利用自动化人工智能工具预测 10 年不良结局的腹部 CT 体成分阈值。

Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes.

机构信息

From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G., P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.).

出版信息

Radiology. 2023 Feb;306(2):e220574. doi: 10.1148/radiol.220574. Epub 2022 Sep 27.

DOI:10.1148/radiol.220574

PMID:36165792

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9885340/

Abstract

Background CT-based body composition measures derived from fully automated artificial intelligence tools are promising for opportunistic screening. However, body composition thresholds associated with adverse clinical outcomes are lacking. Purpose To determine population and sex-specific thresholds for muscle, abdominal fat, and abdominal aortic calcium measures at abdominal CT for predicting risk of death, adverse cardiovascular events, and fragility fractures. Materials and Methods In this retrospective single-center study, fully automated algorithms for quantifying skeletal muscle (L3 level), abdominal fat (L3 level), and abdominal aortic calcium were applied to noncontrast abdominal CT scans from asymptomatic adults screened from 2004 to 2016. Longitudinal follow-up documented subsequent death, adverse cardiovascular events (myocardial infarction, cerebrovascular event, and heart failure), and fragility fractures. Receiver operating characteristic (ROC) curve analysis was performed to derive thresholds for body composition measures to achieve optimal ROC curve performance and high specificity (90%) for 10-year risks. Results A total of 9223 asymptomatic adults (mean age, 57 years ± 7 [SD]; 5152 women and 4071 men) were evaluated (median follow-up, 9 years). Muscle attenuation and aortic calcium had the highest diagnostic performance for predicting death, with areas under the ROC curve of 0.76 for men (95% CI: 0.72, 0.79) and 0.72 for women (95% CI: 0.69, 0.76) for muscle attenuation. Sex-specific thresholds were higher in men than women ( < .001 for muscle attenuation for all outcomes). The highest-performing markers for risk of death were muscle attenuation in men (31 HU; 71% sensitivity [164 of 232 patients]; 72% specificity [1114 of 1543 patients]) and aortic calcium in women (Agatston score, 167; 70% sensitivity [152 of 218 patients]; 70% specificity [1427 of 2034 patients]). Ninety-percent specificity thresholds for muscle attenuation for both risk of death and fragility fractures were 23 HU (men) and 13 HU (women). For aortic calcium and risk of death and adverse cardiovascular events, 90% specificity Agatston score thresholds were 1475 (men) and 735 (women). Conclusion Sex-specific thresholds for automated abdominal CT-based body composition measures can be used to predict risk of death, adverse cardiovascular events, and fragility fractures. © RSNA, 2022 See also the editorial by Ohliger in this issue.

摘要

背景基于全自动人工智能工具的 CT 基础身体成分测量有望用于机会性筛查。然而，与不良临床结局相关的身体成分阈值尚不清楚。目的确定用于预测死亡、不良心血管事件和脆性骨折风险的腹部 CT 上肌肉、腹部脂肪和腹主动脉钙测量的人群和性别特异性阈值。材料与方法本回顾性单中心研究中，应用全自动算法对 2004 年至 2016 年间筛查的无症状成年人的非对比腹部 CT 扫描进行定量分析，以评估骨骼肌（L3 水平）、腹部脂肪（L3 水平）和腹主动脉钙。纵向随访记录了随后的死亡、不良心血管事件（心肌梗死、脑血管事件和心力衰竭）和脆性骨折。进行受试者工作特征（ROC）曲线分析，以得出身体成分测量的阈值，以获得最佳 ROC 曲线性能和 10 年风险的高特异性（90%）。结果共评估了 9223 名无症状成年人（平均年龄 57 岁±7[标准差]；5152 名女性和 4071 名男性）（中位随访 9 年）。肌肉衰减和主动脉钙对预测死亡具有最高的诊断性能，男性的 ROC 曲线下面积为 0.76（95%CI：0.72，0.79），女性为 0.72（95%CI：0.69，0.76）。男性的性别特异性阈值高于女性（所有结果的肌肉衰减均<.001）。死亡风险最高的标志物是男性的肌肉衰减（31 HU；71%的敏感性[232 例患者中的 164 例]；72%的特异性[1543 例患者中的 1114 例]）和女性的主动脉钙（Agatston 评分，167；70%的敏感性[218 例患者中的 152 例]；70%的特异性[2034 例患者中的 1427 例]）。肌肉衰减预测死亡和脆性骨折的 90%特异性阈值分别为 23 HU（男性）和 13 HU（女性）。对于主动脉钙和死亡及不良心血管事件的风险，90%特异性 Agatston 评分阈值分别为 1475（男性）和 735（女性）。结论基于全自动腹部 CT 的身体成分测量的性别特异性阈值可用于预测死亡、不良心血管事件和脆性骨折的风险。