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Background: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. Methods: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon twosample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. Results: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1).

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Purpose We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. Methods A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords 'glioma' and 'diffusional kurtosis'. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics. Results A total of 216 hits were retrieved. Considering duplicates and inclusion criteria, 23 articles were eligible for full-text reading. Ultimately, 19 studies were eligible for final inclusion. The quality assessment revealed 9 studies with low risk of bias in the 4 domains. Using a bivariate random-effect model for data synthesis, summary ROC curve showed a pooled area under the curve (AUC) of 0.92 and estimated sensitivity of 0.87 (95% CI 0.78-0.92) in high-/low-grade gliomas' differentiation. A mean difference in mean kurtosis (MK) value between HGG and LGG of 0.22 (95% CI 0.25-0.19) was illustrated (p value = 0.0014) with moderate heterogeneity (I 2 = 73.8%). Conclusion DKI shows good diagnostic accuracy in the differentiation of high-and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed. Keywords Magnetic resonance imaging. Diffusion-weighted imaging. Gliomas. Diagnosis Abbreviations ADC Apparent diffusion coefficient AUC Area under the curve CI Confidence interval CNS Central nervous system DKI Diffusional kurtosis imaging DTA Diagnostic test accuracy DTI Diffusion tensor imaging DWI Diffusion-weighted imaging HGAs High-grade astrocytomas HGG High-grade glioma IDH Isocitrate dehydrogenase LGG Low-grade glioma Keypoints 1-Evidence that DKI is a promising diagnostic tool for the assessment of CNS gliomas has been increasing. 2-Evidence that DKI is potentially generalisable across different institutions and scanning techniques. 3-This meta-analysis raises the need for establishing a standardized, widely accepted, evidence-based technique for DKI to be integrated into the routine imaging protocols of CNS gliomas.