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Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis

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Abstract

Objectives

Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters.

Methods

A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity.

Results

Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84–94%) and 91% (95% CI, 84–95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94–0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84–97%) and the pooled specificity was 94% (95% CI, 85–98%).

Conclusions

Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis.

Key Points

• Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis.

• The pooled sensitivity was 90% and pooled specificity was 91%.

• Peritumoral rCBV derived from DSC is a relatively well-validated.

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Abbreviations

ASL:

Arterial spin labelling

AUROC:

Area under the receiver operating characteristic curve

DCE:

Dynamic contrast-enhanced imaging

DSC:

Dynamic susceptibility-weighted contrast-enhanced imaging

HSROC:

Hierarchical summary receiver operating characteristic

IAUC:

Initial area under the curve

QUADAS-2:

Quality assessment of diagnostic accuracy studies-2

rCBF:

Relative cerebral blood flow

rCBV:

Relative cerebral blood volume

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Funding

This study was supported by a grant from the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (1720030).

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Correspondence to Ho Sung Kim.

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The scientific guarantor of this publication is Ho Sung Kim.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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One of the authors (Chong Hyun Suh) has significant statistical expertise (4 years of experience in a systematic review and meta-analysis).

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Written informed consent was not required for this study because of the nature of our study, which was a systemic review and meta-analysis.

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Institutional Review Board approval was not required because of the nature of our study, which was a systemic review and meta-analysis.

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• A systemic review and meta-analysis performed at one institution

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Suh, C.H., Kim, H.S., Jung, S.C. et al. Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 28, 3819–3831 (2018). https://doi.org/10.1007/s00330-018-5335-0

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