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Meta-Analysis
. 2018 Jan 22;1(1):CD011551.
doi: 10.1002/14651858.CD011551.pub2.

Magnetic resonance perfusion for differentiating low-grade from high-grade gliomas at first presentation

Jill M Abrigo  1 , Daniel M Fountain, James M Provenzale, Eric K Law, Joey Sw Kwong, Michael G Hart, Wilson Wai San Tam
Affiliations
Meta-Analysis

Magnetic resonance perfusion for differentiating low-grade from high-grade gliomas at first presentation

Jill M Abrigo et al. Cochrane Database Syst Rev. .

Abstract

Background: Gliomas are the most common primary brain tumour. They are graded using the WHO classification system, with Grade II-IV astrocytomas, oligodendrogliomas and oligoastrocytomas. Low-grade gliomas (LGGs) are WHO Grade II infiltrative brain tumours that typically appear solid and non-enhancing on magnetic resonance imaging (MRI) scans. People with LGG often have little or no neurologic deficit, so may opt for a watch-and-wait-approach over surgical resection, radiotherapy or both, as surgery can result in early neurologic disability. Occasionally, high-grade gliomas (HGGs, WHO Grade III and IV) may have the same MRI appearance as LGGs. Taking a watch-and-wait approach could be detrimental for the patient if the tumour progresses quickly. Advanced imaging techniques are increasingly used in clinical practice to predict the grade of the tumour and to aid clinical decision of when to intervene surgically. One such advanced imaging technique is magnetic resonance (MR) perfusion, which detects abnormal haemodynamic changes related to increased angiogenesis and vascular permeability, or "leakiness" that occur with aggressive tumour histology. These are reflected by changes in cerebral blood volume (CBV) expressed as rCBV (ratio of tumoural CBV to normal appearing white matter CBV) and permeability, measured by Ktrans.

Objectives: To determine the diagnostic test accuracy of MR perfusion for identifying patients with primary solid and non-enhancing LGGs (WHO Grade II) at first presentation in children and adults. In performing the quantitative analysis for this review, patients with LGGs were considered disease positive while patients with HGGs were considered disease negative.To determine what clinical features and methodological features affect the accuracy of MR perfusion.

Search methods: Our search strategy used two concepts: (1) glioma and the various histologies of interest, and (2) MR perfusion. We used structured search strategies appropriate for each database searched, which included: MEDLINE (Ovid SP), Embase (Ovid SP), and Web of Science Core Collection (Science Citation Index Expanded and Conference Proceedings Citation Index). The most recent search for this review was run on 9 November 2016.We also identified 'grey literature' from online records of conference proceedings from the American College of Radiology, European Society of Radiology, American Society of Neuroradiology and European Society of Neuroradiology in the last 20 years.

Selection criteria: The titles and abstracts from the search results were screened to obtain full-text articles for inclusion or exclusion. We contacted authors to clarify or obtain missing/unpublished data.We included cross-sectional studies that performed dynamic susceptibility (DSC) or dynamic contrast-enhanced (DCE) MR perfusion or both of untreated LGGs and HGGs, and where rCBV and/or Ktrans values were reported. We selected participants with solid and non-enhancing gliomas who underwent MR perfusion within two months prior to histological confirmation. We excluded studies on participants who received radiation or chemotherapy before MR perfusion, or those without histologic confirmation.

Data collection and analysis: Two review authors extracted information on study characteristics and data, and assessed the methodological quality using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We present a summary of the study characteristics and QUADAS-2 results, and rate studies as good quality when they have low risk of bias in the domains of reference standard of tissue diagnosis and flow and timing between MR perfusion and tissue diagnosis.In the quantitative analysis, LGGs were considered disease positive, while HGGs were disease negative. The sensitivity refers to the proportion of LGGs detected by MR perfusion, and specificity as the proportion of detected HGGs. We constructed two-by-two tables with true positives and false negatives as the number of correctly and incorrectly diagnosed LGG, respectively, while true negatives and false positives are the number of correctly and incorrectly diagnosed HGG, respectively.Meta-analysis was performed on studies with two-by-two tables, with further sensitivity analysis using good quality studies. Limited data precluded regression analysis to explore heterogeneity but subgroup analysis was performed on tumour histology groups.

