Clinical Trials Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 7, 2024; 30(1): 79-90
Published online Jan 7, 2024. doi: 10.3748/wjg.v30.i1.79
Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer
Qing-Qi Hong, Su Yan, Yong-Liang Zhao, Lin Fan, Li Yang, Wen-Bin Zhang, Hao Liu, He-Xin Lin, Jian Zhang, Zhi-Jian Ye, Xian Shen, Li-Sheng Cai, Guo-Wei Zhang, Jia-Ming Zhu, Gang Ji, Jin-Ping Chen, Wei Wang, Zheng-Rong Li, Jing-Tao Zhu, Guo-Xin Li, Jun You
Qing-Qi Hong, He-Xin Lin, Jun You, Department of Gastrointestinal Oncology Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen 361001, Fujian Province, China
Su Yan, Department of Gastrointestinal Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
Yong-Liang Zhao, Department of General Surgery and Center of Minimal Invasive Gastrointestinal Surgery, The First Hospital Affiliated to Army Medical University, Chongqing 400038, China
Lin Fan, Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
Li Yang, Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
Wen-Bin Zhang, Department of Gastrointestinal Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urmuqi 830054, Xinjiang Uygur Autonomous Region, China
Hao Liu, Guo-Xin Li, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China
Jian Zhang, Department of Gastrointestinal Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, Zhejiang Province, China
Zhi-Jian Ye, Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Xiamen 361004, Fujian Province, China
Xian Shen, Department of Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
Li-Sheng Cai, Department of General Surgery Unit 4, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou 363000, Fujian Province, China
Guo-Wei Zhang, Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Xiamen Medical College, Xiamen 361021, Fujian Province, China
Jia-Ming Zhu, Department of Gastrointestinal Nutrition and Hernia Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
Gang Ji, Department of Digestive Diseases, Xijing Hospital, Air Force Military Medical University, Xi'an 710032, Shaanxi Province, China
Jin-Ping Chen, Department of Gastrointestinal Surgery, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362002, Fujian Province, China
Wei Wang, Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, Guangdong Province, China
Zheng-Rong Li, Department of Gastrointestinal Surgery, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
Jing-Tao Zhu, The Third Clinical Medical College, Fujian Medical University, Fuzhou 35000, Fujian Province, China
Co-first authors: Qing-Qi Hong and Su Yan.
Co-corresponding authors: Guo-Xin Li and Jun You.
Author contributions: Yan S, Li GX, You J, and Hong QQ contributed to the conception and design of the research; Hong QQ, Zhao YL, Lin F, Yang L, Zhang WB, Liu H, Lin HX, Zhang J, Ye ZJ, Shen X, Cai LS, Zhang GW, Zhu JM, Ji G, Chen JP, Wang W, Li ZR, Zhu JT, and Yan S contributed to the acquisition of data; Hong QQ, Zhao YL, Lin F, Yang L, Zhang WB, Liu H, Lin HX, Zhang J, Ye ZJ, Shen X, Cai LS, Zhang GW, Zhu JM, Ji G, Chen JP, Wang W, Li ZR, Zhu JT, Yan S, Li GX, and You J contributed to the analysis and interpretation of data; Hong QQ, Lin HX, and Zhu JT contributed to the statistical analysis; Lin HX, Hong QQ, and You J contributed to the obtaining funding; Hong QQ, Zhao YL, Lin F, Yang L, Zhang WB, Liu H, Lin HX, Zhang J, Ye ZJ, Shen X, Cai LS, Zhang GW, Zhu JM, Ji G, Chen JP, Wang W, Li ZR, Zhu JT, Yan S, Li GX, and You J contributed to the drafting the manuscript; Hong QQ, Zhao YL, Lin F, Yang L, Zhang WB, Liu H, Lin HX, Zhang J, Ye ZJ, Shen X, Cai LS, Zhang GW, Zhu JM, Ji G, Chen JP, Wang W, Li ZR, Zhu JT, Yan S, Li GX, and You J contributed to the revision of manuscript for important intellectual content; All authors have read and approve the final manuscript.
Supported by Natural Science Foundation of Fujian Province, No. 2021J011360, and No. 2020J011230; Natural Science Foundation of Xiamen, China, No. 3502Z20214ZD1018, and No. 3502Z20227096; Medical Innovation Project of Fujian Provincial Health Commission, No. 2021CXB019; Youth Scientific Research Project of Fujian Provincial Health Commission, No. 2022QNB013; and Bethune Charitable Foundation, No. HZB-20190528-10.
Institutional review board statement: The study was reviewed and approved by the The First Affiliated Hospital of Xiamen University Institutional Review Board, No. XMFHIIT-2023SL097.
Clinical trial registration statement: This study is registered at Chinese Clinical Trial Registry (www.chictr.org.cn). The registration identification number is ChiCTR2300078445.
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: The authors declare no conflict of interest.
Data sharing statement: The data that support the findings of this study are available from the corresponding author.
CONSORT 2010 statement: The authors have read the CONSORT 2010 statement, and the manuscript was prepared and revised according to the CONSORT 2010 statement.
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: Jun You, PhD, Doctor, Department of Gastrointestinal Oncology Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, No. 55 Zhenhai Road, Xiamen 361001, Fujian Province, China. youjun@xmu.edu.cn
Received: August 30, 2023
Peer-review started: August 30, 2023
First decision: September 23, 2023
Revised: October 30, 2023
Accepted: December 19, 2023
Article in press: December 19, 2023
Published online: January 7, 2024
Abstract
BACKGROUND

Laparoscopic radical gastrectomy is widely used, and perioperative complications have become a highly concerned issue.

AIM

To develop a predictive model for complications in laparoscopic radical gastrectomy for gastric cancer to better predict the likelihood of complications in gastric cancer patients within 30 days after surgery, guide perioperative treatment strategies for gastric cancer patients, and prevent serious complications.

METHODS

In total, 998 patients who underwent laparoscopic radical gastrectomy for gastric cancer at 16 Chinese medical centers were included in the training group for the complication model, and 398 patients were included in the validation group. The clinicopathological data and 30-d postoperative complications of gastric cancer patients were collected. Three machine learning methods, lasso regression, random forest, and artificial neural networks, were used to construct postoperative complication prediction models for laparoscopic distal gastrectomy and laparoscopic total gastrectomy, and their prediction efficacy and accuracy were evaluated.

RESULTS

The constructed complication model, particularly the random forest model, could better predict serious complications in gastric cancer patients undergoing laparoscopic radical gastrectomy. It exhibited stable performance in external validation and is worthy of further promotion in more centers.

CONCLUSION

Using the risk factors identified in multicenter datasets, highly sensitive risk prediction models for complications following laparoscopic radical gastrectomy were established. We hope to facilitate the diagnosis and treatment of preoperative and postoperative decision-making by using these models.

Keywords: Gastric cancer, Laparoscopic radical gastrectomy, Postoperative complications, Laparoscopic total gastrectomy

Core Tip: This is a multicenter clinical study involving 17 Chinese medical centers, which uses machine learning methods to predict the risk of complications in laparoscopic gastric cancer surgery, contributing to the prevention and early warning of complications.