Alavi-Carlsen Calcification Score (ACCS): A Simple Measure of Global Cardiac Atherosclerosis Burden.
Babak SabouryLars EdenbrandtReza PiriOke GerkeTom WernerArmin Arbab-ZadehAbass AlaviPoul Flemming Høilund-CarlsenPublished in: Diagnostics (Basel, Switzerland) (2021)
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.
Keyphrases
- pet ct
- positron emission tomography
- artificial intelligence
- left ventricular
- cardiovascular disease
- cardiovascular risk factors
- coronary artery disease
- coronary artery
- computed tomography
- deep learning
- chronic kidney disease
- emergency department
- machine learning
- randomized controlled trial
- heart failure
- magnetic resonance
- oxidative stress
- magnetic resonance imaging
- aortic stenosis
- risk factors
- metabolic syndrome
- convolutional neural network
- image quality
- aortic valve
- contrast enhanced
- clinical evaluation