The diagnostic and prediction performance of MR diffusion kurtosis imaging in the glioma molecular classification: a systematic review and meta-analysis.

Abstract

Background: Although diffusion magnetic resonance imaging (dMRI), particularly diffusion kurtosis imaging (DKI), has demonstrated efficacy in distinguishing between low- and high-grade gliomas, its predictive utility across various molecular genotypes remains unclear. Evaluating the accuracy of DKI and identifying sources of heterogeneity in its predictive performance could advance noninvasive molecular diagnostic methods and support the development of personalized treatment strategies.

Materials and methods: A literature search of the PubMed, Web of Science, Cochrane Library, Embase, and Medline databases was performed. The studies retrieved were screened by two researchers (HFZ and ZGH), and those fulfilling the inclusion criteria were subsequently included in the meta-analysis. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. The analyses summarized the mean differences in mean kurtosis (MK) and mean diffusivity (MD) in patients harboring various genotypes using suitable models, and explored heterogeneity. Finally, a bivariate restricted maximum likelihood estimation method and meta-regression analysis were performed to assess diagnostic potential and stability.

Results: Fourteen studies comprising 886 patients were included in this meta-analysis. Regarding MK and MD, the mean difference between isocitrate dehydrogenase (IDH) mutation and IDH wild type was -0.21 (95% confidence interval [CI] -0.27 to -0.15; I ² = 93%) and 0.22 (95% CI 0.11 to 0.33; I ² = 92%), respectively. This heterogeneity could be explained by imaging parameters such as repetition time, echo time, maximal b-value, and number of diffusion directions. However, the mean difference did not reflect the genetic status of 1p/19q, α-thalassemia/mental retardation syndrome-X-linked (ATRX) gene, or O₆-methylguanine-DNA-methyltransferase (MGMT). Analysis of diagnostic accuracy revealed that the pooled areas under the curve for MK and MD, based on IDH status, were 0.96 (95% CI 0.93 to 0.97) and 0.76 (95% CI 0.71 to 0.81), respectively. Heterogeneity was not observed for these DKI parameters.

Conclusion: MK and MD exhibited potential diagnostic utility in the prediction of glioma molecular status and should be explored in medical practice. These parameters should be compared with other MRI models to develop a stable and suitable genetic molecular prediction method for patients with gliomas.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024568923, CRD42024568923.