Zhou Ke, Liu Zhou, He Wenguang, Cai Jie, Hu Lingjing
School of Biomedical Engineering, Guangdong Medical University, Zhanjiang, 524023, China.
Department of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524023, China.
Ther Innov Regul Sci. 2022 Jul;56(4):561-571. doi: 10.1007/s43441-021-00373-x. Epub 2022 Mar 27.
Patients with mild cognitive impairment (MCI) are a high-risk group for Alzheimer's disease (AD). Thus, a reliable prediction of the conversion from MCI to AD based on three-dimensional (3D) texture features of MRI images could help doctors in developing effective treatment protocols.
The 3D texture features of the whole-brain were deduced based on the gray-level co-occurrence matrix. Then, the embedded feature selection method based on least squares loss and within-class scatter (LSWCS) was employed to select the optimal subsets of features that were used for binary classification (AD, MCI_C, MCI_S, normal control in pairs) based on SVM. A tenfold cross validation was repeated ten times for each classification. LASSO, fused_LASSO, and group LASSO are used in feature selection step for comparison.
The accuracy and the selected features are the focus of clinical diagnosis reports, indicating that the feature selection algorithm is effective.
轻度认知障碍（MCI）患者是阿尔茨海默病（AD）的高危人群。因此，基于磁共振成像（MRI）图像的三维（3D）纹理特征对MCI向AD的转化进行可靠预测，有助于医生制定有效的治疗方案。
基于灰度共生矩阵推导全脑的3D纹理特征。然后，采用基于最小二乘损失和类内散度（LSWCS）的嵌入式特征选择方法，选择最优特征子集，基于支持向量机（SVM）用于二元分类（AD、MCI_C、MCI_S、正常对照两两分类）。每种分类重复十次十折交叉验证。在特征选择步骤中使用套索（LASSO）、融合套索（fused_LASSO）和组套索（group LASSO）进行比较。
准确率和所选特征是临床诊断报告的重点，表明特征选择算法是有效的。
