Improving Risk Stratification of Pulmonary Nodules: An Integrated Perinodular Vascular and Radiomic Model for Clinical Decision Support

Objectives Lung cancer is the leading cause of cancer-related deaths worldwide, with most patients diagnosed at advanced stages. Accurate differentiation of benign and malignant pulmonary nodules remains a major clinical challenge. Materials and Methods We established and validated the perinodular vessel count (PVC) as an instrumental imaging biomarker, demonstrating its significant contribution to discriminating malignant pulmonary nodules. Leveraging this finding, we constructed an integrated predictive model incorporating intranodular and perinodular radiomics, PVC, and relevant clinical variables. A two-tiered feature selection strategy employing both MRMR and Relief algorithms was implemented to refine feature sets, followed by the development of an ensemble decision tree-based classifier. The model underwent rigorous multi-center validation and exhibited diagnostic performance on par with that of three experienced clinicians, underscoring its potential utility in clinical decision-making. Results The model incorporating perinodular vascular features significantly outperformed the non-vascular feature model. Furthermore, the combined clinical-vascular-radiomic model demonstrated substantially improved performance over the clinical-vascular model, achieving AUCs of 0.8704 (CI: [0.6417,0.9676]) validation set, 0.8225 on independent test set (CI: [0.7298,0.9168]), and 0.7937 (CI: [0.4234,1]) on external test set. The PVC feature was consistently identified as one of the most important feature among all features in both feature selection and SHAP interpretability analysis. Conclusion Integration of vascular characteristics markedly improves diagnostic performance and model generalizability. The consistent importance of PVC highlights its clinical value, and the model shows promising potential to assist in decision-making and reduce unnecessary invasive procedures.