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- Explainable ensemble learning for Epstein-Barr virus risk prediction in ulcerative colitis and Crohn's disease using routine biomarkers.Scientific reports · 2025Article
- A robust artificial intelligence system for predicting EBV status in gastric cancer biopsy and resection specimens.Scientific reports · 2025Article
- Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review.BMC cancer · 2025Article
- Advances in Epstein-Barr Virus Detection: From Traditional Methods to Modern Technologies.Viruses · 2025Review
- Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.BMC gastroenterology · 2025Article
- MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images.Cell reports. Medicine · 2025Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
35 authors at 20 institutions in 9 countries.
Funding
Abstract
backgroundResponse to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning-based classifiers to detect microsatellite instability and EBV status from routine histology slides.
methodsIn this retrospective, multicentre study, we collected tissue samples from ten cohorts of patients with gastric cancer from seven countries (South Korea, Switzerland, Japan, Italy, Germany, the UK and the USA). We trained a deep learning-based classifier to detect microsatellite instability and EBV positivity from digitised, haematoxylin and eosin stained resection slides without annotating tumour containing regions. The performance of the classifier was assessed by within-cohort cross-validation in all ten cohorts and by external validation, for which we split the cohorts into a five-cohort training dataset and a five-cohort test dataset. We measured the area under the receiver operating curve (AUROC) for detection of microsatellite instability and EBV status. Microsatellite instability and EBV status were determined to be detectable if the lower bound of the 95% CI for the AUROC was above 0·5.
findingsAcross the ten cohorts, our analysis included 2823 patients with known microsatellite instability status and 2685 patients with known EBV status. In the within-cohort cross-validation, the deep learning-based classifier could detect microsatellite instability status in nine of ten cohorts, with AUROCs ranging from 0·597 (95% CI 0·522-0·737) to 0·836 (0·795-0·880) and EBV status in five of eight cohorts, with AUROCs ranging from 0·819 (0·752-0·841) to 0·897 (0·513-0·966). Training a classifier on the pooled training dataset and testing it on the five remaining cohorts resulted in high classification performance with AUROCs ranging from 0·723 (95% CI 0·676-0·794) to 0·863 (0·747-0·969) for detection of microsatellite instability and from 0·672 (0·403-0·989) to 0·859 (0·823-0·919) for detection of EBV status.
interpretationClassifiers became increasingly robust when trained on pooled cohorts. After prospective validation, this deep learning-based tissue classification system could be used as an inexpensive predictive biomarker for immunotherapy in gastric cancer.
fundingGerman Cancer Aid and German Federal Ministry of Health.
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