Zhang MN, Xue MJ, Zhou BZ, Xu J, Sun HK, Wang JH, Wang YY. Comprehensive review of Bayesian network applications in gastrointestinal cancers. World J Clin Oncol 2025; 16(6): 104299 [DOI: 10.5306/wjco.v16.i6.104299]
Corresponding Author of This Article
Ji-Han Wang, PhD, Assistant Professor, School of Medicine, Xi'an International University, No. 18 Yudou Road, Yanta District, Xi'an 710077, Shaanxi Province, China. 513837742@qq.com
Research Domain of This Article
Oncology
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Min-Na Zhang, Meng-Ju Xue, Bao-Zhen Zhou, Jing Xu, Hong-Kai Sun, Ji-Han Wang, School of Medicine, Xi'an International University, Xi'an 710077, Shaanxi Province, China
Yang-Yang Wang, School of Physics and Electronic Information, Yan’an University, Yan’an 716000, Shaanxi Province, China
Co-first authors: Min-Na Zhang and Meng-Ju Xue.
Co-corresponding authors: Ji-Han Wang and Yang-Yang Wang.
Author contributions: Zhang MN, and Wang YY contributed to conceptualization; Xue MJ and Zhou BZ; validation, Xu J, Wang JH and Wang YY contributed to methodology; Zhang MN, Xue MJ and Sun HK contributed to writing-original draft preparation; Wang JH and Wang YY contributed to writing-review and editing; All the authors have read and approved the final manuscript. Zhang MN proposed and designed the study. Xue MJ prepared the first draft of the manuscript. Both authors have made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper. Both Wang JH and Wang YY have played important and indispensable roles in the study design, data interpretation and manuscript preparation as the co-corresponding authors.
Supported by Open Funds for Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, No. 2023-KFMS-1.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ji-Han Wang, PhD, Assistant Professor, School of Medicine, Xi'an International University, No. 18 Yudou Road, Yanta District, Xi'an 710077, Shaanxi Province, China. 513837742@qq.com
Received: December 16, 2024 Revised: April 11, 2025 Accepted: May 21, 2025 Published online: June 24, 2025 Processing time: 185 Days and 14.4 Hours
Abstract
Gastrointestinal cancers, including esophageal, gastric, colorectal, liver, gallbladder, cholangiocarcinoma, and pancreatic cancers, pose a significant global health challenge due to their high mortality rates and poor prognosis, particularly when diagnosed at advanced stages. These malignancies, characterized by diverse clinical presentations and etiologies, require innovative approaches for improved management. Bayesian networks (BN) have emerged as a powerful tool in this field, offering the ability to manage uncertainty, integrate heterogeneous data sources, and support clinical decision-making. This review explores the application of BN in addressing critical challenges in gastrointestinal cancers, including the identification of risk factors, early detection, treatment optimization, and prognosis prediction. By integrating genetic predispositions, lifestyle factors, and clinical data, BN hold the potential to enhance survival rates and improve quality of life through personalized treatment strategies. Despite their promise, the widespread adoption of BN is hindered by challenges such as data quality limitations, computational complexities, and the need for greater clinical acceptance. The review concludes with future research directions, emphasizing the development of advanced BN algorithms, the integration of multi-omics data, and strategies to ensure clinical applicability, aiming to fully realize the potential of BN in personalized medicine for gastrointestinal cancers.
Core Tip: Bayesian networks (BN) provide a powerful framework for managing the complexity and uncertainty in gastrointestinal cancer research and clinical practice. By integrating diverse data types and modeling causal relationships, BN facilitate personalized treatment strategies. However, challenges such as data availability, computational demands, and clinical adoption must be addressed to fully unlock their potential for improving patient outcomes and quality of life.
