A model combined machine learning with imaging omics characteristics in differentiating anaplastic glioma from glioblastoma
10.3760/cma.j.cn115354-20191122-00685
- VernacularTitle:机器学习结合影像组学特征鉴别间变性胶质细胞瘤和胶质母细胞瘤
- Author:
Ce WANG
1
;
Zenghui QIAN
;
Zehao CAI
;
Zhuang KANG
;
Baoshi CHEN
Author Information
1. 首都医科大学附属北京天坛医院神经外科，北京 100071
- Keywords:
Machine learning;
Radiomics;
T1 enhanced MR imaging;
Anaplastic glioma;
Glioblastoma
- From:
Chinese Journal of Neuromedicine
2020;19(3):224-228
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct and validate a prediction model combined machine learning with imaging omics characteristics in differentiating anaplastic glioma from glioblastoma.Methods:Imaging data of 241 patients with anaplastic glioma or glioblastoma, confirmed by pathology in our hospital from August 2005 to August 2012, were retrospectively collected. These patients were divided into a training group ( n=140) and a verification group ( n=101) according to random number table method. MRIcron software was used to delineate tumor boundaries of patients from the training group on preoperative T1 enhanced MR imaging. The regions of interest (ROIs) were outlined on preoperative T1 enhanced MR imaging, and the radiomic features were extracted from ROIs by Matlab software. Least absolute shrinkage and selection operator (LASSO) regression model was used to screen the features, and then, the selected features were used to construct the prediction model by support vector machine (SVM) classifier. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of the model. Results:In these 241 patients, 101 were with anaplastic glioma and 140 were with glioblastoma confirmed by pathology. In the training group and validation group, there was statistical difference in age between patients with anaplastic glioma and glioblastoma ( P<0.05); there was no significant difference in gender distribution, tumor location, and percentages of tumor necrosis or edema between patients with anaplastic glioma and glioblastoma ( P>0.05). Totally, 431 radiomic features were extracted; 11 radiomic features were screened by LASSO regression model and the prediction model was established. The AUC of ROC curve was 0.942 and 0.875, respectively, in the training group and validation group. Conclusion:The prediction model combined machine learning and imaging omics characteristics can effectively discriminate anaplastic glioma from glioblastoma.
