Abstract
Introduction: Atherosclerotic plaque characteristics may influence the hemodynamic consequences of coronary lesions. This study sought to assess the performance of a machine learning (ML) score integrating coronary computed tomography angiography (CCTA)-based quantitative plaque features for the prediction of ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by [15O]H2O positron emission tomography (PET).
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