ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2023
Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning.
Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05698056 (A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence), which is not on this map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Swarm Learning for the Detection of Middle Cerebral Artery Occlusion On CT Angiography.Clinical neuroradiology · 2026Article
- SwarmMAP: swarm learning for decentralized cell type annotation in single cell sequencing data.NPJ systems biology and applications · 2026Article
- A multi-task deep learning framework for simultaneous prediction of microsatellite instability and tumor mutational burden in gastric cancer from histopathological images.Frontiers in oncology · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- 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
- Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices.Journal of imaging informatics in medicine · 2025Article
- The current landscape of artificial intelligence in computational histopathology for cancer diagnosis.Discover oncology · 2025Review
- Advances and challenges in gastric cancer testing: the role of biomarkers.Cancer biology & medicine · 2025Review
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging.Communications medicine · 2025Article
- SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data.bioRxiv : the preprint server for biology · 2025Article
- From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.Nature protocols · 2025Article
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Targeting amino acid metabolism to inhibit gastric cancer progression and promote anti-tumor immunity: a review.Frontiers in immunology · 2025Review
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
- Predicting treatment response in multicenter non-small cell lung cancer patients based on federated learning.BMC cancer · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL).
methodsHere, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer.
resultsOn an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance.
conclusionsOur findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability.
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