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

Imaging Role in Diagnosis, Prognosis, and Treatment Response Prediction Associated with High-grade Glioma

Background

Glioma is one of the most drug and radiation-resistant tumors. Gliomas suffer from inter- and intratumor heterogeneity which makes the outcome of similar treatment protocols vary from patient to patient. This article is aimed to overview the potential imaging markers for individual diagnosis, prognosis, and treatment response prediction in malignant glioma. Furthermore, the correlation between imaging findings and biological and clinical information of glioma patients is reviewed.

Materials and Methods

The search strategy in this study is to select related studies from scientific websites such as PubMed, Scopus, Google Scholar, and Web of Science published until 2022. It comprised a combination of keywords such as Biomarkers, Diagnosis, Prognosis, Imaging techniques, and malignant glioma, according to Medical Subject Headings.

Results

Some imaging parameters that are effective in glioma management include: ADC, FA, Ktrans, regional cerebral blood volume (rCBV), cerebral blood flow (CBF), ve, Cho/NAA and lactate/lipid ratios, intratumoral uptake of 18F-FET (for diagnostic application), RD, ADC, ve, vp, Ktrans, CBFT1, rCBV, tumor blood flow, Cho/NAA, lactate/lipid, MI/Cho, uptakes of 18F-FET, 11C-MET, and 18F-FLT (for prognostic and predictive application). Cerebral blood volume and Ktrans are related to molecular markers such as vascular endothelial growth factor (VEGF). Preoperative ADCmin value of GBM tumors is associated with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. 2-hydroxyglutarate metabolite and dynamic 18F-FDOPA positron emission tomography uptake are related to isocitrate dehydrogenase (IDH) mutations.

Conclusion

Parameters including ADC, RD, FA, rCBV, Ktrans, vp, and uptake of 18F-FET are useful for diagnosis, prognosis, and treatment response prediction in glioma. A significant correlation between molecular markers such as VEGF, MGMT, and IDH mutations with some diffusion and perfusion imaging parameters has been identified.

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

Background and objective

Numerous radiomics-based models have been proposed to discriminate between central nervous system (CNS) gliomas and primary central nervous system lymphomas (PCNSLs). Given the heterogeneity of the existing models, we aimed to define their overall performance and identify the most critical variables to pilot future algorithms.

Methods

A systematic review of the literature and a meta-analysis were conducted, encompassing 12 studies and a total of 1779 patients, focusing on radiomics to differentiate gliomas from PCNSLs. A comprehensive literature search was performed through PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus databases. Overall sensitivity (SEN) and specificity (SPE) were estimated. Event rates were pooled using a random-effects meta-analysis, and the heterogeneity was assessed using the χ2 test.

Results

The overall SEN and SPE for differentiation between CNS gliomas and PCNSLs were 88% (95% CI = 0.83 - 0.91) and 87% (95% CI = 0.83 - 0.91), respectively. The best-performing features were the ones extracted from the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by those obtained from the Neighboring Gray Tone Difference Matrix (NGTDM; ACC 93%), and shape-based features (ACC 91%). The 18F-FDG-PET/CT was the best-performing imaging modality (ACC 97%), followed by the MRI CE-T1W (ACC 87% - 95%). Most studies applied a cross-validation analysis (92%).

Conclusion

The current SEN and SPE of radiomics to discriminate CNS gliomas from PCNSLs are high, making radiomics a helpful method to differentiate these tumor types. The best-performing features are the GLRLM, NGTDM, and shape-based features. The 18F-FDG-PET/CT imaging modality is the best-performing, while the MRI CE-T1W is the most used.

Differentiating nasopharyngeal carcinoma from lymphoma in the head and neck region using the apparent diffusion coefficient (ADC) value: a systematic review and meta-analysis

Purpose

This study aimed to assess the applicability of the apparent diffusion coefficient (ADC) for differentiating nasopharyngeal carcinoma (NPC) from lymphomas in the head and neck region.

