ORIGINAL RESEARCH article
Front. Oncol.
Sec. Thoracic Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1588817
This article is part of the Research TopicTherapies Approaches in Venous Thromboembolism Management and CoagulationView all 9 articles
Machine Learning-Based Preoperative Prediction of Perioperative Venous Thromboembolism in Chinese Lung Cancer Patients: A Retrospective Cohort Study
Provisionally accepted- 1Department of Thoracic Surgery, First Affiliated Hospital of Jilin University, Changchun, Hebei Province, China
- 2Department of Neurology, First Affiliated Hospital of Jilin University, Changchun, Jilin Province, China
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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.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.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.The ML-based XGB model provides a reliable preoperative risk assessment for VTE in lung cancer patients, enabling early risk stratification and personalized thromboprophylaxis.
Keywords: lung cancer, Perioperative Period, Venous Thromboembolism, machine learning, Prediction model
Received: 06 Mar 2025; Accepted: 03 Jun 2025.
Copyright: © 2025 Chen, Qiang, Tian, Liu and Tang. 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) or licensor 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.
* Correspondence:
Wei Liu, Department of Thoracic Surgery, First Affiliated Hospital of Jilin University, Changchun, 130021, Hebei Province, China
Mingbo Tang, Department of Thoracic Surgery, First Affiliated Hospital of Jilin University, Changchun, 130021, Hebei Province, China
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