ArticleFrontiers in cardiovascular medicine2026
Integrating preoperative multiregion radiomic features with clinical data to predict atrial fibrillation recurrence after radiofrequency ablation.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.