Sistema de Salut de Catalunya
Institut Català de la Salut
[Chen Z, Tang M, Liu W] Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China. [Qiang M] Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China. College of Clinical Medicine, Jilin University, Changchun, China. [Hong Y] Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. [Tian W] Department of Neurology, The First Hospital of Jilin University, Changchun, China
Vall d'Hebron Barcelona Hospital Campus
2025-10-14T08:30:39Z
2025-10-14T08:30:39Z
2025-06-25
Machine learning; Perioperative period; Venous thromboembolism
Aprendizaje automático; Período perioperatorio; Tromboembolismo venoso
Aprenentatge automàtic; Període perioperatori; Tromboembolisme venós
Background: 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. Methods: 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. Results: 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. Conclusion: The ML-based XGB model provides a reliable preoperative risk assessment for VTE in lung cancer patients, enabling early risk stratification and personalized thromboprophylaxis.
The study was supported by Surgical standardized diagnosis and treatment research project WKZX2023WK0112, Jilin Province’s Special Project for Medical and Health Care Talents JLSWSRCZX2023-35 and JLSWSRCZX2023-96.
Article
Published version
English
Pulmons - Càncer - Cirurgia; Tromboembolisme - Complicacions; Aprenentatge automàtic; DISEASES::Cardiovascular Diseases::Vascular Diseases::Embolism and Thrombosis::Thromboembolism::Venous Thromboembolism; DISEASES::Neoplasms::Neoplasms by Site::Thoracic Neoplasms::Respiratory Tract Neoplasms::Lung Neoplasms; Other subheadings::Other subheadings::Other subheadings::/surgery; PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning; ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Surgical Procedures, Operative::Perioperative Period; ENFERMEDADES::enfermedades cardiovasculares::enfermedades vasculares::embolia y trombosis::tromboembolia::tromboembolia venosa; ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias torácicas::neoplasias del tracto respiratorio::neoplasias pulmonares; Otros calificadores::Otros calificadores::Otros calificadores::/cirugía; FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático; TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::intervenciones quirúrgicas::período perioperatorio
Frontiers Media
Frontiers in Oncology;15
https://doi.org/10.3389/fonc.2025.1588817
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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