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A prospective longitudinal study followed 312 gastric adenocarcinoma patients across 3, 6, 9, and 12 months to quantify percentage weight loss and derive distinct trajectories using latent growth mixture modeling. For malnutrition risk at 6 months, eight machine learning models were trained with predictors selected by LASSO and Boruta, and a multivariable logistic regression nomogram was developed and validated. XGBoost performed best, and the nomogram showed strong discrimination, calibration, and clinical utility.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/identification-of-postoperative-weight-loss-trajectories-and-development-of-a-machine-learning-based-tool-for-predicting-malnutrition-in-gastric-cancer-patients/128809/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/identification-of-postoperative-weight-loss-trajectories-and-development-of-a-machine-learning-based-tool-for-predicting-malnutrition-in-gastric-cancer-patients/128809.png","ImageObject",300,407,{"name":92,"@type":93},"Violet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-19","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the main goal of the study?","Question",{"text":112,"@type":113},"To identify distinct postoperative weight loss trajectories after radical gastrectomy and develop machine learning-based tools to predict malnutrition risk at 6 months in gastric cancer patients.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were postoperative weight loss trajectories determined?",{"text":117,"@type":113},"Percentage weight loss was calculated at multiple postoperative time points, and latent growth mixture modeling (GMM) was used to identify distinct 12-month trajectories.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best for predicting 6-month malnutrition, and how was it evaluated?",{"text":121,"@type":113},"Among eight machine learning algorithms, XGBoost achieved the best performance. Model and nomogram performance were assessed using discrimination, calibration, clinical utility, and DeLong’s test for AUC comparison.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},128809,1786003613,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},1099523885336,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","OPEN ACCESS  \nEDITED BY  \nWilliam Kwame Amakye,  \nSouth China University of Technology, China  \nREVIEWED BY  \nAnwar Abouelnasr, Alexandria University, Egypt Putu Arik Herliawati,  \nAkademi Kebidanan Kartini, Indonesia  \n*CORRESPONDENCE  \nChanghua Zhuo  \n [zhuo12@outlook.com](zhuo12@outlook.com)  \n†These authors have contributed equally to this work  \nRECEIVED 03 August 2025  \nACCEPTED 05 September 2025  \nPUBLISHED 17 September 2025  \nCITATION  \nYan M, Lin Z, Chen R, Liu Y, Jian J and  \nZhuo C (2025) Identification of postoperative weight loss trajectories and development of a machine learning-based tool for predicting malnutrition in gastric cancer patients.  \nFront. Nutr. 12:1678879.  \ndoi: 10.3389/fnut.2025.1678879  \nCOPYRIGHT  \n© 2025 Yan, Lin, Chen, Liu, Jian and Zhuo. This is an open-access 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.  \nTYPE Original Research PUBLISHED 17 September 2025 DOI 10.3389/fnut.2025.1678879  \nIdentification of postoperative weight loss trajectories and development of a machine learning-based tool for predicting malnutrition in gastric cancer patients  \nMingfang Yan 1†, Zhenmeng Lin 2†, Rong Chen3†, Ying Liu 2, Jinliang Jian 2 and Changhua Zhuo 2*  \n1 Department of Anesthesiology Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China, 2 Department of Gastrointestinal Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China, 3College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China  \nBackground: Significant postoperative weight loss and malnutrition represent common and serious complications following radical gastrectomy for gastric cancer. Early identification of distinct weight loss trajectories and prediction of malnutrition risk may facilitate targeted interventions.  \nMethods: This prospective, observational longitudinal study enrolled 312 gastric adenocarcinoma patients undergoing radical gastrectomy. Participants were assessed preoperatively (T0) and at 3, 6, 9, and 12 months postoperatively (T1–T4) . Percentage weight loss was calculated at each postoperative time point. Latent growth mixture modeling (GMM) identified distinct weight loss trajectories. Eight machine learning algorithms (XGBoost, SVM, RF, NB, KNN, MLP, GBM, PLS) were trained using predictors selected by LASSO regression and the Boruta algorithm to predict GLIM-defined malnutrition at 6 months postoperatively (T2, the peak malnutrition timepoint) . Additionally, a multivariable logistic regression-derived nomogram was developed and validated, with assessments of discrimination, calibration, and clinical utility.  \nResults: GMM identified three distinct 12-month postoperative weight loss trajectories: severe (11 .9%), moderate (36 . 2%), and minimal (51 .9%) . The prevalence of GLIM-defined malnutrition peaked at 51.6% at 6 months (T2) . Among the eight machine learning models, XGBoost achieved the best performance in predicting 6-month malnutrition. The final nomogram, which incorporated age ≥65 years, preoperative underweight status, preoperative reduced muscle mass, and total gastrectomy, showed excellent discrimination, calibration, and clinical utility. DeLong’s test indicated no significant difference in AUC between the XGBoost model and the nomogram (p = 0. 121) . Conclusion: This study delineates distinct postoperative weight loss trajectories in gastric cancer patients. We developed and validated both an advanced ML model (XGBoost) and a clinically interpretable nomogram for accurately predicting 6-mon","cbCaik8SEQ5ne0zD","https://ap.wps.com/l/cbCaik8SEQ5ne0zD","pdf",5696537,15,"English","# Background\n# Methods\n## Study design and participants\n## Trajectory modeling\n## Machine learning and prediction models\n## Nomogram development and validation\n# Results\n# Conclusion","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To identify distinct postoperative weight loss trajectories after radical gastrectomy and develop machine learning-based tools to predict malnutrition risk at 6 months in gastric cancer patients.\"},{\"question\":\"How were postoperative weight loss trajectories determined?\",\"answer\":\"Percentage weight loss was calculated at multiple postoperative time points, and latent growth mixture modeling (GMM) was used to identify distinct 12-month trajectories.\"},{\"question\":\"Which model performed best for predicting 6-month malnutrition, and how was it evaluated?\",\"answer\":\"Among eight machine learning algorithms, XGBoost achieved the best performance. Model and nomogram performance were assessed using discrimination, calibration, clinical utility, and DeLong’s test for AUC comparison.\"}]","Identification of postoperative weight loss trajectories and development of a machine learning-based tool for predicting malnutrition in gastric cancer patients | PDF",38]