Application of fractal dimension combined with radiomics and clinical scores in recurrence prediction of atrial fibrillation after radiofrequency ablation.

Background: Atrial fibrillation (AF) is the most common clinical arrhythmia. Radiofrequency catheter ablation (RFCA) is a primary treatment, but its success rate (50%-80%) varies by AF type and individual differences, making recurrence a key clinical challenge. Previous recurrence assessments ignored atrial remodeling-related complex structural features; fractal dimension (FD) quantifies structural complexity, and radiomics enables high-throughput feature extraction. Integrating these with clinical scores to build a composite model may improve risk stratification accuracy. Methods: This single-center retrospective study enrolled 536 AF patients who underwent RFCA between January 2016 and March 2024, with follow-up through March 2025. Preoperative cardiac computed tomography venography (CTV) images and clinical data, such as APPLE and CHA₂DS₂-VASc scores, were collected via the Hospital Information System (HIS). Automated segmentation of the left atrium (LA) and left atrial appendage (LAA) was performed using specialized software, from which radiomics features were extracted and the FD was calculated. Patients were stratified and randomly divided into a training set ( Results: Among the 536 patients, AF recurrence occurred in 139 cases (25.9%). Specifically, recurrence was observed in 111 cases (25.9%) in the training set and 28 cases (25.9%) in the test set. The integrated model, which combined the FD, radiomics features from both the LA and the LAA, as well as the APPLE and CHA₂DS₂-VASc scores, achieved an area under the curve (AUC) of 0.87 in the validation set. Its performance was significantly superior to that of models based solely on clinical scores (APPLE score AUC = 0.57; CHA₂DS₂-VASc score AUC = 0.66) or radiomics features alone (LAA model AUC = 0.77; LA model AUC = 0.62). Conclusion: The integrated model incorporating multidimensional features enables effective and accurate prediction of AF recurrence after ablation, providing a practical tool for clinical individualized management. Its utility requires further validation and broader application through prospective studies.