Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning study

作者：S. Chen, Ting Deng, Qing Yang, Li Jin, Juanyan Shen, Xu Luo, Juan Tang, Xulian Zhang, Jordan Tovera Salvador, Junliang Ma · 发表于：EClinicalMedicine · 年份：2025 · DOI：10.1016/j.eclinm.2025.103386 · 被引用次数：23 · 研究领域：Lung Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Research Studies

Background: Early identification and prediction of postoperative pulmonary complications (PPCs) are vital for patient management in lung cancer (LC) surgery. However, existing predictive models often lack comprehensive validation and interpretability. This study aimed to develop and validate an explainable machine learning (ML) model to predict PPCs in patients with LC undergoing surgery. Methods: A risk factor variable pool was determined by meta-analysis and Delphi surveys. Patients undergoing LC surgery who were admitted to the Thoracic Surgery Department at the Affiliated Hospital of Zunyi Medical University from 1st January 2022 to 31st October 2023 (retrospective) and from 1st November 2023 to 31st July 2024 (prospective) were used for model development and prospective validation, respectively. The retrospective cohort was randomly split into a training set and an internal validation set at an 8:2 ratio. Feature selection involved univariate analysis, collinearity analysis, nine ML algorithms, and expert consensus. Twelve independent ML models and 26 stacking ensemble models were developed. Predictive performance was evaluated using the area under the receiver-operating-characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Prospective validation was analysed using AUC, Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). The Shapley Additive Explanation (S...