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

Abstract

BackgroundIt 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 MethodsData 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.ResultsIn 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.ConclusionsThe 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.

Radiomics for differentiation of gliomas from primary central nervous system lymphomas: a systematic review and meta-analysis

“…For instance, a study used DWI in association with ADC to differentiate between CNS gliomas and PCNSLs, but the findings indicated a slightly lower diagnostic performance, with a SEN of 82% (95% CI 0.75-0.88) and a SPE of 87% (95% CI 0.84-0.90) ( 27 ). Similarly, in another study, the diagnostic efficacy of DWI was investigated for the same discrimination task, but it also exhibited lower performance, with SEN at 82% (95% CI 0.70-0.90) and SPE at 84% (95% CI 0.75-0.90) ( 28 ).…”

Distinctive magnetic resonance imaging features in primary central nervous system lymphoma: A case report

“…MRS plays a crucial role in the evaluation of PCNSL. Elevated choline peak, decreased N-acetylaspartic acid, and a towering lipid peak are commonly observed[ 12 ]. The towering lipid peak is highly specific for the diagnosis of PCNSL, attributed to the accelerated turnover of lymphocytes and macrophages.…”

Use of 18F-FDG-PET/CT in differential diagnosis of primary central nervous system lymphoma and high-grade gliomas: A meta-analysis

“…In addition to Computed Tomography (CT), the primary method for imaging of craniocerebral diseases is craniocerebral Magnetic Resonance Imaging (MRI), which has the ability to diagnose as well as differentially diagnose intracranial diseases (23)(24)(25). For example, a meta-analysis of 18 studies by Du et al suggested that DWI could differentiate between PCNSL and HGG with an AUC of 0.90 (26). Another meta-analysis of 10 articles by You et al suggested that arterial spin labeling could differentiate between PCNSL and HGG with an AUC of PET / CT imaging is an advanced imaging technology that can simultaneously capture the cellular, molecular level images and anatomical structures through an image-fusion technology.…”