Prediction of Atrial Fibrillation Using Radiomic Features of Left Atrial Epicardial Adipose Tissue on Noncontrast Cardiac Computed Tomography.

ABSTRACT:

Background

Early detection of atrial fibrillation (AF) can prevent AF-related complications. Radiomic analysis of epicardial adipose tissue (EAT) was shown to predict AF recurrence postablation, but only limited data exist regarding left atrial EAT (LA-EAT) radiomic analysis for predicting AF in patients with yet unknown AF. Our aim was to develop prediction model for AF, based on the association of machine learning-based radiomic analysis of LA-EAT and AF.

Methods

Retrospective matched case-control study of patients with and without AF, undergoing noncontrast electrocardiographic (ECG)-gated cardiac computed tomography (CT). Segmentation of LA-EAT and extraction of LA-EAT radiomic features were performed using syngo.via Frontier (Siemens Healthineers, Forchheim, Germany). U

SUBMITTER: Cohen-Dor S 

PROVIDER: S-EPMC12277846 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature