[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127395-en":3,"doc-seo-127395-105":30,"detail-sidebar-cat-0-en-105":88},{"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},127395,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning-based preoperative prediction of perioperative venous thromboembolism in Chinese lung cancer patients - A retrospective cohort study","Perioperative venous thromboembolism (VTE) is a serious complication following lung cancer surgery, and conventional prediction models may struggle with complex clinical variables. A retrospective cohort of 1,013 surgically treated Chinese lung cancer patients (April 2021–December 2023) was analyzed to develop and validate a machine learning model for preoperative VTE risk assessment. Six key predictors were selected, and eight ML methods were evaluated using discrimination, calibration, and decision-curve analyses. The extreme gradient boosting (XGB) model achieved the best overall performance and was used to create an online prediction tool for early risk stratification and personalized thromboprophylaxis planning.","TYPE Original Research PUBLISHED 25 June 2025  \nDOI 10.3389/fonc.2025.1588817  \nOPEN ACCESS  \nEDITED BY  \nPierpaolo Di Micco,  \nOspedale Santa Maria Delle Grazie, Italy  \nREVIEWED BY  \nNicolina Capoluongo, Colli Hospital, Italy Ngoc Vo Hong,  \nSanta Chiara Hospital, Italy  \n*CORRESPONDENCE  \nWei Liu  \n [l_w01@jlu.edu.cn](l_w01@jlu.edu.cn)  \nRECEIVED 06 March 2025  \nACCEPTED 03 June 2025  \nPUBLISHED 25 June 2025  \nCITATION  \nChen Z, Qiang M, Hong Y, Tian W, Tang Mand Liu W (2025) Machine learning-based preoperative prediction of perioperative venous thromboembolism in Chinese lung cancer patients: a retrospective cohort study.  \nFront. Oncol. 15:1588817 .  \ndoi: 10.3389/fonc.2025.1588817  \nCOPYRIGHT  \n© 2025 Chen, Qiang, Hong, Tian, Tang and Liu. 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.  \nMachine learning-based preoperative prediction of perioperative venous thromboembolism in Chinese lung cancer patients: a retrospective cohort study  \nZhe Chen 1, Min Qiang 1,2, Yang Hong 3, Weibo Tian 4, Mingbo Tang1 and Wei Liu 1*  \n1 Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China,  \n2College of Clinical Medicine, Jilin University, Changchun, China, 3Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain, 4 Department of Neurology, The First Hospital of Jilin University, Changchun, China  \nBackground: Perioperative venous thromboembolism (VTE) is a severe complication in lung cancer surgery. Traditional prediction models have limitations in handling complex clinical data, whereas machine learning (ML) offers enhanced predictive accuracy. This study aimed to develop and validate an ML-based model for preoperative VTE risk assessment.  \nMethods: A retrospective cohort of 1,013 lung cancer patients who underwent surgery at the First Hospital of Jilin University (April 2021–December 2023) was analyzed. Preoperative clinical and laboratory data were collected, and six key predictors—age, mean corpuscular volume, mean corpuscular hemoglobin,fibrinogen, D-dimer, and albumin—were identified using univariate analysis and Lasso regression. Eight ML models, including extreme gradient boosting (XGB), random forest, logistic regression, and support vector machines, were trained and evaluated using AUC, precision-recall curves, decision curve analysis, and calibration curves.  \nResults: VTE occurred in 175 patients (17 .3%) . The XGB model demonstrated the highest predictive performance (AUC: 0.99 training, 0.66 validation; AUPRC: 0.323), with age and mean corpuscular volume identified as the most influential predictors. An online prediction tool was developed for clinical application.  \nConclusion: The ML-based XGB model provides a reliable preoperative risk assessment for VTE in lung cancer patients, enabling early risk stratification and personalized thromboprophylaxis.  \nKEYWORDS  \nlung cancer, perioperative period, venous thromboembolism, machine learning, prediction model  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nLung cancer remains a leading cause of cancer-related mortality worldwide, accounting for a substantial proportion of global cancer deaths (1) . Surgical intervention plays a crucial role in the treatment of lung cancer, particularly in the early stages ofthe disease, where it not only has the potential to cure the disease but also significantly improves patient survival rates (2) . However, perioperative venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), remains one of the most serious complications after surgery, ","cbCaifijZG578j0Z","https://ap.wps.com/l/cbCaifijZG578j0Z","pdf",1877475,1,11,"English","en",105,"# Introduction\n# Methods\n## Study cohort and data collection\n## Predictor selection\n## Machine learning models and evaluation metrics\n# Results\n# Conclusion","[{\"question\":\"What problem does the study address in lung cancer surgery?\",\"answer\":\"It targets perioperative venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, which can markedly increase morbidity and mortality after surgery. 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