Jiang Liang, Zhou Leilei, Ai Zhongping, Xiao Chaoyong, Liu Wen, Geng Wen, Chen Huiyou, Xiong Zhenyu, Yin Xindao, Chen Yu-Chen
Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210029, China.
Department of Radiology, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China.
J Clin Med. 2022 Apr 21;11(9):2310. doi: 10.3390/jcm11092310.
Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade ( = 61) and high-grade ( = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades.
胶质瘤分级在手术切除中起着重要作用。我们研究了不同特征约简方法在基于支持向量机（SVM）的扩散峰度成像（DKI）直方图参数中区分胶质瘤分级的能力。回顾性纳入了2017年1月至2020年1月期间接受磁共振成像（MRI）检查的161例胶质瘤患者。患者被分为低级别（n = 61）和高级别（n = 100）组。生成了参数化DKI图，并半自动提取了DKI图中的45个特征进行分析。使用三种特征选择方法[主成分分析（PCA）、递归特征消除（RFE）和最小绝对收缩和选择算子（LASSO）]与SVM分类器建立胶质瘤分级模型。为了评估SVM模型的性能，将SVM模型区分胶质瘤分级的受试者工作特征（ROC）曲线与传统统计方法的ROC曲线进行了比较。传统ROC分析表明，平均扩散率（MD）方差、MD偏度和平均峰度（MK）C50能够有效区分胶质瘤分级，尤其是MD方差。使用LASSO时发现最高分类区分AUC为0.904±0.069。相比之下，PCA的分类AUC为0.866±0.061，RFE为0.899±0.079。SVM模型中AUC最低的SVM - PCA模型显著优于传统ROC分析（z = 1.947，P = 0.013）。这些发现证明了通过LASSO分析和SVM的DKI直方图参数在区分胶质瘤分级方面的优越性。
