Objective Intrathecal inflammation correlates with the grey matter damage since the early stages of multiple sclerosis (MS), but whether the cerebrospinal fluid (CSF) profile can help to identify patients at risk of disease activity is still unclear. Methods We evaluated the association between CSF levels of 18 cytokines, previously found to be associated to grey matter damage, and the disease activity, among 99 patients with relapsing‐remitting MS, who underwent blinded clinical and 3 T magnetic resonance imaging (MRI) evaluations for 4 years. Groups with evidence of disease activity (EDA) or no evidence of disease activity (NEDA; occurrence of relapses, new white matter lesions, and Expanded Disability Status Scale [EDSS] change) were identified. Cortical lesions and the annualized cortical thinning were also evaluated. Results Forty‐one patients experienced EDA and, compared to the NEDA group, had at diagnosis higher CSF levels of CXCL13, CXCL12, IFNγ, TNF, sCD163, LIGHT, and APRIL (p < 0.001). In the multivariate analysis, CXCL13 (hazard ratio [HR] = 1.35; p = 0.0002), LIGHT (HR = 1.22; p = 0.005) and APRIL (HR = 1.78; p = 0.0001) were the CSF molecules more strongly associated with the risk of EDA. The model, including CSF variables, predicted more accurately the occurrence of disease activity than the model with only clinical/MRI parameters (C‐index at 4 years = 71% vs 44%). Finally, higher CSF levels of CXCL13 (β = 4.7*10−4; p < 0.001), TNF (β = 3.1*10−3; p = 0.004), LIGHT (β = 2.6*10−4; p = 0.003), sCD163 (β = 4.3*10−3; p = 0.009), and TWEAK (β = 3.4*10−3; p = 0.024) were associated with more severe cortical thinning. Interpretation A specific CSF profile, mainly characterized by elevated levels of B‐cell related cytokines, distinguishes patients at high risk of disease activity and severe cortical damage. The CSF analysis may allow stratifications of patients at diagnosis for optimizing therapeutic approaches. ANN NEUROL 2020;88:562–573

Background: The central vein sign (CVS) is a radiological feature proposed as a multiple sclerosis (MS) imaging biomarker able to accurately differentiate MS from other white matter diseases of the central nervous system. In this work, we evaluated the pooled proportion of the CVS in brain MS lesions and to estimate the diagnostic performance of CVS to perform a diagnosis of MS and propose an optimal cut-off value. Methods: A systematic search was performed on publicly available databases (PUBMED/MEDLINE and Web of Science) up to 24 August 2020. Analysis of the proportion of white matter MS lesions with a central vein was performed using bivariate random-effect models. A meta-regression analysis was performed and the impact of using particular sequences (such as 3D echo-planar imaging) and post-processing techniques (such as FLAIR*) was investigated. Pooled sensibility and specificity were estimated using bivariate models and meta-regression was performed to address heterogeneity. Inclusion and publication bias were assessed using asymmetry tests and a funnel plot. A hierarchical summary receiver operating curve (HSROC) was used to estimate the summary accuracy in diagnostic performance. The Youden index was employed to estimate the optimal cut-off value using individual patient data. Results: The pooled proportion of lesions showing a CVS in the MS population was 73%. The use of the CVS showed a remarkable diagnostic performance in MS cases, providing a pooled specificity of 92% and a sensitivity of 95%. The optimal cut-off value obtained from the individual patient data pooled together was 40% with excellent accuracy calculated by the area under the ROC (0.946). The 3D-EPI sequences showed both a higher pooled proportion compared to other sequences and explained heterogeneity in the meta-regression analysis of diagnostic performances. The 1.5 Tesla (T) scanners showed a lower (58%) proportion of MS lesions with a CVS compared to both 3T (74%) and 7T (82%). Conclusions: The meta-analysis we have performed shows that the use of the CVS in differentiating MS from other mimicking diseases is encouraged; moreover, the use of dedicated sequences such as 3D-EPI and the high MRI field is beneficial.

Background: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. Methods: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon twosample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. Results: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1).

This article focuses on clinical applications of arterial spin labeling (ASL) and is part of a wider effort from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group to update and expand on the recommendations provided in the 2015 ASL consensus paper. Although the 2015 consensus paper provided general guidelines for clinical applications of ASL MRI, there was a lack of guidance on disease-specific parameters. Since that time, the clinical availability and clinical demand for ASL MRI has increased. This position paper provides guidance on using ASL in specific clinical scenarios, including acute ischemic stroke and steno-occlusive disease, arteriovenous malformations and fistulas, brain tumors, neurodegenerative disease, seizures/epilepsy, and pediatric neuroradiology applications, focusing on disease-specific considerations for sequence optimization and interpretation. We present several neuroradiological applications in which ASL provides unique information essential for making the diagnosis. This guidance is intended for anyone interested in using ASL in a routine clinical setting (i.e., on a single-subject basis rather than in cohort studies) building on the previous ASL consensus review.

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