Alavi–Carlsen Calcification Score (ACCS): A Simple Measure of Global Cardiac Atherosclerosis Burden

Diagnostics

Babak Saboury¹  Lars Edenbrandt²  Oke Gerke³  Poul Flemming Høilund-Carlsen³  Reza Piri³  Tom Werner⁴  Abass Alavi⁴  Armin Arbab-Zadeh⁵ 

[1]Clinical Center, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD 20892, USA

[2]Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden

[3]Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark

[4]Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA

[5]Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA

关键词: aorta;    artificial intelligence;    atherosclerosis;    calcium;    heart;    imaging;   

DOI  :  10.3390/diagnostics11081421

来源: DOAJ

【 摘 要 】

Multislice cardiac CT characterizes late stage macrocalcification in epicardial arteries as opposed to PET/CT, which mirrors early phase arterial wall changes in epicardial and transmural coronary arteries. With regard to tracer, there has been a shift from using mainly ¹⁸F-fluorodeoxyglucose (FDG), indicating inflammation, to applying predominantly ¹⁸F-sodium fluoride (NaF) due to its high affinity for arterial wall microcalcification and more consistent association with cardiovascular risk factors. To make NaF-PET/CT an indispensable adjunct to clinical assessment of cardiac atherosclerosis, the Alavi–Carlsen Calcification Score (ACCS) has been proposed. It constitutes a global assessment of cardiac atherosclerosis burden in the individual patient, supported by an artificial intelligence (AI)-based approach for fast observer-independent segmentation. Common measures for characterizing epicardial coronary atherosclerosis by NaF-PET/CT as the maximum standardized uptake value (SUV) or target-to-background ratio are more versatile, error prone, and less reproducible than the ACCS, which equals the average cardiac SUV. The AI-based approach ensures a quick and easy delineation of the entire heart in 3D to obtain the ACCS expressing ongoing global cardiac atherosclerosis, even before it gives rise to CT-detectable coronary calcification. The quantification of global cardiac atherosclerotic burden by the ACCS is suited for management triage and monitoring of disease progression with and without intervention.

【 授权许可】

Unknown