An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis

作者：Xiangfeng Gan, Wei Zhang, Zhuojian Shen, Wenzeng Chen, Xiaohui Duan, Honglue Dai, Ju Chen, Baishen Chen · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2025 · DOI：10.21037/qims-2024-2890 · 被引用次数：2 · 研究领域：Lung Cancer Diagnosis and Treatment、Lung Cancer Research Studies、Radiomics and Machine Learning in Medical Imaging

Background: Visceral pleural invasion (VPI) is a critical prognostic factor in early-stage lung adenocarcinoma, influencing T staging and treatment decisions, yet its preoperative assessment remains challenging due to reliance on postoperative pathological confirmation. This study evaluates interpretable machine learning models to predict VPI in solid cT1 pulmonary nodules using preoperative computed tomography (CT)-based habitat analysis, offering a non-invasive tool to optimize surgical planning. Methods: This retrospective study analyzed data of 802 patient with invasive pulmonary adenocarcinoma and solid lung nodules ≤3 cm from two centers and a public database. CT scans were preprocessed and regions were delineated for feature extraction. Habitat analysis using K-means clustering identified tumor heterogeneity. Radiomic features were extracted and modeled using light gradient boosting machine (LightGBM), with interpretability via SHapley Additive exPlanations (SHAP). Statistical analyses included logistic regression for predictive modeling, Kaplan-Meier for survival assessment, and evaluation of area under the curve (AUC), calibration, and decision curve analysis (DCA) for model performance. Results: The intra-tumoral region and the P5 region, which encompasses both the tumor and peri-tumor areas, were analyzed for habitat characterization. K-means unsupervised clustering was employed to generate habitat subregions. From each subregion, 1,834 radiomic features were extra...