[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122461-en":3,"doc-seo-122461-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122461,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Development and validation of a machine learning model for predicting early postoperative complications after radical gastrectomy","Machine learning is used to stratify the risk of early postoperative complications in patients undergoing radical gastrectomy for gastric cancer. Clinical data are retrospectively collected from patients treated at Peking Union Medical College Hospital between 2014 and 2024. Ten machine learning algorithms are trained and validated with nested cross-validation, and model performance is assessed using ROC curves, decision curve analysis, and calibration curves. The XGBoost model shows the strongest predictive capability and improved calibration, supporting enhanced clinical risk assessment.","TYPE Original Research PUBLISHED 14 October 2025 DOI 10.3389/fonc.2025.1631260  \nOPEN ACCESS  \nEDITED BY  \nArunkumar Krishnan,  \nLevine Cancer Institute, United States  \nREVIEWED BY  \nAlessandro Dario Mazzotta, Sapienza University of Rome, Italy Daorong Wang,  \nYangzhou University, China  \n*CORRESPONDENCE  \nJianchun Yu  \n[yu-jch@163.com](yu-jch@163.com)  \nRECEIVED 19 May 2025  \nACCEPTED 29 September 2025  \nPUBLISHED 14 October 2025  \nCITATION  \nLi R, Zhao Z and Yu J (2025) Development and validation of a machine learning model for predicting early postoperative complications after radical gastrectomy. Front. Oncol. 15:1631260 .  \ndoi: 10.3389/fonc.2025.1631260  \nCOPYRIGHT  \n© 2025 Li, Zhao and Yu. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDevelopment and validation of a machine learning model for predicting early postoperative complications after radical gastrectomy  \nRuyin Li 1, Zirui Zhao 2 and Jianchun Yu 1*  \n1 Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China, 2 Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China  \nBackground: Postoperative complications significantly impact gastric cancer patients ’ recovery and remain a major research focus. This study aimed to develop a machine learning model utilizing preoperative and intraoperative data to stratify the risk of early postoperative complications in patients undergoing radical gastrectomy.  \nMethods: Clinical data from gastric cancer patients who underwent radical gastrectomy at Peking Union Medical College Hospital between 2014 and 2024 were retrospectively collected. Using R software, ten machine learning algorithms—including eXtreme Gradient Boosting, Support Vector Machine, random forest, Neural Network, naive Bayes, logistic regression, Linear Discriminant Analysis, K-Nearest Neighbors, Generalized Linear Model with Elastic-Net Regularization and classification tree—were employed to construct predictive models for early postoperative complications. Nested cross-validation was applied for model validation, and performance was evaluated using receiver operating characteristic curves, decision curve analysis, and calibration curves. Results: A total of 926 patients were included in this study, comprising 667 males (72%) and 259 females (28%), with 131 (14.13%) suffering postoperative complications. Predictive features included smoking, Nutritional Risk Screening 2002 score>3, reconstruction, clinical T-stage>1, operative time, neoadjuvant chemotherapy combined with immunotherapy or targeted therapy, andresection site. Among the ten models, eXtreme Gradient Boosting demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.788, along with superior calibration and decision curve analysis results.  \nConclusion: Based on preoperative and intraoperative data, the eXtreme Gradient Boosting model demonstrated the strongest predictive capability for  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \npostoperative complications following radical gastrectomy.These findings underscore the potential of machine learning-based models in stratifying the risk of early postoperative complications in patients undergoing radical gastrectomy, thereby enhancing clinical decision-making and improving patient outcomes in gastric cancer surgery.  \nKEYWORDS  \ngastric cancer, complication, machine learning, predictive ","cbCaitiOcsgexT5B","https://ap.wps.com/l/cbCaitiOcsgexT5B","pdf",2173992,1,11,"English","en",105,"# Background\n# Methods\n## Data and algorithms\n## Model validation and evaluation\n# Results\n# Conclusion","[{\"question\":\"What is the main goal of the study on radical gastrectomy patients?\",\"answer\":\"To develop and validate a machine learning model that stratifies the risk of early postoperative complications using preoperative and intraoperative data.\"},{\"question\":\"Which machine learning methods were compared in the model development?\",\"answer\":\"Ten algorithms were used, including XGBoost, SVM, random forest, neural network, naive Bayes, logistic regression, LDA, KNN, an elastic-net GLM, and classification trees.\"},{\"question\":\"How was model performance evaluated?\",\"answer\":\"Nested cross-validation was used for validation, and performance was assessed with ROC curves, decision curve analysis, and calibration curves.\"}]","Development and validation of a machine learning model for predicting early postoperative complications after radical gastrectomy | 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is the main goal of the study on radical gastrectomy patients?","Question",{"text":75,"@type":76},"To develop and validate a machine learning model that stratifies the risk of early postoperative complications using preoperative and intraoperative data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods were compared in the model development?",{"text":80,"@type":76},"Ten algorithms were used, including XGBoost, SVM, random forest, neural network, naive Bayes, logistic regression, LDA, KNN, an elastic-net GLM, and classification trees.",{"name":82,"@type":73,"acceptedAnswer":83},"How was model performance evaluated?",{"text":84,"@type":76},"Nested cross-validation was used for validation, and performance was assessed with ROC curves, decision curve analysis, and calibration 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