Eggelbusch Moritz, Weide Guido, Tieland Michael, Vergeer Renske N, Vos Luuk, van den Helder Jantine, van der Zwaard Stephan, Jaspers Richard T, Weijs Peter J M, Wüst Rob C I
Laboratory for Myology, Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands.
Faculty of Health, Sport and Physical Activity, Centre of Expertise Urban Vitality, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands.
Sci Rep. 2025 Aug 18;15(1):30186. doi: 10.1038/s41598-025-11437-5.
The assessment of skeletal muscle volume is valuable for fundamental research and clinical practice, but remains limited in larger cohorts due to its time-consuming nature. Here, we developed a method to accurately estimate vastus lateralis (VL) muscle volume based on a single measurement of anatomical cross-sectional area (ACSA) or tissue thickness. Sixty-nine healthy participants (20-91 years) volunteered. In a subgroup (n = 34) we measured VL volume and ACSAs at 10% intervals along the muscle length to derive a VL muscle shape factor. We subsequently estimated VL volume by multiplying this muscle shape factor with muscle length and a single measure of ACSA at 50% muscle length (ACSA) or an estimated ACSA from a single ultrasound scan of tissue thickness in an independent cohort (n = 35). VL muscle shape factor was determined by integrating a fourth-order polynomial of muscle length and ACSA, and was dependent on muscle size. Estimating muscle volume had a high accuracy (R²=0.976, CCC = 0.987), low bias and error (< 8.5%) in both the main cohort and an independent validation group. Estimating muscle volume from stitching 2D images at 50% muscle length or estimating ACSA with a geometric model explained 91-95% of variance in measured volumes, with high accuracy and concordance correlation coefficients. VL muscle volume can be estimated by multiplying a muscle shape factor with muscle length and ACSA from a single ultrasound image. We present a novel, cost-effective, rapid, yet accurate assessment of VL muscle mass for (large-scale) studies and clinical practice.
骨骼肌体积评估对于基础研究和临床实践具有重要价值，但由于其耗时性，在大规模队列研究中仍受到限制。在此，我们开发了一种方法，可基于解剖横截面积（ACSA）或组织厚度的单次测量准确估计股外侧肌（VL）体积。69名健康参与者（年龄20 - 91岁）自愿参与。在一个亚组（n = 34）中，我们沿肌肉长度以10%的间隔测量VL体积和ACSA，以得出VL肌肉形状因子。随后，我们通过将该肌肉形状因子与肌肉长度以及在肌肉长度50%处的ACSA单次测量值（ACSA）相乘，或者与独立队列（n = 35）中通过单次超声扫描组织厚度估计的ACSA相乘，来估计VL体积。VL肌肉形状因子通过整合肌肉长度和ACSA的四阶多项式来确定，并且依赖于肌肉大小。在主要队列和独立验证组中，估计肌肉体积都具有高精度（R² = 0.976，CCC = 0.987）、低偏差和误差（< 8.5%）。通过拼接肌肉长度50%处的二维图像估计肌肉体积或使用几何模型估计ACSA，可解释测量体积中91 - 95%的方差，具有高精度和一致性相关系数。VL肌肉体积可通过将肌肉形状因子与肌肉长度以及来自单次超声图像的ACSA相乘来估计。我们提出了一种新颖、经济高效、快速且准确的方法，用于（大规模）研究和临床实践中评估VL肌肉质量。
