Ruohola Arttu, Salli Eero, Roine Timo, Tokola Anna, Laine Minna, Tikkanen Ritva, Savolainen Sauli, Autti Taina
HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00290 Helsinki, Finland.
Department of Neuroscience and Biomedical Engineering, Aalto University, P.O Box 11000, FI-02150 Espoo, Finland.
Brain Sci. 2022 Nov 10;12(11):1522. doi: 10.3390/brainsci12111522.
Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients ( = 22) and healthy controls ( = 24) using volumetric features extracted from T1-weighted MR images, the zone variance of gray level size zone matrix (GLSZM) calculated from magnitude susceptibility-weighted MR images, and the caudate-thalamus intensity ratio computed from T2-weighted MR images. The leave-one-out cross-validation and area under the receiver operating characteristic curve were used to compare different models. The left-right-averaged, normalized volumes of the 25 nuclei of the thalamus and the zone variance of the thalamus demonstrated equal and excellent performance as classifier features for binary organization between AGU patients and healthy controls. Our findings show that texture-based features of susceptibility-weighted images and thalamic volumes can differentiate AGU patients from healthy controls with a very low error rate.
磁共振（MR）成像数据可用于开发针对神经退行性疾病（如天冬氨酰葡糖胺尿症（AGU）和其他溶酶体贮积症）的计算机辅助诊断工具。MR图像包含适合区分患病个体与健康个体的特征。在此，对从不同类型磁共振图像中提取的MRI特征进行了比较。使用从T1加权MR图像中提取的体积特征、从幅度敏感性加权MR图像计算的灰度级大小区域矩阵（GLSZM）的区域方差以及从T2加权MR图像计算的尾状核 - 丘脑强度比，训练随机森林分类器对AGU患者（n = 22）和健康对照（n = 24）进行分类。采用留一法交叉验证和受试者操作特征曲线下面积来比较不同模型。丘脑25个核的左右平均归一化体积以及丘脑的区域方差作为AGU患者与健康对照之间二元分类的特征表现出同等且优异的性能。我们的研究结果表明，基于纹理的敏感性加权图像特征和丘脑体积能够以非常低的错误率区分AGU患者与健康对照。
