Abstract
BackgroundLung adenocarcinomas manifesting as part-solid nodules (PSNs) represent a distinct clinical subtype where accurate preoperative determination of pathological invasiveness critically influences both prognosis and surgical decision-making. This multicenter study aims to develop an ensemble machine learning classifier that integrates computed tomography (CT) radiomic signatures with clinical-radiological features to enhance the preoperative prediction of invasive status.MethodsWe retrospectively analyzed 344 patients with pathologically confirmed lung adenocarcinoma presenting as PSNs across three medical centers. Following random allocation into training (n = 240) and validation (n = 104) sets (7:3 ratio), we extracted 1239 quantitative radiomic features from preoperative thin-section CT scans. Through rigorous feature engineering, we constructed a radiomic score using least absolute shrinkage and selection operator regression. We systematically evaluated both single-algorithm classifiers and ensemble approaches (including hard/soft voting and stacking), incorporating both the radiomic score and clinical-radiological features.ResultsAmong the various evaluated machine learning models, the stacking classifier, which combines radiomic scores and clinical-radiological features, performed the best, achieving an AUC of 0.84, an accuracy of 0.817, an F1 score of 0.869, a precision of 0.818, and a recall of 0.926.ConclusionOur stacking ensemble learning classifier, which synergistically combines CT radiomics signatures with clinical-radiological features, provides a clinically actionable tool for the preoperative prediction of pathological invasiveness in PSN-type lung adenocarcinoma, thereby enhancing individualized surgical planning.