Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA; Department of Internal Medicine, Stanford University Hospital, Stanford, California, USA.
Texas A&M University, Engineering Medicine, Houston, Texas, USA.
J Am Coll Cardiol. 2024 Apr 23;83(16):1557-1567. doi: 10.1016/j.jacc.2024.01.039.
Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease risk stratification. The nongated, noncontrast chest computed tomography scan (NCCT) has emerged as a source of CAC characterization with tremendous potential due to the high volume of NCCT scans. Application of incidental CAC characterization from NCCT has raised questions around score accuracy, standardization of methodology including the possibility of deep learning to automate the process, and the risk stratification potential of an NCCT-derived score. In this review, the authors aim to summarize the role of NCCT-derived CAC in preventive cardiovascular health today as well as explore future avenues for eventual clinical applicability in specific patient populations and broader health systems.
冠状动脉钙(CAC)评分是一种用于动脉粥样硬化性心血管疾病风险分层的强大工具。非门控、非对比胸部计算机断层扫描(NCCT)已成为 CAC 特征描述的一种来源,由于 NCCT 扫描量很大,因此具有巨大的潜力。从 NCCT 中偶然发现 CAC 特征描述的应用引发了关于评分准确性、方法标准化的问题,包括使用深度学习使该过程自动化的可能性,以及 NCCT 衍生评分的风险分层潜力。在这篇综述中,作者旨在总结 NCCT 衍生 CAC 在当今预防心血管健康中的作用,并探讨未来在特定患者人群和更广泛的卫生系统中最终实现临床应用的途径。