基于海马亚区放射组学特征的颞叶癫痫分类

    Classification of Temporal Lobe Epilepsy Based on Radiomic Features of Hippocampal Subregions

    • 摘要: 研究海马亚区的放射组学特征是否可以作为诊断颞叶癫痫(temporal lobe epilepsy, TLE)患者的生物学标志物,并探索某些放射组学特征在分类中的重要性. 实验纳入23例TLE患者和30例健康对照(healthy controls, HCs),对所有受试者进行结构磁共振成像(structural magnetic resonance imaging, sMRI)扫描,利用Freesurfer 7.2软件自动分割出海马亚区,3D slicer软件提取出每个亚区的放射组学特征,经过特征选择后采用支持向量机(support vector machine,SVM)对TLE组和HCs组进行分类. 左侧海马体部齿状回颗粒细胞层(GC_ML_DG-body)的分类准确度最高,为79.25%;右侧海马头部的分子层(Molecular_layer_HP-head)的分类准确度最高,为79.25%. 影响分类结果的重要特征中,二阶特征居多,其次是一阶特征和形状特征. 海马亚区的放射组学特征有望作为生物学标志物识别颞叶癫痫,其中二阶特征是用于颞叶癫痫分类的重要特征.

       

      Abstract: To investigate whether radiomic features of hippocampal subregion can be used as biomarkers for the diagnosis of temporal lobe epilepsy (TLE) patients, and to explore the importance of some radiomics features in classification, structural magnetic resonance imaging (sMRI) was performed on 23 TLE patients and 30 healthy controls (HCs) subjects. Freesurfer 7.2 software was used for segmentation of hippocampal subregions. Radiomics features of hippocampal subregion were extracted by using 3D slicer software. After feature selection, support vector machine (SVM) was employed for the classification between TLE patients and HCs.The granule cell layer of dentate gyrus in the body of left hippocampus (GC_ML_DG-body) reached highest accuracy of 79.25%. The molecular_layer_HP in the head of right hippocampus (Molecular_layer_HP-head) has the highest classification accuracy of 79.25%. The majority of features related to classification results were second-order features, followed by first-order features and shape features. The radiomics features of hippocampal subregions can be used as a biomarker to identify TLE. The second-order features are the most important features for the classification of TLE.

       

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