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. 2022 Jun 24:13:882334.
doi: 10.3389/fneur.2022.882334. eCollection 2022.

Diagnostic Accuracy of the Diffusion-Weighted Imaging Method Used in Association With the Apparent Diffusion Coefficient for Differentiating Between Primary Central Nervous System Lymphoma and High-Grade Glioma: Systematic Review and Meta-Analysis

Xiaoli Du  1 , Yue He  2 , Wei Lin  1
Affiliations

Diagnostic Accuracy of the Diffusion-Weighted Imaging Method Used in Association With the Apparent Diffusion Coefficient for Differentiating Between Primary Central Nervous System Lymphoma and High-Grade Glioma: Systematic Review and Meta-Analysis

Xiaoli Du et al. Front Neurol. .

Abstract

Background: It is difficult to differentiate between a few primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) using conventional magnetic resonance imaging techniques. The purpose of this study is to explore whether diffusion-weighted imaging (DWI) can be effectively used to differentiate between these two types of tumors by analyzing the apparent diffusion coefficient (ADC).

Research design and methods: Data presented in Pubmed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Database, and China Science and Technology Journal Database (CQVIP) were analyzed. High-quality literature was included, and the quality was evaluated using the quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool, and the studies were based on the inclusion and exclusion rules. The pooled sensitivity, pooled specificity, pooled positive likelihood ratio (PLR), pooled negative likelihood ratio (NLR), pooled diagnostic odds ratio (DOR), area under the curve (AUC) of the summary operating characteristic curve (SROC), and corresponding 95% confidence interval (CI) were calculated using the bivariate mixed effect model. Meta-regression analysis and subgroup analysis were used to explore the sources of heterogeneity. The publication bias was evaluated by conducting Deek's test.

Results: In total, eighteen high-quality studies were included. The pooled sensitivity was 0.82 (95% CI: 0.75-0.88), the pooled specificity was 0.87 (95% CI: 0.84-0.90), the pooled positive likelihood ratio was 6.49 (95% CI: 5.06-8.32), the pooled NLR was 0.21 (95% CI: 0.14-0.30), the pooled DOR was 31.31 (95% CI: 18.55-52.86), and the pooled AUC was 0.90 (95% CI: 0.87-0.92). Sample size, language and country of publication, magnetic field strength, region of interest (ROI), and cut-off values of different types of ADC can potentially be the sources of heterogeneity. There was no publication bias in this meta-analysis.

Conclusions: The results obtained from the meta-analysis suggest that DWI is characterized by high diagnostic accuracy and thus can be effectively used for differentiating between PCNSL and HGG.

Keywords: diagnosis; diffusion-weighted imaging; high-grade glioma; lymphoma; meta-analysis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A Flowchart representing the literature selection process.
Figure 2
Figure 2
A document quality evaluation chart prepared using the QUADAS-2 tool.
Figure 3
Figure 3
Forest plot generated for pooled (A) sensitivity, (B) specificity, (C) positive likelihood ratio (PLR), (D) negative likelihood ratio (NLR), (E) diagnostic odds ratio (DOR), and (F) area under the curve (AUC) recorded for the summary receiver operating characteristic (SROC) curve.
Figure 4
Figure 4
Fagan's nomogram for evaluating the post-test probability.
Figure 5
Figure 5
Univariable meta-regression analysis for the sensitivity and specificity of DWI for differentiating between primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG).
Figure 6
Figure 6
Results from sensitivity analysis. (A) Goodness-of-fit, (B) bivariate normality, (C) influence analysis, and (D) outlier detection.
Figure 7
Figure 7
Publication bias determined by conducting Deeks' test.
See this image and copyright information in PMC

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