MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis

Abstract

Background: Distinction between tumor and treatment related changes is crucial for clinical management of patients with high-grade gliomas. Our purpose was to evaluate whether dynamic susceptibility contrast-enhanced (DSC) and dynamic contrast enhanced (DCE) perfusion-weighted imaging (PWI) metrics can effectively differentiate between recurrent tumor and posttreatment changes within the enhancing signal abnormality on conventional MRI.

Methods: A comprehensive literature search was performed for studies evaluating PWI-based differentiation of recurrent tumor and posttreatment changes in patients with high-grade gliomas (World Health Organization grades III and IV). Only studies published in the "temozolomide era" beginning in 2005 were included. Summary estimates of diagnostic accuracy were obtained by using a random-effects model.

Results: Of 1581 abstracts screened, 28 articles were included. The pooled sensitivities and specificities of each study's best performing parameter were 90% and 88% (95% CI: 0.85-0.94; 0.83-0.92) and 89% and 85% (95% CI: 0.78-0.96; 0.77-0.91) for DSC and DCE, respectively. The pooled sensitivities and specificities for detecting tumor recurrence using the 2 most commonly evaluated parameters, mean relative cerebral blood volume (rCBV) (threshold range, 0.9-2.15) and maximum rCBV (threshold range, 1.49-3.1), were 88% and 88% (95% CI: 0.81-0.94; 0.78-0.95) and 93% and 76% (95% CI: 0.86-0.98; 0.66-0.85), respectively.

Conclusions: PWI-derived thresholds separating viable tumor from treatment changes demonstrate relatively good accuracy in individual studies. However, because of significant variability in optimal reported thresholds and other limitations in the existing body of literature, further investigation and standardization is needed before implementing any particular quantitative PWI strategy across institutions.

Keywords: MR perfusion; gliomas; meta-analysis; pseudoprogression; radiation necrosis.

Fig. 1

Individual forest plots of the sensitivity and specificity of the best performing PWI parameter from each DSC study. Squares represent the sensitivity or specificity proportion with the size proportional to the weighting of the study. Diamond represents pooled estimate. Lines represent 95% CIs. rPH = relative peak height; PHP = peak height position; xth% = xth histogram percentile.

Fig. 2

Individual forest plots of the sensitivity and specificity of the best performing PWI parameter from each DCE study. Squares represent the sensitivity or specificity proportion with the size proportional to the weighting of the study. Diamond represents pooled estimate. Lines represent 95% CIs. K-trans = volumetric transfer coefficient, AUCR = area under curve ratio; IAUC60 = initial area under the curve after 60 s; MSIVP = maximum slope of enhancement in the initial vascular phase; nVp = normalized fractional plasma volume; Ve = fractional volume of the extracellular extravascular space; xth% = xth histogram percentile.

Fig. 3

Individual forest plots of the sensitivity and specificity of mean rCBV. Squares represent the sensitivity or specificity proportion with the size proportional to the weighting of the study. Diamond represents pooled estimate. Lines represent 95% CIs.

Fig. 4

Individual forest plots of the sensitivity and specificity of maximum rCBV. Squares represent the sensitivity or specificity proportion with the size proportional to the weighting of the study. Diamond represents pooled estimate. Lines represent 95% CIs.

Fig. 5

Results of the study quality assessment. = yes, = no, = unclear, NA = not applicable; PWI = perfusion weighted imaging.