From the Faculty of Medicine (K.S.) and Department of Neurology (H.N., N.T., A.M., H.Y., Y.S., Y.I., R.K.), Tokushima University, 3-18-15 Kuramotocho, Tokushima 770-8503, Japan; and Department of Neurology, Vihara Hananosato Hospital, Hiroshima, Japan (N.T., Y.I.).
Radiology. 2017 May;283(2):492-498. doi: 10.1148/radiol.2016160826. Epub 2017 Feb 2.
Purpose To assess the multiple texture features of skeletal muscles in neurogenic and myogenic diseases by using ultrasonography (US). Materials and Methods After institutional review board approval, muscle US studies of the medial head of the gastrocnemius were performed prospectively in patients with neurogenic diseases (n = 25 [18 men]; mean age, 66.0 years ± 12.3 [standard deviation]), in patients with myogenic diseases (n = 21 [12 men]; mean age, 68.3 years ± 11.5), and in healthy control subjects (n = 21 [11 men]; mean age, 70.5 years ± 8.4) between January 2013 and May 2016. Written informed consent was obtained. Muscle texture parameters were obtained, and five algorithms were used to classify the groups. Results The neurogenic and myogenic disease groups showed higher echo intensities than the control subjects. The histogram-derived texture parameters had overlaps between the neurogenic and myogenic groups and thus had a low discrimination rate. With assessment of more classes of texture parameters, three groups were correctly classified (100% correct, according to four of five classification algorithms). Tenfold cross validation showed 93.5%-95.7% correct classification between the neurogenic and myogenic groups. The run-length matrix, autoregressive model, and co-occurrence matrix were particularly useful in distinguishing the neurogenic and myogenic groups. Conclusion Texture analysis of muscle US data can enable differentiation between neurogenic and myogenic diseases and is useful in noninvasively assessing underlying disease mechanisms. RSNA, 2017 Online supplemental material is available for this article.
目的 利用超声（US）评估神经源性和肌源性疾病中骨骼肌的多种纹理特征。
材料与方法 本研究经机构审查委员会批准，前瞻性地对 2013 年 1 月至 2016 年 5 月间患有神经源性疾病（25 例患者[18 例男性]；平均年龄 66.0 岁±12.3）、肌源性疾病（21 例患者[12 例男性]；平均年龄 68.3 岁±11.5）和健康对照者（21 例患者[11 例男性]；平均年龄 70.5 岁±8.4）的内侧腓肠肌进行肌肉 US 研究。所有患者均获得书面知情同意。获取肌肉纹理参数，并使用 5 种算法对组进行分类。
结果 神经源性和肌源性疾病组的回声强度高于对照组。基于直方图的纹理参数在神经源性和肌源性组之间存在重叠，因此具有较低的鉴别率。通过评估更多类别的纹理参数，可将 3 组正确分类（根据 5 种分类算法中的 4 种算法，分类正确率为 100%）。10 折交叉验证显示神经源性和肌源性组之间的分类正确率为 93.5%-95.7%。运行长度矩阵、自回归模型和共生矩阵特别有助于区分神经源性和肌源性组。
结论 肌肉 US 数据的纹理分析可区分神经源性和肌源性疾病，有助于非侵入性评估潜在的疾病机制。
RSNA，2017 在线补充材料可在本文中查看。
