Coronary computed tomography angiography (CCTA) has emerged as a key modality in the non-invasive assessment of coronary artery disease (CAD), enabling high-resolution visualization of both coronary anatomy and plaque characteristics. Beyond detecting luminal stenosis, CCTA facilitates comprehensive evaluation of plaque burden, composition, and high-risk features, offering incremental prognostic value over traditional risk stratification tools. Recent advancements, including artificial intelligence (AI)-driven analytics, have enhanced the utility of CCTA by enabling automated plaque quantification and the integration of functional metrics such as Fractional reserve by CT (FFR-CT). Moreover, CCTA-derived low-attenuation plaque burden has proven to be a robust predictor of myocardial infarction. Emerging interest in adipose tissue imaging has further expanded the diagnostic capabilities of CCTA. Metrics such as the fat attenuation index (FAI) and pericoronary adipose tissue (PCAT) attenuation reflect coronary inflammation and independently predict adverse cardiac events. AI has also facilitated the evaluation of epicardial and visceral fat depots, linking these adipose compartments to systemic inflammation and cardiometabolic risk. CCTA is increasingly employed to monitor therapeutic efficacy. Serial imaging studies reveal that lipid-lowering therapies-particularly statins, PCSK9 inhibitors, and icosapent ethyl-can induce plaque regression and stabilization. Anti-inflammatory treatments, including colchicine, may contribute to plaque calcification and reduced vascular inflammation. As our understanding of coronary pathophysiology evolves, integrating CCTA with AI and body composition analysis holds promise for refining personalized risk prediction and guiding preventive strategies. This review underscores the expanding role of CCTA as a pivotal modality in preventive cardiology.
Mushtaq et al. (Sat,) studied this question.