Qin Kezhen, Chen Wen, Qi Hengtao, Wang Tiezheng, Wang Yeting, Zhang Huawei, Teng Jianbo
Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Quant Imaging Med Surg. 2025 Sep 1;15(9):7885-7895. doi: 10.21037/qims-2025-638. Epub 2025 Aug 19.
Sarcopenia, an age-related condition marked by progressive muscle loss and dysfunction, is a growing clinical and public health challenge. While current diagnostic methods involve limitations in cost, accessibility, and assessment of muscle quality, ultrasound offers a practical alternative. This study examined grayscale histogram analysis of gastrocnemius muscle ultrasound images as a novel quantitative method for diagnosing sarcopenia by evaluating its ability to detect textural changes associated with intramuscular fat infiltration and fibrosis, with the ultimate aim of establishing an accurate, accessible diagnostic approach.
A retrospective case-control study was conducted on 101 patients diagnosed with sarcopenia who were admitted to the Department of Endocrinology at Shandong Provincial Hospital between March and December 2024. Additionally, 101 healthy volunteers who underwent health examinations in our hospital during the same period were recruited as the control group. Grayscale histogram parameters, including the minimum gray value, maximum gray value, median gray value, mean gray value, standard deviation of gray values, skewness, kurtosis, and the gray values corresponding to seven percentile points (quantile 5, quantile 10, quantile 25, quantile 50, quantile 75, quantile 90, quantile 95) were extracted from the ultrasound images of the participants' gastrocnemius muscles. Statistical methods were used to analyze the differences between the sarcopenia and control groups. Receiver operating characteristic (ROC) curves were used to compare the differential diagnostic efficacy of each parameter and their combinations. Linear regression and least absolute shrinkage and selection operator (LASSO) were used to predict the probability of sarcopenia, with model performance evaluated with R values and the mean square error.
The grayscale histogram parameters of the gastrocnemius ultrasound images in the sarcopenia group, including the minimum gray value, maximum gray value, median gray value, mean gray value, standard deviation of gray values, and the gray values corresponding to seven percentile points, were significantly higher than those in the control group (P<0.001), while both the skewness and kurtosis were smaller than those in the control group (P<0.001). The gray value corresponding to quantile 75 demonstrated the best diagnostic efficacy [area under the curve (AUC) =0.988, sensitivity =96%, specificity =95%] at a cutoff of 132.5. The LASSO regression model outperformed linear regression (test set: R =0.769 0.727; mean square error =0.057 0.068).
The grayscale histogram parameters extracted from ultrasound images may be able to quantitatively reflect the differences between patients with sarcopenia and healthy individuals to some extent. Grayscale histogram analysis based on ultrasound images could be valuable for the diagnosis of sarcopenia.
肌肉减少症是一种与年龄相关的疾病，其特征为进行性肌肉流失和功能障碍，是一个日益严峻的临床和公共卫生挑战。虽然目前的诊断方法在成本、可及性和肌肉质量评估方面存在局限性，但超声提供了一种实用的替代方法。本研究通过评估腓肠肌超声图像的灰度直方图分析检测与肌内脂肪浸润和纤维化相关的纹理变化的能力，将其作为诊断肌肉减少症的一种新型定量方法，最终目的是建立一种准确、可及的诊断方法。
对2024年3月至12月在山东省立医院内分泌科住院的101例诊断为肌肉减少症的患者进行回顾性病例对照研究。此外，同期在我院进行健康体检的101名健康志愿者作为对照组。从参与者腓肠肌的超声图像中提取灰度直方图参数，包括最小灰度值、最大灰度值、中位数灰度值、平均灰度值、灰度值标准差、偏度、峰度以及七个百分位点（分位数5、分位数10、分位数25、分位数50、分位数75、分位数90、分位数95）对应的灰度值。采用统计方法分析肌肉减少症组与对照组之间的差异。采用受试者操作特征（ROC）曲线比较各参数及其组合的鉴别诊断效能。使用线性回归和最小绝对收缩和选择算子（LASSO）预测肌肉减少症的概率，用R值和均方误差评估模型性能。
肌肉减少症组腓肠肌超声图像的灰度直方图参数，包括最小灰度值、最大灰度值、中位数灰度值、平均灰度值、灰度值标准差以及七个百分位点对应的灰度值，均显著高于对照组（P<0.001），而偏度和峰度均小于对照组（P<0.001）。在截断值为132.5时，分位数75对应的灰度值显示出最佳诊断效能[曲线下面积（AUC）=0.988，灵敏度=96%，特异度=95%]。LASSO回归模型优于线性回归（测试集：R=0.769对0.727；均方误差=0.057对0.068）。
从超声图像中提取的灰度直方图参数可能在一定程度上能够定量反映肌肉减少症患者与健康个体之间的差异。基于超声图像的灰度直方图分析对肌肉减少症的诊断可能具有重要价值。
