Integrating preoperative multiregion radiomic features with clinical data to predict atrial fibrillation recurrence after radiofrequency ablation - Hui Yan; Xiao-Le Li; Jun-Hao Mei; Lei Zhou; Shu-Hao Guo; Li-Xiang Xie; Xiao-Yun Zhu; Chun-Feng Hu; Chang-Jie Pan; Hui Xue (Frontiers in Cardiovascular Medicine, 2026)
Objective To develop and validate a prediction framework integrating clinical data, left atrium and pulmonary vein morphology, and radiomic features to identify patients at high risk of atrial fibrillation (AF) recurrence after radiofrequency ablation (RFA). Methods Patients with AF who underwent RFA at three centers between August 2018 and October 2024 were retrospectively screened. Patients from two centers were divided into training and internal validation cohorts, and patients from the third center formed the external validation cohort. Clinical and CTA-derived morphological variables and radiomic features from the left atrium and epicardial adipose tissue were analyzed. Clinical, radiomic, and fusion models were evaluated using five machine-learning algorithms. Training performance was estimated from five-fold out-of-fold predictions. Discrimination, calibration, clinical utility, pairwise AUROC differences, and model contributions were assessed using ROC analysis, calibration plots, decision curve analysis, DeLong tests, and SHAP. Results Of 877 patients, 449 formed the training cohort, 192 the internal validation cohort, and 236 the external validation cohort; recurrence occurred in 23.6%, 30.2%, and 35.2%, respectively. The optimal clinical GBDT, radiomic RF, and fusion RF models achieved AUROCs of 0.739, 0.922, and 0.947 in the training cohort; 0.726, 0.837, and 0.839 in internal validation; and 0.722, 0.837, and 0.848 in external validation. Radiomic and fusion models outperformed the clinical model in both validation cohorts, whereas fusion did not significantly outperform radiomic (DeLong P = 0.95 internally and P = 0.43 externally). Decision curves showed net benefit across relevant threshold ranges, while calibration varied across models and cohorts. Conclusion Radiomic models substantially improved post-RFA recurrence discrimination over the clinical model. The fusion model achieved the numerically highest external AUROC, but did not significantly outperform radiomic alone in either validation cohort. These findings support radiomic-based risk stratification while underscoring the need for prospective calibration and validation.