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Glioma grade discrimination with MR diffusion kurtosis imaging : A meta-Analysis of diagnostic accuracy

Delgado, Anna Falk; Nilsson, Markus LU ; Van Westen, Danielle LU and Delgado, Alberto Falk (2018) In Radiology 287(1). p.119-127
Abstract

Purpose: To assess the diagnostic test accuracy and sources of heterogeneity for the discriminative potential of diffusion kurtosis imaging (DKI) to differentiate low-grade glioma (LGG) (World Health Organization [WHO] grade II) from high-grade glioma (HGG) (WHO grade III or IV). Materials and Methods: The Cochrane Library, Embase, Medline, and the Web of Science Core Collection were systematically searched by two librarians. Retrieved hits were screened for inclusion and were evaluated with the revised tool for quality assessment for diagnostic accuracy studies (commonly known as QUADAS-2) by two researchers. Statistical analysis comprised a random-effects model with associated heterogeneity analysis for mean differences in mean... (More)

Purpose: To assess the diagnostic test accuracy and sources of heterogeneity for the discriminative potential of diffusion kurtosis imaging (DKI) to differentiate low-grade glioma (LGG) (World Health Organization [WHO] grade II) from high-grade glioma (HGG) (WHO grade III or IV). Materials and Methods: The Cochrane Library, Embase, Medline, and the Web of Science Core Collection were systematically searched by two librarians. Retrieved hits were screened for inclusion and were evaluated with the revised tool for quality assessment for diagnostic accuracy studies (commonly known as QUADAS-2) by two researchers. Statistical analysis comprised a random-effects model with associated heterogeneity analysis for mean differences in mean kurtosis (MK) in patients with LGG or HGG. A bivariate restricted maximum likelihood estimation method was used to describe the summary receiver operating characteristics curve and bivariate meta-regression. Results: Ten studies involving 430 patients were included. The mean difference in MK between LGG and HGG was 0.17 (95% confidence interval [CI]: 0.11, 0.22) with a z score equal to 5.86 (P < .001). The statistical heterogeneity was explained by glioma subtype, echo time, and the proportion of recurrent glioma versus primary glioma. The pooled area under the curve was 0.94 for discrimination of HGG from LGG, with 0.85 (95% CI: 0.74, 0.92) sensitivity and 0.92 (95% CI: 0.81, 0.96) specificity. Heterogeneity was driven by neuropathologic subtype and DKI technique. Conclusion: MK shows high diagnostic accuracy in the discrimination of LGG from HGG.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Radiology
volume
287
issue
1
pages
9 pages
publisher
Radiological Society of North America
external identifiers
  • scopus:85044314743
ISSN
0033-8419
DOI
10.1148/radiol.2017171315
language
English
LU publication?
yes
id
22873d77-e22a-47e1-addc-b55f77d00fbe
date added to LUP
2018-04-04 13:02:51
date last changed
2018-11-18 05:04:19
@article{22873d77-e22a-47e1-addc-b55f77d00fbe,
  abstract     = {<p>Purpose: To assess the diagnostic test accuracy and sources of heterogeneity for the discriminative potential of diffusion kurtosis imaging (DKI) to differentiate low-grade glioma (LGG) (World Health Organization [WHO] grade II) from high-grade glioma (HGG) (WHO grade III or IV). Materials and Methods: The Cochrane Library, Embase, Medline, and the Web of Science Core Collection were systematically searched by two librarians. Retrieved hits were screened for inclusion and were evaluated with the revised tool for quality assessment for diagnostic accuracy studies (commonly known as QUADAS-2) by two researchers. Statistical analysis comprised a random-effects model with associated heterogeneity analysis for mean differences in mean kurtosis (MK) in patients with LGG or HGG. A bivariate restricted maximum likelihood estimation method was used to describe the summary receiver operating characteristics curve and bivariate meta-regression. Results: Ten studies involving 430 patients were included. The mean difference in MK between LGG and HGG was 0.17 (95% confidence interval [CI]: 0.11, 0.22) with a z score equal to 5.86 (P &lt; .001). The statistical heterogeneity was explained by glioma subtype, echo time, and the proportion of recurrent glioma versus primary glioma. The pooled area under the curve was 0.94 for discrimination of HGG from LGG, with 0.85 (95% CI: 0.74, 0.92) sensitivity and 0.92 (95% CI: 0.81, 0.96) specificity. Heterogeneity was driven by neuropathologic subtype and DKI technique. Conclusion: MK shows high diagnostic accuracy in the discrimination of LGG from HGG.</p>},
  author       = {Delgado, Anna Falk and Nilsson, Markus and Van Westen, Danielle and Delgado, Alberto Falk},
  issn         = {0033-8419},
  language     = {eng},
  month        = {04},
  number       = {1},
  pages        = {119--127},
  publisher    = {Radiological Society of North America},
  series       = {Radiology},
  title        = {Glioma grade discrimination with MR diffusion kurtosis imaging : A meta-Analysis of diagnostic accuracy},
  url          = {http://dx.doi.org/10.1148/radiol.2017171315},
  volume       = {287},
  year         = {2018},
}