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肌萎缩侧索硬化症中基于纹理分析的定量肌肉超声检查

Quantitative Muscle Ultrasonography Using Textural Analysis in Amyotrophic Lateral Sclerosis.

作者信息

Martínez-Payá Jacinto Javier, Ríos-Díaz José, Del Baño-Aledo María Elena, Tembl-Ferrairó Jose Ignacio, Vazquez-Costa Juan Francisco, Medina-Mirapeix Francesc

机构信息

1 Faculty of Health Sciences, Universidad Católica de Murcia, Murcia, Spain.

2 Centro de Ciencias de la Salud San Rafael, Universidad Antonio de Nebrija, Madrid, Spain.

出版信息

Ultrason Imaging. 2017 Nov;39(6):357-368. doi: 10.1177/0161734617711370. Epub 2017 May 28.

Abstract

The purpose of this study was to analyze differences in gray-level co-occurrence matrix (GLCM) parameters, as assessed by muscle ultrasound (MUS), between amyotrophic lateral sclerosis (ALS) patients and healthy controls, and to compare the diagnostic accuracy of these GLCM parameters with first-order MUS parameters (echointensity [EI], echovariation [EV], and muscle thickness [MTh]) in different muscle groups. Twenty-six patients with ALS and 26 healthy subjects underwent bilateral and transverse ultrasound of the biceps/brachialis, forearm flexor, quadriceps femoris, and tibialis anterior muscle groups. MTh was measured with electronic calipers, and EI, EV, and GLCM were obtained using Image J (v.1.48) software. Sensitivity, specificity, likelihood ratios, and area under the curve (AUC) were performed by logistic regression models and receiver operating characteristic curves. GLCM parameters showed reduced granularity in the muscles of ALS patients compared with the controls. Regarding the discrimination capacity, the best single diagnostic parameter in forearm flexors and quadriceps was GLCM and in biceps brachialis and tibialis anterior was EV. The respective combination of these two parameters with MTh resulted in the best AUC (over 90% in all muscle groups and close to the maximum combination model). The use of new textural parameters (EV and GLCM) combined with usual quantitative MUS variables is a promising biomarker in ALS.

摘要

本研究的目的是分析肌萎缩侧索硬化症(ALS)患者与健康对照者之间,通过肌肉超声(MUS)评估的灰度共生矩阵(GLCM)参数的差异,并比较这些GLCM参数与不同肌肉群中一阶MUS参数(回声强度[EI]、回声变化[EV]和肌肉厚度[MTh])的诊断准确性。26例ALS患者和26名健康受试者接受了肱二头肌/肱肌、前臂屈肌、股四头肌和胫骨前肌组的双侧横向超声检查。使用电子卡尺测量MTh,并使用Image J(v.1.48)软件获取EI、EV和GLCM。通过逻辑回归模型和受试者工作特征曲线进行敏感性、特异性、似然比和曲线下面积(AUC)分析。与对照组相比,GLCM参数显示ALS患者肌肉中的颗粒度降低。关于鉴别能力,在前臂屈肌和股四头肌中最佳的单一诊断参数是GLCM,在肱二头肌和胫骨前肌中是EV。这两个参数与MTh的各自组合产生了最佳的AUC(在所有肌肉群中超过90%,接近最大组合模型)。将新的纹理参数(EV和GLCM)与常用的定量MUS变量相结合,是ALS中有前景的生物标志物。

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