Introduction 

Gliomas are the most common primary intra-axial brain tumours. Precise and robust diagnosis and staging are crucial for implementing successful management strategies and predicting their outcome. However, there is noticeable variation in individual tumour response even if they are attributed the same histological grade. This could be explained by the divergent proliferative patterns, cellular complexity and neovascularisation among these tumours.1 WHO 2007 classification used to classify brain gliomas into four subgroups depending on their histopathological features such as cellular atypia, mitotic activity and anaplasia.2 Meanwhile, efforts have been made in order to further stratify gliomas based on their molecular features aiming to better explain the tumours’ behaviour and determine better treatment strategies. The revised fourth edition of the updated WHO classification of central nervous system tumours in 2016 incorporates both molecular and histopathological features of gliomas.3 As previously, histopathological examination after neurosurgical sampling is still considered as the state-of-the-art staging method of gliomas; owing to the moderate specificity of non-invasive imaging techniques.4 5 On the other hand, there is a demand for reliable non-invasive imaging methods, namely for patients who are unfit for surgery, to elucidate concerns regarding postbiopsy complications, or even to diagnose patients with suspected low-grade gliomas whose management can be regular follow-up.3 In the last few years, both diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) have emerged as presurgical imaging tools and as tools for evaluating treatment response to chemotherapy and radiotherapy regimens in patients with gliomas.6 However, DWI and DTI quantify water molecules’ mobility on the assumption of unrestricted—but possibly hindered—random diffusion. Accordingly, the likelihood of certain proton diffusing from one location to another in a given time (known as the probability distribution function, PDF) is thought to be Gaussian related.7 However, the paramount differences inside the brain tissue cytoarchitecture due to cell membranes, organelles and discrete compartments will drift the diffusion of water molecules from the normal Gaussian distribution. Therefore, the real PDF will be more soared up in contrast to the Gaussian PDF. The widely used metric of diffusion attenuation in tumours, namely the apparent diffusion coefficient, is limited in detecting this deviation from the normal Gaussian behaviour. A novel diffusion model known as diffusional kurtosis imaging (DKI) is a dimensionless metric that has recently evolved to quantify water molecules’ degree of non-Gaussianity.1 7 Thus, DKI can provide a more realistic biomarker that reflects the brain microenvironment’s complexity.8 It is simply considered a continuation of the DTI model and at least 2 non-zero diffusion gradient factors (b-values) in more than 15 non-linear diffusion directions are applied to acquire both the kurtosis metrics (radial kurtosis, axial kurtosis and mean kurtosis (Mk)) and the diffusion tensor metrics (mean diffusivity and fractional anisotropy simultaneously.1 It is therefore important to understand the diagnostic performance of DKI, both regarding its ability to stage the gliomas in subgroups and to differentiate gliomas from other intra-axial brain tumours.

Recent studies have shown promising results.8 9 In April 2018, a systematic review and meta-analysis of the diagnostic accuracy of DKI for glioma grading revealed that the pooled area under the curve for Mk in differentiating high-grade from low-grade gliomas was 0.94.10 This review included only 10 studies, whereas in the current work, we believe that more studies about DKI have been held in the interim. Moreover, we aim to include studies that compared DKI between gliomas and other intra-axial brain tumours.