A computed tomography-based radiomics-clinical model incorporating left atrial and proximal pulmonary vein features predicts recurrence after radiofrequency catheter ablation of atrial fibrillation: a multicenter study

Objective To develop and validate a CCTA-based radiomics-clinical model for predicting late recurrence after radiofrequency catheter ablation (RFCA) in patients with atrial fibrillation (AF). Methods This retrospective multicenter study included 379 patients who underwent first-time RFCA and preprocedural CCTA at two centers (Center A, n = 238; Center B, n = 141). Patients from Center A were randomly divided into a training cohort ( n = 166) and an internal validation cohort ( n = 72) at a 7:3 ratio, whereas patients from Center B served as the external validation cohort. A TotalSegmentator model based on the nnU-Net v2 framework was used for fully automated segmentation of the left atrial body, left atrial appendage, and proximal pulmonary vein trunks, defined as the segments from the ostia to the first major bifurcations. These structures were merged and defined as the left atrial complex (LAC), which served as the unified region of interest for radiomics analysis. After assessing feature stability and reducing dimensionality, key features were selected to construct a radiomics score and an XGBoost-based radiomics model. A combined model was then developed by integrating the radiomics score with independent clinical predictors. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), and model comparisons were performed using the DeLong test. Results Of the 379 included patients, 106 (28.0%) developed late AF recurrence within 2 years after ablation. Multivariable logistic regression identified sex and left ventricular ejection fraction as independent clinical predictors. The combined model achieved AUCs of 0.896, 0.894, and 0.869 in the training, internal validation, and external validation cohorts, respectively, consistently outperforming the radiomics model (0.874, 0.840, and 0.787, respectively) and the clinical model (0.694, 0.658, and 0.704, respectively). These findings indicate good predictive performance, with maintained performance in the external validation cohort. Conclusion The CCTA-based radiomics-clinical model derived from the LAC showed good performance for predicting late recurrence after RFCA for AF and may serve as a practical tool for individualized preprocedural risk stratification.