Key result
Integrating LAA deep learning radiomics and clinical variables predicts post-ablation AFib recurrence with 0.92 AUC.
Why the study?
Left atrial appendage remodeling is characteristic of AF and impacts RFCA outcomes, prompting development of a predictive model combining LAA morphological features, deep learning radiomics, and clinical variables.
Does a predictive model combining deep learning radiomics of left atrial appendage features and clinical variables improve prediction of atrial fibrillation recurrence in patients undergoing radiofrequency catheter ablation?
Cohort (n=480)
Yes
Does a predictive model combining deep learning radiomics of left atrial appendage features and clinical variables improve prediction of atrial fibrillation recurrence in patients undergoing radiofrequency catheter ablation?
Effect estimate: AUC 0.92 (95% CI 0.87-0.96)
A predictive model integrating deep learning radiomics of left atrial appendage morphology from CT angiography with clinical variables accurately predicts atrial fibrillation recurrence after radiofrequency catheter ablation.
BACKGROUND: Structural remodeling of the left atrial appendage (LAA) is characteristic of atrial fibrillation (AF), and LAA morphology impacts radiofrequency catheter ablation (RFCA) outcomes. In this study, we aimed to develop and validate a predictive model for AF ablation outcomes using LAA morphological features, deep learning (DL) radiomics, and clinical variables. METHODS: In this multicenter retrospective study, 480 consecutive patients who underwent RFCA for AF at three tertiary hospitals between January 2016 and December 2022 were analyzed, with follow-up through December 2023. Preprocedural CT angiography (CTA) images and laboratory data were systematically collected. LAA segmentation was performed using an nnUNet-based model, followed by radiomic feature extraction. Cox proportional hazard regression analysis assessed the relationship between AF recurrence and LAA volume. The dataset was randomly split into training (70%) and validation (30%) cohorts using stratified sampling. An AF recurrence prediction model integrating LAA DL radiomics with clinical variables was developed. RESULTS: The cohort had a median follow-up of 22 months (IQR 15-32), with 103 patients (21.5%) experiencing AF recurrence. The nnUNet segmentation model achieved a Dice coefficient of 0.89. Multivariate analysis showed that LAA volume was associated with a 5.8% increase in hazard risk per unit increase (aHR 1.058, 95% CI 1.021-1.095; p = 0.002). The model combining LAA DL radiomics with clinical variables demonstrated an AUC of 0.92 (95% CI 0.87-0.96) in the test set, maintaining robust predictive performance across subgroups. CONCLUSION: LAA morphology and volume are strongly linked to AF RFCA outcomes. We developed an LAA segmentation network and a predictive model that combines DL radiomics and clinical variables to estimate the probability of AF recurrence.
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Yanping et al. (2025) conducted a cohort in Atrial fibrillation (n=480). Deep learning radiomics of left atrial appendage features and clinical variables was evaluated on Atrial fibrillation recurrence prediction (AUC 0.92, 95% CI 0.87-0.96). A predictive model integrating left atrial appendage deep learning radiomics with clinical variables accurately predicted atrial fibrillation recurrence after ablation (AUC 0.92; 95% CI 0.87-0.96).
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