Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Jan 27, 2024; 16(1): 85-94
Published online Jan 27, 2024. doi: 10.4240/wjgs.v16.i1.85
Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study
Tong Lu, Miao Lu, Dong Wu, Yuan-Yuan Ding, Hao-Nan Liu, Tao-Tao Li, Da-Qing Song
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
Core Tip

Core Tip: The purpose of this study was to explore the performance of machine learning based models for the risk assessment of lymph node metastasis in patients with gastric cancer. We used seven different methods to analyze our data. After training, the algorithm with the highest average area under the receiver operating characteristic curve was selected as the optimal algorithm.