Radiomic analysis of EAT on CCTA predicted non-calcified coronary plaques with severe stenosis with 95% accuracy, 100% sensitivity, and 93% specificity.
Does radiomic assessment of epicardial adipose tissue on CCTA predict the presence of severe non-calcified coronary atherosclerotic plaques in patients with known or suspected CAD?
Radiomic texture analysis of epicardial adipose tissue on CCTA can accurately predict the presence of severe non-calcified coronary plaques, serving as a potential novel non-invasive imaging biomarker.
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Epicardial adipose tissue (EAT) has previously been associated with coronary artery calcium scores, an increased burden of coronary artery disease (CAD), and features of plaque instability. These associations are likely mediated by endocrine and paracrine signaling from bioactive molecules secreted by EAT, which may contribute to coronary atherosclerosis. EAT can be non-invasively quantified on images obtained during coronary computed tomography angiography (CCTA). This study aimed to evaluate the potential association between EAT and non-calcified coronary plaques with severe stenosis using radiomic methodology. Materials and Methods: A total of 128 consecutive patients undergoing CCTA—both with and without contrast—for known or suspected CAD were retrospectively analyzed. EAT features were extracted from contrast scans. Coronary artery plaque features were evaluated using Coronary Artery Disease-Reporting and Data System (CAD-RADS). Results: EAT features showed a statistically significant positive correlation with non-calcified coronary plaques with severe grades of stenosis (CAD-RADS > 4). The Ensemble Machine Learning (EML) model combined with coronary plaque data showed a sensitivity of 1.00 and a specificity of 0.93, with a negative predictive value of 1.00 and a positive predictive value of 0.85, and an accuracy of 0.95 (95% CI: 0.9221–1) in internal validation. Conclusions: EAT may represent a novel imaging biomarker associated with the presence of actionable coronary plaques. Radiomic texture analysis of EAT could enhance the non-invasive prediction of coronary stenoses. These preliminary findings support the clinical utility of EAT evaluation via CCTA in patients with low to intermediate cardiovascular risk.
Donna et al. (Mon,) reported a other. Radiomic analysis of EAT on CCTA predicted non-calcified coronary plaques with severe stenosis with 95% accuracy, 100% sensitivity, and 93% specificity.