Wang C, Chen MY, Wang YG, Shi M. Integrating tumor location into artificial intelligence-based prognostic models in cancer. World J Clin Oncol 2025; 16(8): 109934 [DOI: 10.5306/wjco.v16.i8.109934]
Corresponding Author of This Article
Min Shi, MD, Chief Physician, Professor, Department of Gastroenterology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai 200336, China. sm1790@shtrhospital.com
Research Domain of This Article
Multidisciplinary Sciences
Article-Type of This Article
Letter to the Editor
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/
World J Clin Oncol. Aug 24, 2025; 16(8): 109934 Published online Aug 24, 2025. doi: 10.5306/wjco.v16.i8.109934
Integrating tumor location into artificial intelligence-based prognostic models in cancer
Chen Wang, Meng-Yan Chen, Yu-Gang Wang, Min Shi
Chen Wang, Meng-Yan Chen, Yu-Gang Wang, Min Shi, Department of Gastroenterology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China
Co-first authors: Chen Wang and Meng-Yan Chen.
Co-corresponding authors: Yu-Gang Wang and Min Shi.
Author contributions: Wang C and Chen MY were the primary contributors to the manuscript writing as the co-first authors of the paper; Shi M and Wang YG conceptualized the theme and structure of this letter as the co-corresponding authors; all authors have read and approved the final manuscript.
Supported by Natural Science Foundation of the Science and Technology Commission of Shanghai Municipality, No. 23ZR1458300; Key Discipline Project of Shanghai Municipal Health System, No. 2024ZDXK0004; Doctoral Innovation Talent Base Project for Diagnosis and Treatment of Chronic Liver Diseases, No. RCJD2021B02; and Pujiang Project of Shanghai Magnolia Talent Plan, No. 24PJD098.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
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: Min Shi, MD, Chief Physician, Professor, Department of Gastroenterology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai 200336, China. sm1790@shtrhospital.com
Received: May 27, 2025 Revised: June 20, 2025 Accepted: July 17, 2025 Published online: August 24, 2025 Processing time: 86 Days and 17.8 Hours
Abstract
This letter is a commentary on the findings of Huang et al, who emphasize the prognostic value of tumor location in gastric cancer. Analyzing data from 3287 patients using Kaplan-Meier and multivariate Cox models, the authors found that the tumor location correlated with patient prognosis following surgery. Patients with tumors situated nearer to the stomach’s proximal end were associated with shorter survival periods and poorer outcomes. Notably, gender-based differences in tumor markers, particularly carbohydrate antigen 72-4, further highlight the need for sex-specific influence on the tumor location. Despite increasing recognition of tumor location as a prognostic factor, its role remains unclear in clinical prediction models for various cancers. This letter highlights the potential of incorporating tumor location into artificial intelligence -based prognostic tools to enhance prognostic models. It also outlines a stepwise framework for developing these models, from retrospective training to prospective multicenter validation and clinical implementation. In addition, it addresses the technical, ethical, and interoperability challenges critical to successful real-world prognosis.
Core Tip: This study highlights the prognostic significance of tumor location in gastric cancer, showing that proximal tumors are associated with worse survival outcomes. Gender differences, particularly in carbohydrate antigen 72-4 expression, further influence prognosis. The letter proposes integrating tumor location into artificial intelligence-based clinical prediction models to improve prognostic accuracy. It outlines a stepwise framework for model development, multicenter validation, and clinical implementation, while addressing critical technical, ethical, and interoperability challenges for real-world application.
