REVIEW article
Front. Oncol.
Sec. Gastrointestinal Cancers: Gastric and Esophageal Cancers
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1630628
This article is part of the Research TopicReal-World Clinical and Translational Research in Gastrointestinal CancersView all 12 articles
Artificial Intelligence in Advanced Gastric Cancer: A Comprehensive Review of Applications in Precision Oncology
Provisionally accepted
- The First people's Hospital of Xiaoshan District, Hangzhou, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Gastric cancer (GC) remains a major global health challenge, particularly in its advanced stages where prognosis is poor, and treatment responses are heterogeneous. Precision oncology aims to tailor therapies, but current biomarkers have limitations. Artificial Intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers powerful tools to analyze complex, multi-dimensional data from advanced GC patients, including clinical records, genomics, imaging (radiomics), and digital pathology (pathomics). This review synthesizes the current state of AI applications in unresectable, advanced GC. AI models demonstrate significant potential in refining diagnosis and staging, predicting treatment efficacy for chemotherapy, immunotherapy, and targeted therapies, and assessing prognosis. Multi-modal AI approaches, integrating data from diverse sources, consistently show improved predictive performance over single-modality models, better reflecting the complexity of the disease. Key challenges remain, including data quality and standardization, model generalizability and interpretability, and the need for rigorous prospective validation. Future directions emphasize multi-center collaborations, development of robust and explainable AI (XAI), and seamless integration into clinical workflows. Overcoming these hurdles will be crucial to translate AI's potential into tangible clinical benefits, enabling truly personalized and effective management for patients with advanced gastric cancer.
Keywords: artificial intelligence, Advanced gastric cancer, precision oncology, Treatment response prediction, multi-modal data
Received: 18 May 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 Fu, Xu, Lv and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Baijun Jin, The First people's Hospital of Xiaoshan District, Hangzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.