Pericoronary adipose tissue imaging: quantitative assessment, artificial intelligence integration, and therapeutic modulation

Pericoronary adipose tissue (PCAT) is increasingly recognised as a biosensor of vascular inflammation. The guideline-driven widespread adoption of coronary computed tomography angiography (CCTA) as the first-line investigation for coronary artery disease (CAD) has created opportunities for evaluating the inflammatory burden through quantitative assessment of PCAT. Standardising raw PCAT imaging data for technical, anatomical, and biological variability provides the Fat-Attenuation Index (FAI) Score, which shows promise as a metric of coronary inflammation. Quantification of coronary inflammation has implications for the diagnosis, risk stratification, and monitoring of treatment in atherosclerotic cardiovascular disease. This review examines the anatomical and physiological basis of PCAT, highlighting the importance of standardising PCAT imaging for the implementation as a clinical biomarker, and reviews the role of artificial intelligence (AI) in enhancing precision and scalability. Emerging evidence on the modulation of FAI Score by therapeutic agents, including statins, biologics, and cardiometabolic drugs, and the potential utility of serial imaging in guiding clinical care is also discussed. With ongoing large-scale validation and emerging AI -based approaches, PCAT imaging is poised to complement traditional risk factors and plaque metrics; however, current evidence remains evolving, and the integration of inflammatory risk assessment could be useful to guide emerging anti-inflammatory treatments in personalised cardiovascular medicine.