An evidence-based approach to assess the accuracy of diffusion kurtosis imaging in characterization of gliomas

Abstract

Objective:Accurate and noninvasive pathologic grading of glioma patients before surgery was crucial to guiding clinicians to select appropriate treatment and improve patient prognosis. This study was performed to investigate the potential diagnostic value of diffusion kurtosis imaging (DKI) to distinguish high-grade gliomas (HGGs) from low-grade gliomas (LGGs) based on an evidence-based approach.Methods:Relevant articles that used DKI to distinguish HGG from LGG in Embase, PubMed, China Knowledge Resource Integrated database (CNKI), Web of Knowledge, and Cochrane Libraries databases were electronically searched to April 31, 2018 by 2 reviewers. All analysis was performed by using Meta-disc1.4 and Stata. Influence factors on the diagnostic accuracy were evaluated using meta-regression analysis.Results:Five eligible studies were included in this meta-analysis. The pooled sensitivity (SEN) and specificity (SPE) was 91% (confidence interval [CI]: 0.78–0.96; P = .02) and 91% (CI: 0.80–0.97; P = .01). The pooled data showed that diagnostic odds ratio (DOR) of DKI was 79.75 (CI: 31.57–201.45). The area under the curve (AUC) of summary receiver operating characteristic curve was 0.96. There is no evidence that our research has a threshold effect (Spearman correlation coefficient: 0.300, P = .624) and publication bias. Meta regression analysis identified that country, language, field strength, and parameter of magnetic resonance imaging had no significant effect on diagnostic performance.Conclusion:The present meta-analysis shows that the mean kurtosis values derived from DKI may be useful in characterization of gliomas with high sensitivity and specificity. Taken into consideration the small sample of this study, we need to be cautious when interpreting the results of this study.

The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis

“…To date, there have been only two published meta-analyses which have looked at the diagnostic test accuracy (DTA) of DKI in glioma discrimination and both demonstrated very promising initial results [10,11]. Our systematic review and meta-analysis extend this further by increasing the number of electronic databases, removing language restrictions and broadening the eligibility criteria.…”

“…Early research is encouraging. Two prior meta-analyses which looked at the diagnostic accuracy of DKI for glioma grading projected a pooled area under the curve (AUC) of 0.94 and 0.96 for MK [10,11]. We attempted to consolidate the preliminary meta-analyses evidence in the topic through an updated systematic review and meta-analysis.…”

Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors

“…By acquiring data in more than 15 nonlinear directions, both kurtosis metrics (mean, axial, and radial kurtosis) and conventional diffusion metrics (mean, axial, radial diffusivity, and fractional anisotropy) are obtained. Meta-analyses that examined the diagnostic accuracy of DKI for differentiating high- and low-grade gliomas projected a pooled area under the curve of 0.94 and 0.96 for mean kurtosis [ 68 , 69 ]. More recently, Abdalla et al performed an extended and updated meta-analysis and also concluded that DKI has good diagnostic accuracy in differentiating high- from low-grade gliomas [ 70 ].…”