Abstract
Abstract
Coronary artery calcium (CAC) can be identified on thoracic CT performed for a variety of clinical indications, including routine thoracic CT, lung cancer screening CT, and attenuation correction CT. Identification of CAC on thoracic CT as part of opportunistic screening can improve the assessment of cardiovascular risk and can identify patients who may benefit from preventative medical therapies. This approach enhances early detection of coronary artery disease within existing healthcare workflows. However, at present, there are no randomized trials that assess the clinical impact of this approach. Recently, the development of artificial intelligence/machine learning (AI/ML) models has facilitated the automated identification and quantification of CAC, offering rapid and reproducible assessments. Artificial intelligence/machine learning models could reduce radiologist workload and provide end-to-end integration into routine clinical practice. Novel technologies such as photon-counting CT offer further potential for CAC detection and risk stratification through the use of high-resolution imaging and spectral imaging with virtual non-contrast images. This review discusses the evidence for opportunistic screening for CAC on thoracic CT and highlights novel applications including AI/ML assessments, CAC as a multi-system biomarker, and the potential for photon counting CT.