ArticleFrontiers in cardiovascular medicine2022
Development and Validation of a Machine Learning-Based Radiomics Model on Cardiac Computed Tomography of Epicardial Adipose Tissue in Predicting Characteristics and Recurrence of Atrial Fibrillation.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2023Pooled it
- Explainable machine learning model reveals its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation.BMC cardiovascular disorders · 2023Trial
- Machine learning prediction of outcome following pulsed-field atrial fibrillation ablation: patient selection and risk factors.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- Atrial fibrillation type-specific prediction of recurrence after catheter ablation: the pivotal role of right atrial remodeling revealed by explainable machine learning.Frontiers in cardiovascular medicine · 2026Article
- 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.Frontiers in medicine · 2026Article
- Correlation between myocardial radiomics features at the left atrium-left atrial appendage junction and stroke risk in atrial fibrillation patients: a retrospective case-control study.Frontiers in cardiovascular medicine · 2026Article
- Reduction of metallic artifacts from the novel leadless pacemaker using single-energy metal artifact reduction algorithm in coronary CT angiography.BMC medical imaging · 2025Article
- Chest CT-based analysis of radiomic and volumetric differences in epicardial adipose tissue in HFrEF patients with and without AF.BMC cardiovascular disorders · 2025Article
- Radiomic features of peri-left atrial epicardial adipose tissue and atrial fibrillation recurrence after ablation.Open heart · 2025Article
- Article
- Machine learning and deep learning predictive models for prognosis in patients with atrial fibrillation: a systematic review and meta-analysis.BMJ digital health & AI · 2025Article
- A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations.Journal of imaging informatics in medicine · 2024Article
- Radiomics parameters of epicardial adipose tissue predict mortality in acute pulmonary embolism.Respiratory research · 2024Article
- Beyond Clinical Factors: Harnessing Artificial Intelligence and Multimodal Cardiac Imaging to Predict Atrial Fibrillation Recurrence Post-Catheter Ablation.Journal of cardiovascular development and disease · 2024Review
- CT-based whole lung radiomics nomogram: a tool for identifying the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease.European radiology · 2024Article
- Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus.BMC cardiovascular disorders · 2024Article
- CT radiomic features reproducibility of virtual non-contrast series derived from photon-counting CCTA datasets using a novel calcium-preserving reconstruction algorithm compared with standard non-contrast series: focusing on epicardial adipose tissue.The international journal of cardiovascular imaging · 2024Article
- Cardiac computer tomography-derived radiomics in assessing myocardial characteristics at the connection between the left atrial appendage and the left atrium in atrial fibrillation patients.Frontiers in cardiovascular medicine · 2024Article
- Review
- Radiomics Signature of Epicardial Adipose Tissue for Predicting Postoperative Atrial Fibrillation after Off-Pump Coronary Artery Bypass Surgery.Reviews in cardiovascular medicine · 2023Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to evaluate the feasibility of differentiating the atrial fibrillation (AF) subtype and preliminary explore the prognostic value of AF recurrence after ablation using radiomics models based on epicardial adipose tissue around the left atrium (LA-EAT) of cardiac CT images. Method: The cardiac CT images of 314 patients were collected wherein 251 and 63 cases were randomly enrolled in the training and validation cohorts, respectively. Mutual information and the random forest algorithm were used to screen for the radiomic features and construct the radiomics signature. Radiomics models reflecting the features of LA-EAT were built to differentiate the AF subtype, and the multivariable logistic regression model was adopted to integrate the radiomics signature and volume information. The same methodology and algorithm were applied to the radiomic features to explore the ability for predicting AF recurrence. Results: The predictive model constructed by integrating the radiomic features and volume information using a radiomics nomogram showed the best ability in differentiating AF subtype in the training [AUC, 0.915; 95% confidence interval (CI), 0.880-0.951] and validation (AUC, 0.853; 95% CI, 0.755-0.951) cohorts. The radiomic features have shown convincible predictive ability of AF recurrence in both training (AUC, 0.808; 95% CI, 0.750-0.866) and validation (AUC, 0.793; 95% CI, 0.654-0.931) cohorts. Conclusions: The LA-EAT radiomic signatures are a promising tool in the differentiation of AF subtype and prediction of AF recurrence, which may have clinical implications in the early diagnosis of AF subtype and disease management.
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