Efficacy of MR diffusion kurtosis imaging for differentiating low-grade from high-grade glioma before surgery: A systematic review and meta-analysis

Background: Accurate discrimination and diagnosis of low-grade glioma (LGG) and high-grade glioma (HGG) before surgery is clinically important because it affects the patient's outcome and guides the clinicians to select appropriate management. The aim of this study was to evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) for differentiating LGG from HGG. Methods: A literature search of the PubMed, Web of Science, Cochrane Library and EMBASE databases was conducted up to December 15, 2020. Studies that evaluated the diagnostic performance of DKI for differentiating LGG from HGG were selected. Retrieved hits were evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Summary sensitivity and specificity were determined, and the data analysis was performed using Stata 14.0 and Review Manager 5.3. Results: Thirteen studies with 705 patients were included. The individual sensitivity and specificity of the 13 studies varied from 71% to 100% for sensitivity and 73-100% for specificity. The pooled sensitivity of DKI was 88% (95% confidence interval [CI], 83-91%), and the pooled specificity was 91% (95% CI, 86-95%). The area under the summary receiver operating characteristic curve was 0.93 (95% CI, 0.90-0.95). The pooled diagnostic odds ratio of DKI was 64.85 (95% CI 38.52-109.19). The levels of heterogeneity for sensitivity and specificity across the included studies were high (I-2 =66%) and mild (I-2 =47.04%), respectively. The multiple subgroup analyses were driven by DKI technique and study region. Conclusions: DKI demonstrated a high diagnostic performance for differentiation of LGG from HGG.