Chiou Hong-Jen, Yeh Chih-Kuang, Hwang Hsuen-En, Liao Yin-Yin
Division of Ultrasound and Breast Imaging, Department of Radiology, Taipei Veterans General Hospital, Taipei 11217, Taiwan.
School of Medicine, National Yang Ming University, Taipei 11221, Taiwan.
Entropy (Basel). 2019 Jul 22;21(7):714. doi: 10.3390/e21070714.
Pompe disease is a hereditary neuromuscular disorder attributed to acid α-glucosidase deficiency, and accurately identifying this disease is essential. Our aim was to discriminate normal muscles from neuropathic muscles in children affected by Pompe disease using a texture-feature parametric imaging method that simultaneously considers microstructure and macrostructure. The study included 22 children aged 0.02-54 months with Pompe disease and six healthy children aged 2-12 months with normal muscles. For each subject, transverse ultrasound images of the bilateral rectus femoris and sartorius muscles were obtained. Gray-level co-occurrence matrix-based Haralick's features were used for constructing parametric images and identifying neuropathic muscles: autocorrelation (AUT), contrast, energy (ENE), entropy (ENT), maximum probability (MAXP), variance (VAR), and cluster prominence (CPR). Stepwise regression was used in feature selection. The Fisher linear discriminant analysis was used for combination of the selected features to distinguish between normal and pathological muscles. The VAR and CPR were the optimal feature set for classifying normal and pathological rectus femoris muscles, whereas the ENE, VAR, and CPR were the optimal feature set for distinguishing between normal and pathological sartorius muscles. The two feature sets were combined to discriminate between children with and without neuropathic muscles affected by Pompe disease, achieving an accuracy of 94.6%, a specificity of 100%, a sensitivity of 93.2%, and an area under the receiver operating characteristic curve of 0.98 ± 0.02. The CPR for the rectus femoris muscles and the AUT, ENT, MAXP, and VAR for the sartorius muscles exhibited statistically significant differences in distinguishing between the infantile-onset Pompe disease and late-onset Pompe disease groups ( < 0.05). Texture-feature parametric imaging can be used to quantify and map tissue structures in skeletal muscles and distinguish between pathological and normal muscles in children or newborns.
庞贝病是一种由于酸性α-葡萄糖苷酶缺乏引起的遗传性神经肌肉疾病,准确识别这种疾病至关重要。我们的目的是使用一种同时考虑微观结构和宏观结构的纹理特征参数成像方法,区分受庞贝病影响儿童的正常肌肉和神经病变肌肉。该研究纳入了22名年龄在0.02 - 54个月的庞贝病患儿以及6名年龄在2 - 12个月的肌肉正常的健康儿童。对每个受试者获取双侧股直肌和缝匠肌的横向超声图像。基于灰度共生矩阵的哈勒克特征用于构建参数图像并识别神经病变肌肉:自相关(AUT)、对比度、能量(ENE)、熵(ENT)、最大概率(MAXP)、方差(VAR)和聚类突出度(CPR)。特征选择采用逐步回归。费希尔线性判别分析用于将所选特征进行组合,以区分正常肌肉和病变肌肉。VAR和CPR是用于区分正常和病变股直肌的最佳特征集,而ENE、VAR和CPR是区分正常和病变缝匠肌的最佳特征集。将这两个特征集组合起来区分受庞贝病影响有无神经病变肌肉的儿童,准确率达到94.6%,特异性为100%,敏感性为93.2%,受试者工作特征曲线下面积为0.98±0.02。股直肌的CPR以及缝匠肌的AUT、ENT、MAXP和VAR在区分婴儿型庞贝病和晚发型庞贝病组时表现出统计学显著差异(<0.05)。纹理特征参数成像可用于量化和绘制骨骼肌组织结构,并区分儿童或新生儿的病变肌肉和正常肌肉。