ArticlePloS one2025
Construction of an automated machine learning-based predictive model for postoperative pulmonary complications risk in non-small cell lung cancer patients undergoing thoracoscopic surgery.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Prediction of postoperative pulmonary infection after video-assisted thoracoscopic anatomical pulmonary resection using an interpretable machine learning model.Journal of thoracic disease · 2026Article
- Prediction of peak oxygen uptake using interpretable machine learning on routinely available preoperative assessments before lung resection.Translational lung cancer research · 2026Article
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- Interpretable machine learning unveils non-linear inflammatory thresholds and synergistic interactions in post-burn hypertrophic scarring: development of an intelligent clinical decision support system.Scientific reports · 2026Article
- Perioperative Blood Biomarkers of Infectious and Non-Infectious Postoperative Pulmonary Complications: A Narrative Review.Journal of clinical medicine · 2026Review
- Perioperative ΔNLR and ΔPNI independently predict postoperative pulmonary complications in non-small cell lung cancer: a difference analysis-based nomogram.American journal of cancer research · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a predictive framework integrating machine learning and clinical parameters for postoperative pulmonary complications (PPCs) in non-small cell lung cancer (NSCLC) patients undergoing video-assisted thoracic surgery (VATS).
methodsThis retrospective study analyzed 286 NSCLC patients (2022-2024), incorporating 13 demographic, metabolic-inflammatory, and surgical variables. An Improved Blood-Sucking Leech Optimizer (IBSLO) enhanced via Cubic mapping and opposition-based learning was developed. Model performance was evaluated using AUC-ROC, F1-score, and decision curve analysis (DCA). SHAP interpretation identified key predictors.
resultsThe IBSLO demonstrated significantly superior convergence performance versus original BSLO, ant lion optimizer (ALO), Harris hawks optimization (HHO), and whale optimization algorithm (WOA) across all 12 CEC2022 test functions. Subsequently, the IBSLO-optimized automated machine learning (AutoML) model achieved ROC-AUC/PR-AUC values of 0.9038/0.8091 (training set) and 0.8775/0.8175 (testing set), significantly outperforming four baseline models: logistic regression (LR), support vector machine (SVM), XGBoost, and LightGBM. SHAP interpretability identified six key predictors: preoperative leukocyte count, body mass index (BMI), surgical approach, age, intraoperative blood loss, and C-reactive protein (CRP). Decision curve analysis demonstrated significantly higher net clinical benefit of the AutoML model compared to conventional methods across expanded threshold probability ranges (training set: 8-99%; testing set: 3-80%).
conclusionThis study establishes an interpretable machine learning framework that improves preoperative risk stratification for NSCLC patients, offering actionable guidance for thoracic oncology practice.
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