国家神经系统疾病临床医学研究中心数据管理与共享系统

Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

《国家中心数据管理与共享系统操作说明书 —— 研究者部分》

A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas

第一作者：Qi Chong,Li Yiming

通讯作者：Jiang Tao,Li Shaowu

Glioma grading; Machine learning; Proton magnetic

基本信息

2019-04-22

已发表文章

发表杂志：NeuroImage. Clinical

发表类型: Research Article

影响因子(IF): 3.943

PMID: 31035232 Pubmed原文链接 全文下载

主要研究背景及目的

方法

This study included 112 glioma patients who were divided into the training (n = 74) and validation (n = 38) sets based on the time of hospitalization. Twenty-six metabolic features were extracted from the preoperative 1H-MRS image. The Student's t-test was conducted to screen for differentially expressed features between low- and high-grade gliomas (WHO grades II and III/IV, respectively). Next, the minimum Redundancy Maximum Relevance (mRMR) algorithm was performed to further select features for a support vector machine (SVM) classifier building. Performance of the predictive model was evaluated both in the training and validation sets using ROC curve analysis.

结论

1H-MRS metabolic features could help in predicting the grade of gliomas. The machine-learning model achieved a better prediction performance in grading gliomas than individual features, indicating that it could complement the traditionally used metabolic features.

主要结果

Among the extracted 1H-MRS metabolic features, thirteen features were differentially expressed. Four features were further selected as grade-predictive imaging signatures using the mRMR algorithm. The predictive performance of the machine-learning model measured by the AUC was 0.825 and 0.820 in the training and validation sets, respectively. This was better than the predictive performances of individual metabolic features, the best of which was 0.812.