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
Abstract
BACKGROUND
Gastric cancer is one of the most common malignant tumors in the digestive system, ranking sixth in incidence and fourth in mortality worldwide. Since 42.5% of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type, the application of imaging diagnosis is restricted.
AIM
To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) algorithms and to evaluate their predictive performance in clinical practice.
METHODS
Data of a total of 369 patients who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People’s Hospital (Jining, China) were collected and analyzed as the verification group. Seven ML models, including decision tree, random forest, support vector machine (SVM), gradient boosting machine, naive Bayes, neural network, and logistic regression, were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML models were established following ten cross-validation iterations using the training dataset, and subsequently, each model was assessed using the test dataset. The models’ performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.
RESULTS
Among the seven ML models, except for SVM, the other ones exhibited higher accuracy and reliability, and the influences of various risk factors on the models are intuitive.
CONCLUSION
The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer, which can aid in personalized clinical diagnosis and treatment.
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.
