From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., A.A.P., M.G.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.).
Radiographics. 2021 Mar-Apr;41(2):524-542. doi: 10.1148/rg.2021200056.
Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. RSNA, 2021.
腹部 CT 是一种常用于多种临床适应证的影像学检查。除了扫描的直接原因外，每次 CT 检查还包含丰富的关于身体成分的额外数据，这些数据在常规临床实践中通常未被使用。现在，人们越来越关注利用这些额外的信息。心脏代谢信息的主要示例包括用于骨质疏松症筛查的骨密度测量、用于评估心血管风险的主动脉钙定量、用于评估代谢综合征的内脏脂肪定量、用于诊断肌肉减少症的肌肉量和密度评估，以及用于评估肝脂肪变性的肝脂肪定量。所有这些相关的生物计量测量现在都可以通过使用人工智能算法来实现全自动，这提供了快速和客观的评估，并允许基于人群的大规模筛查。这些身体成分测量方法的初步研究表明，其对未来不良事件的预测性能与最佳的现有临床预测模型相当或超过，特别是当这些基于 CT 的测量方法联合使用时。在这篇综述中，讨论了基于 CT 的机会性筛查的概念，并概述了可以从几乎所有腹部 CT 检查中得出的各种自动生物标志物，这主要借鉴了作者的经验。随着放射学从基于量的实践向基于价值的实践转变，机会性筛查是为已经提供的服务增加价值的一个很有前途的范例。如果这些客观的基于 CT 的自动测量方法具有潜在的高附加值，在后续研究中得到证实，那么这种机会性筛查方法可以考虑用于有针对性的基于 CT 的筛查。RSNA，2021 年。
