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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Gastrointestinal Cancers: Gastric and Esophageal Cancers
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1420517

Current Status and Trends of Machine Learning Applied in Clinical Research of Gastric Cancer from 2004 to 2023: Global Bibliometric and Visual Analysis

Provisionally accepted
Xinyi Wang Xinyi Wang 1Wu Chao Wu Chao 1Siqing Yue Siqing Yue 2Mengyuan Zhou Mengyuan Zhou 1Enba Zhuo Enba Zhuo 1Xin Wu Xin Wu 1Yafen Wang Yafen Wang 1Bangjie Chen Bangjie Chen 1*Fan Wang Fan Wang 1*
  • 1 First Affiliated Hospital of Anhui Medical University, Hefei, China
  • 2 Anhui Medical University, Hefei, Anhui Province, China

The final, formatted version of the article will be published soon.

    Background: Gastric cancer is a serious disease that threatens human life; early diagnosis and treatment have been the focus of many studies. With advancements in imaging evaluation and machine learning, early detection and treatment of gastric cancer have become feasible. This study aimed to explore research trends and hotspots in the field of gastric cancer and machine learning through bibliometric analysis and to provide new insights for related clinical applications. Methods: Literature on gastric cancer and machine learning published from 2004 to 2023 was retrieved from the Web of Science database. Microsoft Excel 2019 was used for statistical analysis of influential articles, journals, authors, organizations, countries (regions), and co-citation references in this research domain. VOSviewer (version 1.6.16) and CiteSpace (version 5.8.R3) were utilized to visualize the corresponding data. Results: We analyzed and evaluated 425 articles authored by 2,899 researchers from 825 organizations across 52 countries (regions). The People’s Republic of China, the Chinese Academy of Sciences, and the University of the Chinese Academy of Sciences were identified as leaders in this field. The article "Genome-wide cell-free DNA fragmentation in patients with cancer," published in Nature, was the most frequently cited work. The diagnosis and treatment of gastric cancer have consistently been research hotspots, with a shift in focus from laboratory-based studies to clinical applications. This trend highlights the transition from etiology-oriented research to studies emphasizing treatment and practical applications. Conclusions: This study offers a comprehensive visual analysis of research on gastric cancer and machine learning, representing the most detailed bibliometric study in this domain. With the continuous advancement of research, artificial intelligence-assisted early diagnostic methods for gastric cancer and corresponding treatment strategies may emerge as a pivotal direction for future research in this area.

    Keywords: Bibliometrics, gastric cancer, machine learning, Global trends, diagnosis, Treatment

    Received: 20 Apr 2024; Accepted: 10 Jan 2025.

    Copyright: © 2025 Wang, Chao, Yue, Zhou, Zhuo, Wu, Wang, Chen and Wang. 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:
    Bangjie Chen, First Affiliated Hospital of Anhui Medical University, Hefei, China
    Fan Wang, First Affiliated Hospital of Anhui Medical University, Hefei, 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.