Main results: Seven studies with small sample sizes (4 to 48) met our inclusion criteria. These were mostly conducted in university hospitals and mostly recruited adult patients. All studies performed DSC MR perfusion and described heterogeneous acquisition and post-processing methods. Only one study performed DCE MR perfusion, precluding quantitative analysis.Using patient-level data allowed selection of individual participants relevant to the review, with generally low risks of bias for the participant selection, reference standard and flow and timing domains. Most studies did not use a pre-specified threshold, which was considered a significant source of bias, however this did not affect quantitative analysis as we adopted a common rCBV threshold of 1.75 for the review. Concerns regarding applicability were low.From published and unpublished data, 115 participants were selected and included in the meta-analysis. Average rCBV (range) of 83 LGGs and 32 HGGs were 1.29 (0.01 to 5.10) and 1.89 (0.30 to 6.51), respectively. Using the widely accepted rCBV threshold of <1.75 to differentiate LGG from HGG, the summary sensitivity/specificity estimates were 0.83 (95% CI 0.66 to 0.93)/0.48 (95% CI 0.09 to 0.90). Sensitivity analysis using five good quality studies yielded sensitivity/specificity of 0.80 (95% CI 0.61 to 0.91)/0.67 (95% CI 0.07 to 0.98). Subgroup analysis for tumour histology showed sensitivity/specificity of 0.92 (95% CI 0.55 to 0.99)/0.42 (95% CI 0.02 to 0.95) in astrocytomas (6 studies, 55 participants) and 0.77 (95% CI 0.46 to 0.93)/0.53 (95% CI 0.14 to 0.88) in oligodendrogliomas+oligoastrocytomas (6 studies, 56 participants). Data were too sparse to investigate any differences across subgroups.

Authors' conclusions: The limited available evidence precludes reliable estimation of the performance of DSC MR perfusion-derived rCBV for the identification of grade in untreated solid and non-enhancing LGG from that of HGG. Pooled data yielded a wide range of estimates for both sensitivity (range 66% to 93% for detection of LGGs) and specificity (range 9% to 90% for detection of HGGs). Other clinical and methodological features affecting accuracy of the technique could not be determined from the limited data. A larger sample size of both LGG and HGG, preferably using a standardised scanning approach and with an updated reference standard incorporating molecular profiles, is required for a definite conclusion.

PubMed Disclaimer

Conflict of interest statement

Jill M Abrigo ‐ Has no competing interest to declare Wilson Wai San Tam ‐ None known Dan Fountain ‐ None known Michael G Hart ‐ None Known Eric Ka Chai Law ‐ None Known Joey SW Kwong ‐ None known James M Provenzale ‐ Reports that he holds consultancies, industry‐sponsored lectures and grants but avows that they have no relationship to the research topic being studied herein.

Figures

1
1
Diagram shows the clinical management algorithm for patients with infiltrative glioma. The role of the index test (MRP) for differentiating LGGs and HGGs at first presentation is shown with alternative tests (MRS, DWI, PET). These advanced MRI techniques are also used to identify progression or recurrence during interval scanning and are included, although they are outside the scope of this review. *May or may not be offered, depending on institutional/regional practice. Abbreviations: LGG: Low‐grade glioma, HGG: High‐grade glioma,MRP: Magnetic resonance perfusion, MRS: magnetic resonance spectroscopy, DWI: Diffusion‐weighted imaging, PET: Positron emission tomography
2
2
Flow diagram.
3
3
Risk of bias and applicability concerns summary: review authors' judgements about each domain for each included study using QUADAS 2 tool, applied on study design and included patient data
4
4
Risk of bias and applicability concerns graph: review authors' judgements about each domain presented as percentages across included studies
5
5
Coupled forest plots of included studies using rCBV threshold of < 1.75 for differentiating low grade gliomas from high‐grade gliomas.
6
6
Summary ROC Plot of DSC MR perfusion using rCBV threshold of 1.75 for differentiating low grade gliomas from high‐grade gliomas. In this review, a positive test or rCBV < 1.75 implied an LGG diagnosis, while a negative test or rCBV > 1.75 suggested an HGG diagnosis. In the SROC plot, each study is represented by an open circle with emanating lines, representing the sensitivity and specificity with their confidence intervals. The size of the open circle is proportional to the study sample size. The shaded circle represents the pooled sensitivity and specificity surrounded by a 95% confidence ellipse (dotted line), which in this case is 0.830 (95% CI 0.657, 0.926) and 0.479 (95% CI 0.086, 0.900), respectively.
1
1. Test
rCBV ‐ Law Threshold.
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