Faculty of Medicine and University Hospital Cologne, Department for Diagnostic and Interventional Radiology, University of Cologne, Kerpener Strasse 62, 50937, Cologne, Germany.
Cancer- and Immunometabolism Research Group, Gene Center LMU, Ludwig-Maximilians-University, Munich, Germany.
Eur Radiol. 2020 Mar;30(3):1701-1708. doi: 10.1007/s00330-019-06526-9. Epub 2019 Nov 27.
To evaluate the correlation between simple planimetric measurements in axial computed tomography (CT) slices and measurements of patient body composition and anthropometric data performed with bioelectrical impedance analysis (BIA) and metric clinical assessments.
In this prospective cross-sectional study, we analyzed data of a cohort of 62 consecutive, untreated adult patients with advanced malignant melanoma who underwent concurrent BIA assessments at their radiologic baseline staging by CT between July 2016 and October 2017. To assess muscle and adipose tissue mass, we analyzed the areas of the paraspinal muscles as well as the cross-sectional total patient area in a single CT slice at the height of the third lumbar vertebra. These measurements were subsequently correlated with anthropometric (body weight) and body composition parameters derived from BIA (muscle mass, fat mass, fat-free mass, and visceral fat mass). Linear regression models were built to allow for estimation of each parameter based on CT measurements.
Linear regression models allowed for accurate prediction of patient body weight (adjusted R = 0.886), absolute muscle mass (adjusted R = 0.866), fat-free mass (adjusted R = 0.855), and total as well as visceral fat mass (adjusted R = 0.887 and 0.839, respectively).
Our data suggest that patient body composition can accurately and quantitatively be determined by using simple measurements in a single axial CT slice. This could be useful in various medical and scientific settings, where the knowledge of the patient's anthropometric parameters is not immediately or easily available.
• Easy to perform measurements on a single CT slice highly correlate with clinically valuable parameters of body composition. • Body composition data were acquired using bioelectrical impedance analysis to correlate CT measurements with a non-imaging-based method, which is frequently lacking in previous studies. • The obtained equations facilitate a quick, opportunistic assessment of relevant parameters of body composition.
评估轴向计算机断层扫描(CT)切片中的简单平面测量值与生物电阻抗分析(BIA)和计量临床评估所测量的患者身体成分和人体测量数据之间的相关性。
在这项前瞻性的横断面研究中,我们分析了 2016 年 7 月至 2017 年 10 月期间,62 例未经治疗的晚期恶性黑色素瘤成年患者的队列数据,这些患者在放射学基线分期时通过 CT 同时进行 BIA 评估。为了评估肌肉和脂肪组织量,我们在第三腰椎高度的单个 CT 切片中分析了脊柱旁肌肉的面积以及患者的横截面积。这些测量值随后与人体测量(体重)和 BIA 得出的身体成分参数(肌肉质量、脂肪质量、去脂体重和内脏脂肪质量)相关联。构建线性回归模型,以便能够根据 CT 测量值估计每个参数。
线性回归模型允许准确预测患者体重(调整后的 R = 0.886)、绝对肌肉质量(调整后的 R = 0.866)、去脂体重(调整后的 R = 0.855)以及总脂肪和内脏脂肪质量(调整后的 R 分别为 0.887 和 0.839)。
我们的数据表明,通过在单个轴向 CT 切片中使用简单的测量值,可以准确和定量地确定患者的身体成分。这在各种医学和科学环境中可能非常有用,在这些环境中,患者的人体测量参数的知识不是立即或容易获得的。
在单个 CT 切片上进行易于执行的测量值与身体成分的有临床价值的参数高度相关。
使用生物电阻抗分析获取身体成分数据,以将 CT 测量值与以前研究中经常缺乏的非成像方法相关联。
获得的方程有助于快速、偶然地评估身体成分的相关参数。