Background: Ublituximab, a novel monoclonal antibody (mAb) targeting a unique epitope on the CD20 antigen, is glycoengineered for enhanced B-cell targeting through antibody-dependent cellular cytotoxicity (ADCC). Greater ADCC may allow lower doses and shorter infusion times versus other anti-CD20 mAbs. Objective: The objective was to determine optimal dose, infusion time, and activity of ublituximab in relapsing multiple sclerosis. Methods: This is a phase 2, placebo-controlled study. Patients received three ublituximab infusions (150 mg over 1–4 hours on day 1 and 450–600 mg over 1–3 hours on day 15 and week 24) in six dosing cohorts. The primary endpoint was B-cell depletion. Results: In all cohorts ( N = 48), median B-cell depletion was >99% by week 4, maintained at weeks 24 and 48. Most common adverse events (AEs) were infusion-related reactions (all grade 1–2), with no apparent increased incidence at shorter infusion times. There were no AE-related discontinuations. At weeks 24 and 48, no T1 gadolinium-enhancing lesions ( p = 0.003) and a 10.6% decrease in T2 lesion volume ( p = 0.002) were detected. The annualized relapse rate was 0.07; 93% remained relapse free on study. Overall, 74% of patients had no evidence of disease activity (NEDA). Conclusion: Ublituximab was safely infused as rapid as 1 hour, producing robust B-cell depletion and profound reductions in magnetic resonance imaging (MRI) activity and relapses.

The authors describe an Italian family with autosomal dominant ataxia, dementia, psychiatric and extrapyramidal features, epilepsy, mild sensorimotor axonal neuropathy, and MRI findings of cerebral and cerebellar atrophy. A child had a distinctive presentation with onset at 3 years, growth retardation, fast progression, and early death. Molecular analysis demonstrated an expanded CAG/CAA repeat in the TBP gene (SCA-17). The repeat size was 66 triplets in the child and 53 in all the other patients.

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• In multiple sclerosis, previous Gadolinium administrations correlate with dentate nuclei T1 relaxometry. • Such correlation is linked to linear Gadolinium chelates and unrelated to disease duration or severity. • Dentate nuclei T2* relaxometry is age-related and independent of previous Gadolinium administrations. • Changes in dentate nuclei T1 relaxometry are not determined by iron accumulation. • MR relaxometry can quantitatively assess Gadolinium accumulation in dentate nuclei.

Objective: Friedreich ataxia (FRDA) is an inherited neurological disease defined by progressive movement incoordination. We undertook a comprehensive characterization of the spatial profile and progressive evolution of structural brain abnormalities in people with FRDA.

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human wholebrain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Gadolinium-based contrast agents (GBCA), widely used in Magnetic Resonance Imaging (MRI) for almost 30 years, were recently shown to be deposited in the brain and to induce persistent T1 shortening in deep gray matter structures in subjects with normal renal function. The aim of the present study is to summarize the evidence derived from the rapidly growing scientific literature on Gadolinium retention in the brain and in the rest of the body. To this end, the original articles that described imaging and pathology findings in humans and animals exposed to GBCA were reviewed. The main aspects that emerged were the different effects of linear and macrocyclic GBCA on brain MRI appearance, the evidence of Gadolinium tissue retention in multiple organs, and the debated issue of the possible clinical consequences. Although no adverse health effects have been documented so far, updated information about GBCA build-up in the body is necessary for health professionals, also in view of the increasing concern in the general population. To date, our knowledge about the mechanisms of Gadolinium tissue deposition and, above all, its long-term consequences is still largely incomplete. However, while official guidelines are eagerly awaited, some advices may already be given, to help our radiological daily practice.

Background/Aim: To investigate whether a radiomic machine learning (ML) approach employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able to predict tumor grade (TG) and nodal status (NS) in patients with oropharyngeal (OP) and oral cavity (OC) squamous-cell carcinoma (SCC). Patients and Methods: Contrast-enhanced CT images of 40 patients with OP and OC SCC were post-processed to extract TA features from PTLs. A feature selection method and different ML algorithms were applied to find the most accurate subset of features to predict TG and NS. Results: For the prediction of TG, the best accuracy (92.9%) was achieved by Naïve Bayes (NB), bagging of NB and K Nearest Neighbor (KNN). For the prediction of NS, J48, NB, bagging of NB and boosting of J48 overcame the accuracy of 90%. Conclusion: A radiomic ML approach applied to PTLs is able to predict TG and NS in patients with OC and OP SCC.