ArticleNature communications2024
Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic value of lymph node ratio after neoadjuvant chemotherapy for gastric cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke.Annals of medicine · 2026Review
- Article
- Federated generative prompt learning with vision foundation models: universal efficient multi-center medical image analysis.NPJ digital medicine · 2026Article
- Global trends in artificial intelligence applications for gastric cancer prediction, treatment, and management: a topic modeling and bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.World journal of gastrointestinal oncology · 2026Review
- Multimodal Fusion of 3D CT and Pathological Images for Gastric Cancer Recurrence Prediction.Cancer management and research · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Artificial intelligence-driven gastrointestinal functional assessment: multimodal imaging, digital biomarkers, and real-time monitoring.Frontiers in physiology · 2026Review
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Review
- Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges.World journal of gastroenterology · 2025Review
- Chemical knowledge-informed framework for privacy-aware retrosynthesis learning.Nature communications · 2025Article
- Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- FedOcw: optimized federated learning for cross-lingual speech-based Parkinson's disease detection.NPJ digital medicine · 2025Article
- Federated Learning for Predicting Major Postoperative Complications.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the receiver operating characteristic curve (AUC) values of 0.710, 0.798, 0.809, and 0.869 across four data centres. Additionally, the effectiveness of the algorithm is evaluated, and both adaptive and common features are identified through analysis.
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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.