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Objectives: To investigate the pooled diagnostic yield of MR myelography in patients with newly diagnosed spontaneous intracranial hypotension (SIH). Methods: A literature search of the MEDLINE/PubMed and Embase databases was conducted until July 25, 2021, including studies with the following inclusion criteria: (a) population: patients with newly diagnosed SIH; (b) diagnostic modality: MR myelography or MR myelography with intrathecal gadolinium for evaluation of CSF leakage; (c) outcomes: diagnostic yield of MR myelography or MR myelography with intrathecal gadolinium. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. DerSimonian–Laird random-effects modeling was used to calculate the pooled estimates. Subgroup analysis regarding epidural fluid collection and meta-regression were additionally performed. Results: Fifteen studies with 643 patients were included. Eight studies used MR myelography with intrathecal gadolinium, and 11 used MR myelography. The overall quality of the included studies was moderate. The pooled diagnostic yield of MR myelography was 86% (95% CI, 80–91%) and that of MR myelography with intrathecal gadolinium was 83% (95% CI, 51–96%). There was no significant difference in pooled diagnostic yield between MR myelography and MR myelography with intrathecal gadolinium (p = 0.512). In subgroup analysis, the pooled diagnostic yield of the epidural fluid collection was 91% (95% CI, 84–94%). In meta-regression, the diagnostic yield was unaffected regardless of consecutive enrollment, magnet strength, or 2D/3D. Conclusions: MR myelography had a high diagnostic yield in patients with SIH. MR myelography is non-invasive and not inferior to MR myelography with intrathecal gadolinium. Key Points: • The pooled diagnostic yield of MR myelography was 86% (95% CI, 80–91%) in patients with spontaneous intracranial hypotension. • There was no significant difference in pooled diagnostic yield between MR myelography and MR myelography with intrathecal gadolinium. • MR myelography is non-invasive and not inferior to MR myelography with intrathecal gadolinium. [ABSTRACT FROM AUTHOR]

Purpose: Grading of brain gliomas is of clinical importance, and noninvasive molecular imaging may help differentiate low- and high-grade gliomas. We aimed to evaluate the diagnostic performance of amide proton transfer-weighted (APTw) MRI for differentiating low- and high-grade gliomas on 3-T scanners. Methods: A systematic literature search of Ovid-MEDLINE and EMBASE was performed up to March 28, 2018. Original articles evaluating the diagnostic performance of APTw MRI for differentiating low- and high-grade gliomas were selected. The pooled sensitivity and specificity were calculated using a bivariate random-effects model. A coupled forest plot and a hierarchical summary receiver operating characteristic curve were obtained. Heterogeneity was investigated using Higgins inconsistency index (I2) test. Meta-regression was performed. Results: Ten original articles with a total of 353 patients were included. High-grade gliomas showed significantly higher APT signal intensity than low-grade gliomas. The pooled sensitivity and specificity for the diagnostic performance of APTw MRI for differentiating low-grade and high-grade gliomas were 88% (95% CI, 77–94%) and 91% (95% CI, 82–96%), respectively. Higgins I2 statistic demonstrated heterogeneity in the sensitivity (I2 = 68.17%), whereas no heterogeneity was noted in the specificity (I2 = 44.84%). In meta-regression, RF saturation power was associated with study heterogeneity. Correlation coefficients between APT signal intensity and Ki-67 cellular proliferation index ranged from 0.430 to 0.597, indicating moderate correlation. All studies showed excellent interobserver agreement. Conclusions: Although heterogeneous protocols were used, APTw MRI demonstrated excellent diagnostic performance for differentiating low- and high-grade gliomas. APTw MRI could be a reliable technique for glioma grading in clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters.Methods: A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity.Results: Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%).Conclusions: Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis.Key Points: • Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated. [ABSTRACT FROM AUTHOR]

Progressive supranuclear palsy (PSP) and Parkinson's disease (PD) are difficult to differentiate especially in the early stages. We aimed to investigate the diagnostic performance of the magnetic resonance parkinsonism index (MRPI) in differentiating PSP from PD. A systematic literature search of PubMed-MEDLINE and EMBASE was performed to identify original articles evaluating the diagnostic performance of the MRPI in differentiating PSP from PD published up to 20 February 2021. The pooled sensitivity, specificity, and 95% CI were calculated using the bivariate random-effects model. The area under the curve (AUC) was calculated using a hierarchical summary receiver operating characteristic (HSROC) model. Meta-regression was performed to explain the effects of heterogeneity. A total of 14 original articles involving 484 PSP patients and 1243 PD patients were included. In all studies, T1-weighted images were used to calculate the MRPI. Among the 14 studies, nine studies used 3D T1-weighted images. The pooled sensitivity and specificity for the diagnostic performance of the MRPI in differentiating PSP from PD were 96% (95% CI, 87–99%) and 98% (95% CI, 91–100%), respectively. The area under the HSROC curve was 0.99 (95% CI, 0.98–1.00). Heterogeneity was present (sensitivity: I2 = 97.29%; specificity: I2 = 98.82%). Meta-regression showed the association of the magnet field strength with heterogeneity. Studies using 3 T MRI showed significantly higher sensitivity (100%) and specificity (100%) than those of studies using 1.5 T MRI (sensitivity of 98% and specificity of 97%) (p < 0.01). Thus, the MRPI could accurately differentiate PSP from PD and support the implementation of appropriate management strategies for patients with PSP. [ABSTRACT FROM AUTHOR]

We aimed to evaluate the pooled incidence of central vein sign on T2*-weighted images from patients with multiple sclerosis (MS), and to determine the diagnostic performance of this central vein sign for differentiating MS from other white matter lesions and provide an optimal cut-off value. A computerized systematic search of the literature in PUBMED and EMBASE was conducted up to December 14, 2018. Original articles investigating central vein sign on T2*-weighted images of patients with MS were selected. The pooled incidence was obtained using random-effects model. The pooled sensitivity and specificity were obtained using a bivariate random-effects model. An optimal cut-off value for the proportion of lesions with a central vein sign was calculated from those studies providing individual patient data. Twenty-one eligible articles covering 501 patients with MS were included. The pooled incidence of central vein sign at the level of individual lesion in patients with MS was 74% (95% CI, 65–82%). The pooled sensitivity and pooled specificity for the diagnostic performance of the central vein sign were 98% (95% CI, 92–100%) and 97% (95% CI, 91–99%), respectively. The area under the HSROC curve was 1.00 (95% CI, 0.99–1.00). The optimal cut-off value for the proportion of lesions with a central vein sign was found to be 45%. Although various T2*-weighted images have been used across studies, the current evidence supports the use of the central vein sign on T2*-weighted images to differentiate MS from other white matter lesions. [ABSTRACT FROM AUTHOR]