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

Abstract

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.

Keywords: PET/CT; aorta; artificial intelligence; atherosclerosis; calcium; heart; imaging; microcalcification; score; sodium fluoride.

Figure 1

Axial (a), coronal (b), and sagittal (c) reconstruction of AI-based cardiac segmentation in the same patient and 2D representation of the 3D reconstructed heart using AI (d).