Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas
First author：Qi Chong,Li Yiming
Corresponding author：Jiang Tao,Li Shaowu
essential_information
- Person in charge：Qi Chong,Li Yiming
- Data Sources： OTHERS
- Number of papers：OTHERS20190805
- Published time：2019-04-22
2019-04-22
Published articles
Journal：NeuroImage. Clinical
Publication type: Research Article
IF(IF): 3.943
PMID: 31035232 Link To Pubmed Full text Download
Background And Purpose
Method
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.
Conclusion
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.
Main results
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.