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Pericoronary Adipose Tissue Imaging: Quantitative Assessment, Artificial Intelligence Integration, and Therapeutic
Leanne Eveson1, Ikboljon Sobirov1, Kenneth Chan1
1Acute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
The British Journal of Radiology
|February 20, 2026
Summary
Pericoronary adipose tissue (PCAT) shows promise as a biosensor for vascular inflammation. Standardized imaging and AI can enhance its use for diagnosing and managing coronary artery disease (CAD).
Area of Science:
- Cardiovascular Imaging
- Biomarker Discovery
- Artificial Intelligence in Medicine
Background:
- Pericoronary adipose tissue (PCAT) is recognized as a marker of vascular inflammation.
- Coronary computed tomography angiography (CCTA) enables quantitative PCAT assessment for evaluating inflammatory burden in coronary artery disease (CAD).
Purpose of the Study:
- To review the anatomical and physiological basis of PCAT as a clinical biomarker.
- To highlight the importance of standardizing PCAT imaging and explore the role of AI in its application.
- To discuss the potential of PCAT imaging in diagnosis, risk stratification, and treatment monitoring for atherosclerotic cardiovascular disease.
Main Methods:
- Review of existing literature on PCAT imaging, standardization techniques, and AI applications.
- Examination of the Fat-Attenuation Index (FAI) Score as a metric for coronary inflammation.
- Discussion of emerging evidence on therapeutic modulation of FAI Score and serial imaging utility.
Main Results:
- Standardized PCAT imaging, particularly the FAI Score, shows potential for quantifying coronary inflammation.
- Artificial intelligence can improve the precision and scalability of PCAT analysis.
- Emerging data suggest FAI Score can be modulated by various therapeutic agents.
Conclusions:
- PCAT imaging, enhanced by AI, is poised to complement traditional CAD risk factors and plaque assessment.
- Standardization is crucial for PCAT to become a reliable clinical biomarker.
- Inflammatory risk assessment using PCAT may guide personalized cardiovascular medicine and anti-inflammatory treatments.
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