Coronary computed tomography angiography and AI-driven quantitative plaque characterization provide novel imaging biomarkers for precision phenotyping and individualized management in stable CAD.
CCTA-derived imaging biomarkers and AI-driven quantitative plaque assessments hold promise for personalized risk stratification and management in stable coronary artery disease.
Abstract Following technological developments and new landmark trials, the diagnostic work-up of symptomatic chronic coronary artery disease has evolved. Clinical guidelines now favor non-invasive anatomical assessments by coronary computed tomography angiography (CCTA) as the first-line modality to evaluate coronary artery disease in the majority of patients with chest pain. This shift from ischemia testing to stenosis and plaque characterization has resulted in the development of new imaging biomarkers reflecting a variety of coronary plaque features, many of which have proven to be important clinical risk markers. Consequently, there has been a transition from qualitative to semi-quantitative and fully quantitative plaque acquisitions over the entire coronary tree. With the integration of artificial intelligence, novel software enables rapid quantitative acquisitions of plaque components, making them feasible for use in clinical practice. CCTA has also enabled identification of precursor features associated with plaque development such as peri-coronary artery adipose tissue attenuation and epicardial adipose tissue volume. This review provides an overview of CCTA derived plaque features in coronary artery disease and associated imaging biomarkers of risk to highlight their potential applications in precision phenotyping and individualized management decisions. It further outlines anticipated future developments that may enable widespread clinical adoption of these novel imaging biomarkers.
Lenell et al. (Mon,) conducted a review in stable coronary artery disease. Coronary computed tomography angiography (CCTA) was evaluated. Coronary computed tomography angiography and AI-driven quantitative plaque characterization provide novel imaging biomarkers for precision phenotyping and individualized management in stable CAD.