The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI, 53792-3252, USA.
Abdom Radiol (NY). 2021 Mar;46(3):1229-1235. doi: 10.1007/s00261-020-02755-5. Epub 2020 Sep 18.
Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures.
Initial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r) was generated for each comparison.
Mean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm, 18.8 ± 6.0 HU, - 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r = 0.72), MA (pre = 0.98 × post; r = 0.92), M-HU (pre = 0.75 × post + 5.7; r = 0.75), and V/S (pre = 1.11 × post; r = 0.94); p < 0.001 for all r values. There were no significant trends according to patient age or gender that required further correction.
Fully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.
基于 CT 的全自动算法已被验证可用于定量评估未增强腹部扫描的骨量、肌肉量和脂肪量。本研究旨在确定并校正静脉（IV）对比剂对这些全自动体成分测量的影响。
初始研究队列由 1211 名接受腹部 CT 检查以评估潜在肾捐献的健康成年人组成（平均年龄 45.2 岁；733 名女性）。多期 CT 方案包括平扫期、动脉期和实质期。全自动 CT 算法用于定量测量骨密度（L1 松质骨 HU）、肌肉面积和密度（L3 水平 MA 和 M-HU）以及脂肪（内脏/皮下（V/S）脂肪比），并应用于平扫期和实质期。分析 IV 对比剂对这些体成分测量的影响。对于每项比较，均生成 Pearson 相关系数（r）的平方。
L1 骨密度、L3 水平 MA 和 M-HU 以及 V/S 脂肪比的平均变化（±SD）分别为 26.7±27.2 HU、2.9±10.2 cm、18.8±6.0 HU、-0.1±0.2。所有自动测量值的平扫期与增强后期值之间均呈良好的线性相关性：骨密度（平扫期=0.87×增强后期；r=0.72）、MA（平扫期=0.98×增强后期；r=0.92）、M-HU（平扫期=0.75×增强后期+5.7；r=0.75）以及 V/S（平扫期=1.11×增强后期；r=0.94）；所有 r 值的 p<0.001。根据患者年龄或性别，没有观察到需要进一步校正的显著趋势。
对比增强腹部 CT 全自动定量组织测量的骨量、肌肉量和脂肪量可以通过简单的线性关系与非对比剂等效物相关联。这些发现将有助于评估包含更大患者群体的混合 CT 队列，并可能极大地扩大机会性筛查的潜力。
