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

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.