Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma - Fan, Yimeng; Chen, Chaoyue; Zhao, Fumin; Tian, Zerong; Wang, Jian; Ma, Xuelei; Xu, Jianguo (Frontiers in Oncology, 2019)
Purpose The aim of this study was to test whether radiomics-based machine learning can enable the differentiation between glioblastoma (GBM) and anaplastic oligodendroglioma (AO). Methods This retrospective study involved 126 patients histologically diagnosed as GBM (n=76) or AO (n=50) in our institution from January 2015 to December 2018. A total number of 40 three-dimensional texture features were extracted from contrast-enhanced T1-weighted images using LIFEx package. Six diagnostic models were established with selection methods and classifiers. The optimal radiomics features were separately selected into three datasets with three feature selection methods (distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT)). Then datasets were separately adopted into linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Specificity, sensitivity, accuracy, and area under curve (AUC) of each model were calculated to evaluate their diagnostic performances. Results Both classifiers showed promising ability in discrimination with AUC more than 0.900 when combined with suitable feature selection method. For LDA-based models, the AUC of models were 0.986, 0.994 and 0.970 in the testing group, respectively. For the SVM-based models, the AUC of models were 0.923, 0.817 and 0.500 in the testing group, respectively. The over-fitting model was GBDT+SVM, suggesting that this model was too volatile that unsuitable for classification. Conclusion This study indicates radiomics-based machine learning has the potential to be utilized in discriminating GBM from AO.