Abstract Coronary computed tomography angiography (CCTA) has become a cornerstone in the non-invasive evaluation of coronary artery disease (CAD). Beyond defining stenosis severity, CCTA enables detailed quantification of total atherosclerotic burden, plaque composition, and imaging markers of plaque vulnerability. Recent advances—including artificial intelligence (AI)-enhanced algorithms—now allow automated and highly reproducible plaque phenotyping, with major implications for risk stratification and therapeutic monitoring. Concurrently, imaging of pericoronary adipose tissue has introduced novel biomarkers of vascular inflammation, particularly the Fat Attenuation Index (FAI) and pericoronary adipose tissue attenuation (PCAT). These indices independently predict adverse cardiovascular events beyond traditional risk factors. Serial imaging studies further demonstrate that lipid-lowering and anti-inflammatory therapies modulate plaque biology, promoting regression or stabilization. Integration of coronary plaque analytics, adipose tissue biology, and AI-driven risk prediction is redefining preventive cardiology and enabling increasingly individualized management strategies.
Mushtaq et al. (2026) studied this question.