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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Dec 27, 2025; 17(12): 112520
Published online Dec 27, 2025. doi: 10.4240/wjgs.v17.i12.112520
Published online Dec 27, 2025. doi: 10.4240/wjgs.v17.i12.112520
Machine-learning-based prediction model for Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy in gastric cancer
Ru-Yin Li, Jian-Chun Yu, Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
Zi-Rui Zhao, Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
Tian Yu, Department of Gastrointestinal Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing 102218, China
Author contributions: Li RY and Yu JC designed the research study; Li RY, Zhao ZR, and Yu T performed the research; Li RY and Zhao ZR analyzed the data and wrote the manuscript; All authors read and approved the final manuscript.
Supported by the National Key Research and Development Program of China, No. 2022YFF1100404; and the National High Level Hospital Clinical Research Funding of China, No. 2022-PUMCH-B-005.
Institutional review board statement: The study was reviewed and approved by the Peking Union Medical College Hospital, Chinese Academy of Medical Sciences Ethics Committee (Approval No. I-24PJ0626).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All authors report no relevant conflicts of interest for this article.
Data sharing statement: The datasets generated and analyzed during the current study are not publicly available due the policy of the institution but are available from the corresponding author on reasonable request.
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: Jian-Chun Yu, MD, PhD, Chief, Professor, Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China. yu-jch@163.com
Received: July 31, 2025
Revised: September 26, 2025
Accepted: October 27, 2025
Published online: December 27, 2025
Processing time: 147 Days and 14.8 Hours
Revised: September 26, 2025
Accepted: October 27, 2025
Published online: December 27, 2025
Processing time: 147 Days and 14.8 Hours
Core Tip
Core Tip: Addressing data scarcity in gastric cancer neoadjuvant therapy, this study used a large patient cohort (n = 455) to pioneer a machine learning model for predicting Clavien-Dindo grade ≥ II complications post-neoadjuvant therapy and laparoscopic gastrectomy. Key predictors included smoking status, Nutritional Risk Screening-2002 score, American Society of Anesthesiologists classification, neoadjuvant therapy, surgical approach, operating time, and intraoperative blood loss. The neural network ensemble model demonstrated superior performance with optimal discrimination, calibration, and clinical utility, potentially offering a tool for perioperative risk stratification and management optimization.