This study aimed to assess the diagnostic performance of multiparametric MRI radiomics for glioma class prediction according to the WHO 2016 classification. Histogram features were extracted from prospectively acquired multiparametric MRI (pCASL, DSC-MRI, DCE-MRI, and DWI) in 32 patients with primary gliomas. The uncombined significant features of ASL, ADC, DSC, and DCE, revealed diagnostic performances varying from low (44% ) to fair (86%) and unable to predict all the histomolecular classes. However, combining them for each MRI method, independently, enhanced the diagnostic accuracy up to 100% and predict all the classes. This alludes the use of multimodal radiomics for glioma classification.