MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis

Suh, Chong Hyun; Kim, Ho Sung; Jung, Sung Eun; Park, Ji Eun; Choi, Choong Gon; Kim, Sang Joon

doi:10.1002/jmri.26602

Cited by 48 publications

(44 citation statements)

References 46 publications

(367 reference statements)

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“…IVIM-DWI is a non-invasive technique that shows superiority in reflecting tumor cellularity and perfusion without requiring a contrast agent. It has already been applied in the differentiation of lung nodules ( 29 ), thyroid nodules ( 30 ), and breast ( 31 ) and brain tumors ( 32 ) with good diagnostic performance. To our best knowledge, no prostate study with a large sample size has been reported to determine the value of IVIM to quantitatively distinguish PCa from non-cancerous tissues and identify the tumor Gleason grades, in the background of IVIM becoming a research hotspot in whole-body tumors.…”

Section: Discussionmentioning

confidence: 99%

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Xie³

et al. 2020

Front. Oncol.

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Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa. Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D * ), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities. Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D * value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D * , and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D * (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76). Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.

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Section: Discussionmentioning

confidence: 99%

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Xie³

et al. 2020

Front. Oncol.

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“…Magnetic resonance imaging (MRI) generally enables the noninvasive differentiation of typical PCNSL from GBM and advanced MRI techniques, such as dynamic contrastenhanced imaging, arterial spin labeling, magnetic resonance spectroscopy, and dynamic susceptibility-weighted contrastenhanced imaging, have been helpful in the differentiation of more complex cases. 8 However, extra expense and time are needed to perform advanced MRI techniques and these are generally not performed in routine clinical practice. 9 Conventional multiparametric-MRI (MP-MRI) including contrastenhanced T 1 -weighted imaging (CE-T 1 WI), fluid-attenuation inversion recovery (FLAIR), and diffusion-weighted imaging (DWI) are almost always performed.…”

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confidence: 99%

Multiparametric‐MRI‐Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross‐Vendor Validation

Wei

et al. 2020

Magnetic Resonance Imaging

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Background: Preoperative differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) is important to guide neurosurgical decision-making. Purpose: To validate the generalization ability of radiomics models based on multiparametric-MRI (MP-MRI) for differentiating PCNSL from GBM. Study Type: Retrospective. Population: In all, 240 patients with GBM (n = 129) or PCNSL (n = 111). Field Strength/Sequence: 3.0T scanners (two vendors). Sequences: fluid-attenuation inversion recovery, diffusionweighted imaging (DWI), and contrast-enhanced T 1-weighted imaging (CE-T 1 WI). Apparent diffusion coefficients (ADCs) were derived from DWI. Assessment: Cross-vendor and mixed-vendor validation were conducted. In cross-vendor validation, the training set was 149 patients' data from vendor 1, and test set was 91 patients' data from vendor 2. In mixed-vendor validation, a training set was 80% of data from both vendors, and the test set remained at 20% of data. Single and multisequence radiomics models were built. The diagnoses by radiologists with 5, 10, and 20 years' experience were obtained. The integrated models were built combining the diagnoses by the best-performing radiomics model and each radiologist. Model performance was validated in the test set using area under the ROC curve (AUC). Histological results were used as the reference standard. Statistical Tests: DeLong test: differences between AUCs. U-test: differences of numerical variables. Fisher's exact test: differences of categorical variables. Results: In cross-vendor and mixed-vendor validation, the combination of CE-T 1 WI and ADC produced the bestperforming radiomics model, with AUC of 0.943 vs. 0.935, P = 0.854. The integrated models had higher AUCs than radiologists, with 5 (0.975 vs. 0.891, P = 0.002 and 0.995 vs. 0.885, P = 0.007), 10 (0.975 vs. 0.913, P = 0.029 and 0.995 vs. 0.900, P = 0.030), and 20 (0.975 vs. 0.945, P = 0.179 and 0.995 vs. 0.923, P = 0.046) years' experiences. Data Conclusion: Radiomics for differentiating PCNSL from GBM was generalizable. The model combining MP-MRI and radiologists' diagnoses had superior performance compared to the radiologists alone. Level of Evidence: 4 Technical Efficacy Stage: 2

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“…To our knowledge, this is the first systematic review aiming at summarizing the role of diffusion MR imaging metrics in predicting survival in GBM patients. To date, systematic studies exploring diffusion metrics applied to GBM patients aimed at evaluating the power of DWI and DTI for differentiating GBM from brain metastasis or PCNSL, or from lower grade gliomas [ 32 , 33 , 88 ]. However, analysis of survival outcomes was out of the purpose of these works.…”

Section: Discussionmentioning

confidence: 99%

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics—A Systematic Review

Brancato

Nuzzo

Tramontano

et al. 2020

Cancers

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Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

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MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis

Cited by 48 publications

References 46 publications

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Multiparametric‐MRI‐Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross‐Vendor Validation

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics—A Systematic Review

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