CT Radiomics of Epicardial Adipose Tissue for Predicting Atrial Fibrillation: A Systematic Review.
Authors
Affiliations (7)
Affiliations (7)
- Boonshoft School of Medicine, Wright State University, Dayton, Ohio.
- McGill Faculty of Medicine and Health Sciences, Montréal, QC Canada.
- Temerty Faculty of Medicine, Toronto, ON, Canada.
- Institut Jules Bordet, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Bruxelles, Belgium.
- Department of Biological Sciences, George Washington University, Washington, District of Columbia.
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio.
- Faculty of Health Sciences, Queen's University, Kingston, ON Canada; Department of Cardiac Surgery, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, 11100 Euclid Ave̥, Cleveland, Ohio 44106. Electronic address: [email protected].
Abstract
Atrial fibrillation (AF) is the most common arrhythmia, affecting 1-2% of the general population. Epicardial adipose tissue (EAT) may contribute to AF through its proximity to the myocardium and coronary vessels, as well as its vasocrine and paracrine functions. Previous studies linked EAT thickness and volume with AF incidence. This review consolidates evidence on the correlation between radiomic analysis of EAT and the incidence of AF. This systematic review was conducted in accordance with PRISMA 2020 and registered in the International Prospective Register of Systematic Reviews (PROSPERO). Embase, PubMed, MEDLINE, Web of Science, and Google Scholar were searched for primary studies using CT-based EAT radiomics to predict AF. Case reports, animal studies, pediatric populations, and Magnetic Resonance Imaging (MRI)/ultrasound studies were excluded. Risk of bias was assessed with PROBAST, and methodological quality with the Radiomics Quality Score. Ten studies, including 2952 patients (53.8-81.4% male; mean age range 42.2-74.1 years), met eligibility. Six were retrospective, and four were prospective. The studies evaluated postoperative AF, AF recurrence after catheter ablation, AF detection, and AF subtype classification. Reported machine learning models included random forest, logistic regression, Cox regression, and Least Absolute Shrinkage and Selection Operator (LASSO). Performance metrics ranged from AUC 0.73-0.92, sensitivity 0.50-0.90 and specificity 0.53-0.98. Segmentation methods and feature extraction varied, and external validation was rare. Machine learning models using EAT radiomic features show promise for AF prediction. However, this heterogeneity limits generalizability and precludes immediate incorporation into clinical practice.