Chronic kidney disease (CKD) is characterized by progressive reductions in skeletal muscle function and size. The concept of muscle quality is increasingly being used to assess muscle health, although the best means of assessment remains unidentified. The use of muscle echogenicity is limited by an inability to be compared across devices. Gray level of co-occurrence matrix (GLCM), a form of image texture analysis, may provide a measure of muscle quality, robust to scanner settings. This study aimed to identify GLCM values from skeletal muscle images in CKD and investigate their association with physical performance and strength (a surrogate of muscle function). Transverse images of the rectus femoris muscle were obtained using B-mode 2D ultrasound imaging. Texture analysis (GLCM) was performed using ImageJ. Five different GLCM features were quantified: energy or angular second moment (ASM), entropy, homogeneity, or inverse difference moment (IDM), correlation, and contrast. Physical function and strength were assessed using tests of handgrip strength, sit to stand-60, gait speed, incremental shuttle walk test, and timed up-and-go. Correlation coefficients between GLCM indices were compared to each objective functional measure. A total of 90 CKD patients (age 64.6 (10.9) years, 44% male, eGFR 33.8 (15.7) mL/minutes/1.73 m²) were included. Better muscle function was largely associated with those values suggestive of greater image texture homogeneity (i.e., greater ASM, correlation, and IDM, lower entropy and contrast). Entropy showed the greatest association across all the functional assessments (r = −.177). All GLCM parameters, a form of higher-order texture analysis, were associated with muscle function, although the largest association as seen with image entropy. Image homogeneity likely indicates lower muscle infiltration of fat and fibrosis. Texture analysis may provide a novel indicator of muscle quality that is robust to changes in scanner settings. Further research is needed to substantiate our findings.

慢性肾病（CKD）的特点是骨骼肌功能和大小进行性下降。肌肉质量的概念越来越多地被用来评估肌肉健康，尽管最佳的评估方法仍然未知。肌肉回声的使用受到无法跨设备比较的限制。灰度共生矩阵 (GLCM) 是图像纹理分析的一种形式，可以提供肌肉质量的测量，并且对扫描仪设置具有鲁棒性。本研究旨在从 CKD 骨骼肌图像中识别 GLCM 值，并研究它们与身体表现和力量（肌肉功能的替代指标）的关联。使用 B 型 2D 超声成像获得股直肌的横向图像。使用 ImageJ 进行纹理分析 (GLCM)。量化了五种不同的 GLCM 特征：能量或角二阶矩 (ASM)、熵、均匀性或反差矩 (IDM)、相关性和对比度。通过握力测试、坐立 60 度测试、步态速度测试、增量穿梭步行测试和计时起立行走测试来评估身体功能和力量。将 GLCM 指数之间的相关系数与每个目标函数测量值进行比较。总共包括 90 名 CKD 患者（年龄 64.6 (10.9) 岁，44% 男性，eGFR 33.8 (15.7) mL/分钟/1.73 m 2 ）^。更好的肌肉功能很大程度上与那些暗示更大的图像纹理同质性的值相关（即更大的 ASM、相关性和 IDM、更低的熵和对比度）。熵在所有功能评估中显示出最大的关联性 ( r  = −.177)。所有 GLCM 参数（一种高阶纹理分析形式）都与肌肉功能相关，尽管与图像熵的关联最大。图像均匀性可能表明脂肪和纤维化的肌肉浸润较低。纹理分析可以提供一种新颖的肌肉质量指标，该指标对扫描仪设置的变化具有鲁棒性。需要进一步的研究来证实我们的发现。