Lu T, Lu M, Wu D, Ding YY, Liu HN, Li TT, Song DQ. Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study. World J Gastrointest Surg 2024; 16(1): 85-94 [PMID: 38328326 DOI: 10.4240/wjgs.v16.i1.85]
Corresponding Author of This Article
Da-Qing Song, Doctor, Additional Professor, Department of Emergency Medicine, Jining No. 1 People’s Hospital, No. 6 Jiankang Road, Rencheng District, Jining 272000, Shandong Province, China. 19552153365@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
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/
Tong Lu, Dong Wu, Tao-Tao Li, Da-Qing Song, Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
Miao Lu, Wuxi Mental Health Center, Wuxi 214000, Jiangsu Province, China
Yuan-Yuan Ding, Department of Gastroenterology, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
Hao-Nan Liu, Department of Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, Jiangsu Province, China
Co-first authors: Tong Lu and Miao Lu.
Author contributions: Lu T and Wu D designed the study and wrote the manuscript; Li TT and Ding YY analyzed the data; Liu HN collected the data; Lu M and Song DQ revised the manuscript; Lu T and Lu M designed the study and wrote the article; Wu D and Ding YY analyzed the data; Li T and Song DQ revised the article; Lu M is one of the co-first authors of this paper. Lu T and Lu M contributed equally to this work as co-first authors. The research was performed as a collaborative effort, and the designation of co-first authors authorship accurately reflects the distribution of responsibilities and burdens associated with the time and effort required to complete the study and the resultant paper. This also ensures effective communication and management of post-submission matters, ultimately enhancing the paper's quality and reliability. Lu T and Lu M contributed efforts of equal substance throughout the research process. The choice of these researchers as co-first authors acknowledges and respects this equal contribution, while recognizing the spirit of teamwork and collaboration of this study.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University and Jining First People’s Hospital.
Informed consent statement: All study participants or their legal guardian provided informed written consent about personal and medical data collection prior to study enrolment.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
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: Da-Qing Song, Doctor, Additional Professor, Department of Emergency Medicine, Jining No. 1 People’s Hospital, No. 6 Jiankang Road, Rencheng District, Jining 272000, Shandong Province, China. 19552153365@163.com
Received: September 14, 2023 Peer-review started: September 14, 2023 First decision: November 17, 2023 Revised: November 24, 2023 Accepted: December 21, 2023 Article in press: December 21, 2023 Published online: January 27, 2024
ARTICLE HIGHLIGHTS
Research background
Gastric cancer is one of the most common malignant tumors of the digestive system, ranking sixth in incidence and fourth in mortality worldwide. Machine learning (ML) represents an evolving frontier in the field of medicine, drawing substantial resources to connect computer science and statistical analysis with medical challenges. ML has the capacity to effectively handle extensive, diverse, and intricate medical data. Consequently, the implementation of ML techniques in medicine is widely regarded as the cornerstone of future endeavors in biomedical research, personalized medicine, and computer-aided diagnosis.
Research motivation
Using machine learning-based models to predict lymph node metastasis of gastric cancer is helpful to individualized diagnosis and treatment of gastric cancer patients.
Research objectives
Based on the clinicopathological data of 492 gastric cancer patients in two centers, we used ML algorithms to establish clinical models and conduct cross-validation, and finally compared the area under the receiver operating characteristic curve to draw conclusions. In addition to support vector machine, other ML models have good accuracy and reliability, and have better predictive value for gastric cancer lymph node metastasis. Among them, gradient boosting machine (GBM) has the best performance and the highest predictive value and accuracy. Through this study, ML can dig out the ability of clinical data to reflect disease, which can help clinicians evaluate patients' conditions and make better treatment decisions.
Research methods
Seven machine algorithm models were built with data from two centers, and then their performance was evaluated. Based on GBM model, a web-based online estimator and Shapley Additive Explanations summary plot were established.
Research results
ML can tap into the ability of clinical data to reflect disease, which can help clinicians assess patients' conditions and make better treatment decisions.
Research conclusions
ML algorithms have been used to establish an optimal prediction model for lymph node metastasis in gastric cancer, which is helpful for clinical risk stratification and individualized diagnosis and treatment of gastric cancer patients.
Research perspectives
In the future, multi-center data are needed to verify the external applicability of our model.
