Objective: To build highly predictive performance models for glioma stratification with radiomics features from non-invasive MRI. Methods: T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) imaging, diffusion-weighted MRI and diffusion kurtosis imaging were retrospectively collected for 139 glioma cases (2 with grade I, 67 with grade II, 36 with grade III and 34 with grade IV disease). Multi-parameter maps, including the apparent diffusion coefficient (ADC), mean diffusion coefficient (Dmean), fractional anisotropy (FA), and mean kurtosis (MK), were co-registered to T2-FLAIR, and 431 radiomics features for each were extracted within manually segmented tumour regions. Partial correlation analysis revealed correlations between radiomics features and glioma stratification factors (tumour grades and Ki-67 LI). Predictive models were built with adjusted-imbalanced logistic regression. Results: MK radiomics features were more closely correlated with glioma stratification than other modalities analysed. The maximum R in MK was 0.52 for tumour grade and 0.56 for Ki-67 index (compared with 0.36 and 0.34 in FA, and 0.43 and 0.37 in ADC, and 0.48 and 0.42 in T2-FLAIR). The best predictive models for grade II vs. III, grade II vs. IV, low-grade vs. high-grade gliomas and positive vs. highly positive Ki-67 LI were obtained by combining multi-parameter MR radiomics features with AUCs of 0.858, 0.966, 0.853 and 0.870, respectively. However, for grade III vs. IV gliomas, the model obtained from MK radiomics features achieved the highest AUC (0.947), with excellent sensitivity and specificity. Conclusion: Compared with the other modalities, MK showed closer correlations with tumour grade and Ki-67 LI. Combined radiomics models integrating multi-parameter MRI allow for the generation of highly predictive models for glioma stratification.