Material and methods

Four databases, including PubMed, the Cochrane Library, EMBASE, and Web of Science, were searched systematically to find relevant literature. The search date was updated to 8 September 2022, with no starting time restriction. The methodological quality of the studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Firstly, a random-effects model was used in a meta-analysis of continuous variables with low heterogeneity to determine the overall effect size, which was reported as the standard mean difference (SMD). Then, bivariate random effects modelling was used to calculate the combined sensitivity and specificity. The area under the curve (AUC) for each diffusion parameter was calculated after constructing summary receiver operating characteristic curves. The presence of heterogeneity was evaluated using subgroup and meta-regression analysis.

Results

Twelve studies involving 181 lymphoma and 449 NPC lesions (N = 630) in the head and neck region were included, of which 5 studies provided sufficient data for pooling diagnostic test accuracy. A meta-analysis of the 12 studies using a random-effects model yielded an SMD of 1.03 (CI = 0.76-1.30; p = 0.00001), implying that NPC lesions had a significantly higher ADC value than lymphoma lesions. By pooling 5 standard DWI studies, the pooled sensitivity and specificity of ADC were 0.90 (95% CI: 0.82-0.95) and 0.63 (95% CI: 0.52-0.72), respectively. The area under the curve (AUC) calculated from the SROC curve was 0.74 (95% CI: 0.70-0.78).

Conclusions

According to this systematic review and meta-analysis, nasopharyngeal carcinoma has a significantly higher ADC value than lymphomas. Furthermore, while ADC has excellent sensitivity for distinguishing these 2 types of tumours, its specificity is relatively low, yielding a moderate diagnostic performance. Further investigations with larger sample sizes are required.

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

BACKGROUND

Primary central nervous system lymphoma (PCNSL) is a rare malignant tumor originating from the lymphatic hematopoietic system. It exhibits unique imaging manifestations due to its biological characteristics.

CASE SUMMARY

Magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy was performed. The imaging findings showed multiple space-occupying lesions with low signal on T1-weighted imaging, uniform high signal on T2-weighted imaging, and obvious enhancement on contrast-enhanced scans. DWI revealed diffusion restriction, PWI demonstrated hypoperfusion, and spectroscopy showed elevated choline peak and decreased N-acetylaspartic acid. The patient's condition significantly improved after hormone shock therapy.

CONCLUSION

This case highlights the distinctive imaging features of PCNSL and their importance in accurate diagnosis and management.

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

Background

Primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) appear similar under imaging. However, since the two tumors vary in their treatment methods, their differential diagnosis is crucial. The use of 18F-fluorodeoxyglucose positron emission tomography computed tomography (18F-FDG-PET/CT) imaging to effectively distinguish between the two tumors is not clear; therefore, a meta-analysis was carried out to determine its effectiveness.

Materials and methods

The databases PubMed, EMBASE, Cochrane, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, China Science, and Technology Journal Database (CQVIP) were exhaustively searched using stringent inclusion and exclusion criteria to select high-quality literature. The Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) was used for the qualitative assessment of the included literature. The bivariate effect model was used to combine statistics such as sensitivity (SEN) and specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) [95% confidence intervals (CI)], plot summary receiver operating characteristic (SROC) curve, and calculate the area under the curve (AUC) value. Sensitivity analysis was used to evaluate the stability of the results, and Deek's test was used to assess publication bias. Meta-regression and subgroup analysis was used to determine the sources of heterogeneity.

Results

A total of nine studies were included in this study. For differential diagnosis of PCNSL and HGG, the combined SEN was 0.91 (95% CI: 0.80–0.96; I² = 46.73%), combined SPE was 0.88 (95% CI: 0.82–0.93; I² = 56.30%), the combined PLR was 7.83 (95% CI: 4.96–12.37; I² = 15.57%), combined NLR was 0.10 (95% CI: 0.05–0.23; I² = 31.99%), combined DOR was 77.36 (95% CI: 32.74–182.77; I² = 70.70%). The AUC of SROC was 0.95 (95% CI: 0.93–0.97). No publication bias was found and the sample size and different parameters were the primary reason for heterogeneity.

Conclusion

The 18F-FDG-PET/CT imaging technique has a high diagnostic accuracy in the differential diagnosis of PNCSL and HGG. Patients suspected to have the above two tumors are suggested to be examined by 18F-FDG-PET / CT to help in the clinical distinction and further treatment modalities.