A glioma grading method based on radiomics
10.3760/cma.j.issn.1005-1201.2017.12.003
- VernacularTitle:基于影像组学的脑胶质瘤分级方法
- Author:
Yaping WU
1
,
2
;
郑州大学互联网医疗与健康服务河南省协同创新中心
;
Bo LIU
;
Jianqin GU
;
Guangzhi LIU
;
Weiguo WU
;
Jie TIAN
;
Yan BAI
;
Meiyun WANG
;
Yusong LIN
Author Information
1. 710049 西安交通大学电子与信息工程学院
2. 郑州大学互联网医疗与健康服务河南省协同创新中心
- Keywords:
Glioma;
Radiomics;
Artificial intelligence
- From:
Chinese Journal of Radiology
2017;51(12):902-905
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the classification of gliomas according to the theory and method of radiomics. Methods In this study, 161 pathologically confirmed glioma patients were retrospectively selected from 2012 to 2016 including 52 low-grade gliomas and 109 high-grade gliomas.Three hundred and forty-six quantization features were extracted from the MRI images, including shape, density, texture and wavelet imaging features. Mutual information and logistic regression model were used to select feature reduction and prediction model. The predictive ability of the model was validated using 10-fold cross-validation. Results Nineteen radiomics features were chosen from 346 quantization features. The sensitivity of the model was 96.3% (105/109), the specificity was 78.8% (41/52), the area under the curve (AUC) was 0.952 7, and the accuracy was 90.7%(146/161). Conclusion The solution proposed in this paper showed that radiomics can non-invasively and quickly provide an adjunct to the clinical grade of glioma with high accuracy.
