Preoperative Prediction of Persistent Type II Endoleaks After Endovascular Aortic Repair Using Multiregional Perianeurysmal Computed Tomography Angiography Radiomics: A Multicenter Study

Background: Preoperative identification of patients with persistent type II endoleaks (T2ELs) after endovascular aortic repair (EVAR) for abdominal aortic aneurysm (AAA) could improve individualized management. Current approaches based on conventional anatomical factors show limited predictive accuracy. Methods: Consecutive patients with AAA undergoing EVAR at three centers were retrospectively included. Radiomic characteristics were extracted from preoperative computed tomography angiographic images, including the aneurysm sac and five concentric perianeurysmal zones positioned 2–10 mm from the outer wall. After feature selection, six radiomic models employing a support vector machine classifier were developed and subsequently compared. This optimal radiomic signature was then combined with significant clinical predictors to formulate a combined model. The model’s performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis, while its interpretability was assessed using SHapley Additive exPlanations (SHAP) analysis. Results: The radiomic model combining features from the aneurysm intra-sac and the 6-mm perianeurysmal region demonstrated superior predictive accuracy, with AUCs of 0.910, 0.907, 0.886, and 0.859 in the training, internal validation, and two external test sets, respectively. The maximum aneurysm diameter and thrombus area were identified as the independent clinical predictors. The combined model further improved discrimination, achieving AUCs of 0.954, 0.933, 0.924, and 0.896 in the corresponding cohorts, along with excellent calibration and clinical net benefit. The SHAP analysis explained its predictions both locally and globally. Conclusion: A combined model that merges perianeurysmal radiomic features with essential clinical factors offers a precise and non-invasive approach for preoperative T2EL risk stratification following EVAR, thereby facilitating personalized surveillance protocols.