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. 2022 Oct;12(10):4805-4822.
doi: 10.21037/qims-22-32.

Perfusion magnetic resonance imaging in the differentiation between glioma recurrence and pseudoprogression: a systematic review, meta-analysis and meta-regression

Jun Zhang #  1   2 , Yulin Wang #  1 , Yan Wang  1 , Huafeng Xiao  1 , Xinjing Chen  1 , Yifei Lei  3 , Zhebin Feng  3 , Xiaodong Ma  3 , Lin Ma  1
Affiliations

Perfusion magnetic resonance imaging in the differentiation between glioma recurrence and pseudoprogression: a systematic review, meta-analysis and meta-regression

Jun Zhang et al. Quant Imaging Med Surg. 2022 Oct.

Abstract

Background: Tumor recurrence and pseudoprogression (PsP) have similar imaging manifestations in conventional magnetic resonance imaging (MRI), although the subsequent treatments are completely different. This study aimed to evaluate the value of perfusion-weighted imaging (PWI) in differentiating PsP from glioma recurrence.

Methods: A comprehensive literature search was performed to evaluate clinical studies focused on differentiating recurrent glioma from PsP using PWI, including dynamic susceptibility contrast MRI (DSC-MRI), dynamic contrast enhanced MRI (DCE-MRI), and arterial spin labeling (ASL). Study selection and data extraction were independently completed by two reviewers. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was applied to evaluate the quality of the included studies. The software Stata 16.0 and Meta-Disc 1.4 were used for the meta-analysis. Meta-regression and subgroup analyses were applied to identify the sources of heterogeneity in the studies. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) prior to initiation (CRD42022304404).

Results: A total of 40 studies were included, including 27 English studies and 13 Chinese studies. There were 1,341 patients with glioma recurrence and 876 patients with PsP. The pooled sensitivity and specificity of DSC-MRI for differentiating glioma recurrence from PsP were 0.82 [95% confidence interval (CI): 0.78 to 0.86] and 0.87 (95% CI: 0.80 to 0.92), respectively. The pooled sensitivity and specificity of DCE-MRI were 0.83 (95% CI: 0.76 to 0.89) and 0.83 (95% CI: 0.78 to 0.87), respectively. The pooled sensitivity and specificity of ASL were 0.80 (95% CI: 0.73 to 0.86) and 0.86 (95% CI: 0.76 to 0.92), respectively.

Discussion: The DSC-MRI, DCE-MRI, and ASL perfusion techniques displayed high accuracy in distinguishing glioma recurrence from PsP, and DSC-MRI had a higher diagnostic performance than the other two techniques. However, due to the diversity of the parameters and threshold differences, further investigation and standardization are needed.

Keywords: Perfusion-weighted imaging (PWI); glioma; meta-analysis; pseudoprogression (PsP); tumor recurrence.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-32/coif). Yulin Wang reports that this study was supported by China Postdoctoral Science Foundation (No. 2020T130779). The other authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
MRI findings of glioma recurrence and PsP. (A-D) Recurrent IDH-wildtype glioblastoma in the right temporal lobe in a 54-year-old man. (A) Axial T2-weighted imaging shows ill-defined lesion with heterogeneous hyperintensity. (B) Axial post-contrast T1-weighted imaging shows heterogeneous enhancement. (C) ASL image shows iso-perfusion mixed with spot-like hyper-perfusion. (D) The CBV map of DSC-MRI shows hyper-perfusion in most of the lesion. (E-H) PsP in IDH-mutant astrocytoma after surgery and radiotherapy in the right frontal lobe in a 53-year-old woman. (E) Axial T2-weighted imaging shows well-defined lesion with iso-intensity. (F) Axial post-contrast T1-weighted imaging shows ring enhancement. (G) ASL image shows hypo-perfusion. (H) The CBV map of DSC also shows hypo-perfusion. MRI, magnetic resonance imaging; PsP, pseudoprogression; ASL, arterial spin labeling; CBV, cerebral blood volume; DSC-MRI, dynamic susceptibility contrast magnetic resonance imaging.
Figure 2
Figure 2
Flow chart of the study selection process.
Figure 3
Figure 3
Risk of bias and applicability concerns graph for each included study. High risk (−), unclear risk (?) and low risk (+).
Figure 4
Figure 4
Forest plots of sensitivity and specificity in the included studies [(A) DSC-MRI, (B) DCE-MRI, (C) ASL]. CI, confidence interval; DSC-MRI, dynamic susceptibility contrast magnetic resonance imaging; DCE-MRI, dynamic contrast enhanced magnetic resonance imaging; ASL, arterial spin labeling.
Figure 5
Figure 5
SROC curves of three PWI techniques to distinguish glioma recurrence from PsP [(A) DSC-MRI, (B) DCE-MRI, (C) ASL]. DSC-MRI, dynamic susceptibility contrast magnetic resonance imaging; SENS, sensitivity; SPEC, specificity; SROC, the summary receiver operating characteristic; AUC, area under the curve; PWI, perfusion-weighted imaging; PsP, pseudoprogression; DCE-MRI, dynamic contrast enhanced magnetic resonance imaging; ASL, arterial spin labeling.
Figure 6
Figure 6
Fagan plots of three PWI techniques to distinguish glioma recurrence from PsP [(A) DSC-MRI, (B) DCE-MRI, (C) ASL]. LR, likelihood ratio; PWI, perfusion-weighted imaging; PsP, pseudoprogression; DSC-MRI, dynamic susceptibility contrast magnetic resonance imaging; DCE-MRI, dynamic contrast enhanced magnetic resonance imaging; ASL, arterial spin labeling.
Figure 7
Figure 7
Funnel plots of the included studies [(A) DSC-MRI, (B) DCE-MRI, (C) ASL]. ESS, effective sample size; DSC-MRI, dynamic susceptibility contrast magnetic resonance imaging; DCE-MRI, dynamic contrast enhanced magnetic resonance imaging; ASL, arterial spin labeling.
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