Abstract Autologous haematopoietic stem cell transplantation is an effective treatment for aggressive multiple sclerosis refractory to disease-modifying therapies. Yet, its impact on neurodegeneration remains underexplored, and few predictive biomarkers for clinical outcomes after autologous haematopoietic stem cell transplantation exist. We investigated whether autologous haematopoietic stem cell transplantation attenuates retinal neurodegeneration and whether optical coherence tomography-derived retinal measures predict disability progression after transplantation. In this single-centre longitudinal cohort study, optical coherence tomography was performed in people with multiple sclerosis treated with autologous haematopoietic stem cell transplantation and non-transplanted controls with relapsing-remitting multiple sclerosis. Retinal layer atrophy rates pre-transplantation and up to 36 months post-transplantation were estimated using linear mixed-effects models, and post-transplantation rates were compared with those of non-transplanted controls. Cumulative link mixed models were used to assess whether baseline retinal layer thickness predicted clinical progression after transplantation. The autologous haematopoietic stem cell transplantation cohort included 39 participants [23/39 (59%) female], comprising 23 with relapsing-remitting [15/23 (65%) female], 8 with secondary progressive [5/8 (62.5%) female] and 8 with primary progressive multiple sclerosis [3/8 (37.5%) female]. The relapsing-remitting multiple sclerosis control cohort on disease-modifying treatment included 48 participants [31/48 (65%) female]. In relapsing-remitting multiple sclerosis, autologous haematopoietic stem cell transplantation reduced thinning of the ganglion cell/inner plexiform layer by 0.65 µm/year (95% CI 0.16 to 1.15, P = 0.010), temporal-quadrant peripapillary retinal-nerve-fibre layer by 1.22 µm/year (95% CI 0.63 to 1.81, P < 0.001) and papillomacular bundle peripapillary retinal-nerve-fibre layer by 1.59 µm/year (95% CI 0.89 to 2.29, P < 0.001). Post-transplantation atrophy rates of the global, temporal and papillomacular-bundle peripapillary retinal nerve fibre layer were lower in the autologous haematopoietic stem cell transplantation cohort than in the control cohort by 0.275 µm/year (95% CI 0.048 to 0.502, P = 0.014), 0.294 µm/year (95% CI 0.118 to 0.470, P = 0.001) and 0.200 µm/year (95% CI 0.043 to 0.357, P = 0.015), respectively. Higher baseline inner nuclear layer thickness predicted better disability outcomes after transplantation in both relapsing and progressive multiple sclerosis in exploratory statistical models (P < 0.001). In conclusion, retinal optical coherence tomography demonstrated reduced retinal atrophy rates in relapsing-remitting multiple sclerosis after autologous haematopoietic stem cell transplantation. Higher inner nuclear layer thickness before transplantation predicted more favourable neurological outcomes, indicating that the inner nuclear layer, a retinal layer linked to inflammatory multiple sclerosis activity, may help identify patients most likely to benefit from autologous haematopoietic stem cell transplantation.

BACKGROUND AND OBJECTIVES:Retinal optical coherence tomography (OCT) in rodent models has been used to longitudinally image retinal changes, to define end points for more costly or time-consuming experiments, and to better understand the pathophysiology underlying OCT findings in human diseases. No standardization of rodent OCT reporting currently exists. Here, we aim to establish consensus recommendation for reporting results from retinal OCT studies in rodents. METHODS:Initial recommendations were developed based on the APOSTEL criteria for quantitative OCT reporting in humans by a core team. Using a modified Delphi process, an expert panel of rodent OCT researchers (N = 31) and the wider scientific community discussed, refined, and voted on these initial recommendations. The list of recommendations was then revised and approved by the expert panel. RESULTS:The final 7-point checklist includes reporting recommendations regarding the study protocol, OCT device, acquisition settings and modifications, scanning protocol, funduscopic imaging, postacquisition data selection and image data analyses, and qualitative and quantitative results. With a median agreement score of 3 or 4 out of 4, the scientific community agreed with these recommendations. After revisions, the expert panel accepted the final recommendations. DISCUSSION:The Advised Protocol for OCT Study Terminology and Elements for reporting OCT studies in rodents (APOSTEL-R) originates from an expert consensus. They will provide guidance throughout the experimental process and will contribute to the standardization and quality improvement of preclinical OCT studies.

The aim of this study was to investigate the dynamics of annual whole brain volume loss (BVL/year) and annual thalamic volume loss (ThalaVL/year) in patients with relapsing-remitting multiple sclerosis (PwRRMS) during the course of the disease. A longitudinal database of magnetic resonance imaging (MRI) scans of 195 healthy individuals (age range, 22.8–63.7 years) and longitudinal MRI data of 256 PwRRMS (age range, 20.1–60.8 years) were analyzed and compared. BVL/year and ThalaVL/year were computed for healthy individuals as well as for all patients with MS using a Jacobian integration approach. A linear regression was used to compute the relationship between age and BVL/year and ThalaVL/year for healthy individuals. The linear regression was then used to decompose the BVL/year and ThalaVL/year into a multiple sclerosis (MS)-related and an age-related component for each PwRRMS. PwRRMS were dichotomized into early-phase RRMS (disease duration ≤ 6 years) and later-phase RRMS (disease duration > 6 years), and a t-test was performed to test for differences between these groups. The 135 early-phase patients (disease duration, ≤ 6 years) had statistically significantly higher MS-related BVL/year than the later-phase patients (n = 121) (− 0.21

MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose “BrainLossNet”, a convolutional neural network (CNN)-based method for BVL-estimation. BrainLossNet uses CNN-based non-linear registration of baseline(BL)/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL/FU scans. The BVL estimate is corrected for image distortions using the apparent volume change of the total intracranial volume. BrainLossNet was trained on 1525 BL/FU pairs from 83 scanners. Agreement between BrainLossNet and SIENA was assessed in 225 BL/FU pairs from 94 MS patients acquired with a single scanner and 268 BL/FU pairs from 52 scanners acquired for various indications. Robustness to short-term variability of 3D-T1w-MRI was compared in 354 BL/FU pairs from a single healthy men acquired in the same session without repositioning with 116 scanners (Frequently-Traveling-Human-Phantom dataset, FTHP). Processing time of BrainLossNet was 2–3 min. The median [interquartile range] of the SIENA-BrainLossNet BVL difference was 0.10

This is a summary of a previously published paper: Joint Healthcare Professional and Patient Development of Communication Tools to Improve the Standard of MS Care. It describes a collaboration between people with multiple sclerosis (PwMS) and healthcare professionals (HCPs) to identify challenges in multiple sclerosis (MS) care and design tools to improve communication during consultations.

Purpose Total intracranial volume (TIV) is often a nuisance covariate in MRI-based brain volumetry. This study compared two TIV adjustment methods with respect to their impact on z -scores in single subject analyses of regional brain volume estimates. Methods Brain parenchyma, hippocampus, thalamus, and TIV were segmented in a normal database comprising 5059 T1w images. Regional volume estimates were adjusted for TIV using the residual method or the proportion method. Age was taken into account by regression with both methods. TIV- and age-adjusted regional volumes were transformed to z -scores and then compared between the two adjustment methods. Their impact on the detection of thalamus atrophy was tested in 127 patients with multiple sclerosis. Results The residual method removed the association with TIV in all regions. The proportion method resulted in a switch of the direction without relevant change of the strength of the association. The reduction of physiological between-subject variability was larger with the residual method than with the proportion method. The difference between z -scores obtained with the residual method versus the proportion method was strongly correlated with TIV. It was larger than one z -score point in 5% of the subjects. The area under the ROC curve of the TIV- and age-adjusted thalamus volume for identification of multiple sclerosis patients was larger with the residual method than with the proportion method (0.84 versus 0.79). Conclusion The residual method should be preferred for TIV and age adjustments of T1w-MRI-based brain volume estimates in single subject analyses.

Background and Objectives To evaluate the long-term safety and efficacy of tocilizumab (TCZ), a humanized anti–interleukin-6 receptor antibody in myelin oligodendrocyte glycoprotein–IgG–associated disease (MOGAD) and neuromyelitis optica spectrum disorders (NMOSD). Methods Annualized relapse rate (ARR), Expanded Disability Status Scale score, MRI, autoantibody titers, pain, and adverse events were retrospectively evaluated in 57 patients with MOGAD (n = 14), aquaporin-4 (AQP4)-IgG seropositive (n = 36), and seronegative NMOSD (n = 7; 12%), switched to TCZ from previous immunotherapies, particularly rituximab. Results Patients received TCZ for 23.8 months (median; interquartile range 13.0–51.1 months), with an IV dose of 8.0 mg/kg (median; range 6–12 mg/kg) every 31.6 days (mean; range 26–44 days). For MOGAD, the median ARR decreased from 1.75 (range 0.5–5) to 0 (range 0–0.9; p = 0.0011) under TCZ. A similar effect was seen for AQP4-IgG+ (ARR reduction from 1.5 [range 0–5] to 0 [range 0–4.2]; p < 0.001) and for seronegative NMOSD (from 3.0 [range 1.0–3.0] to 0.2 [range 0–2.0]; p = 0.031). During TCZ, 60% of all patients were relapse free (79% for MOGAD, 56% for AQP4-IgG+, and 43% for seronegative NMOSD). Disability follow-up indicated stabilization. MRI inflammatory activity decreased in MOGAD (p = 0.04; for the brain) and in AQP4-IgG+ NMOSD (p < 0.001; for the spinal cord). Chronic pain was unchanged. Regarding only patients treated with TCZ for at least 12 months (n = 44), ARR reductions were confirmed, including the subgroups of MOGAD (n = 11) and AQP4-IgG+ patients (n = 28). Similarly, in the group of patients treated with TCZ for at least 12 months, 59% of them were relapse free, with 73% for MOGAD, 57% for AQP4-IgG+, and 40% for patients with seronegative NMOSD. No severe or unexpected safety signals were observed. Add-on therapy showed no advantage compared with TCZ monotherapy. Discussion This study provides Class III evidence that long-term TCZ therapy is safe and reduces relapse probability in MOGAD and AQP4-IgG+ NMOSD.

OBJECTIVE:Automated quantification of infratentorial multiple sclerosis lesions on magnetic resonance imaging is clinically relevant but challenging. To overcome some of these problems, we propose a fully automated lesion segmentation algorithm using 3D convolutional neural networks (CNNs).METHODS:The CNN was trained on a FLAIR image alone or on FLAIR and T1-weighted images from 1809 patients acquired on 156 different scanners. An additional training using an extra class for infratentorial lesions was implemented. Three experienced raters manually annotated three datasets from 123 MS patients from different scanners.RESULTS:The inter-rater sensitivity (SEN) was 80% for supratentorial lesions but only 62% for infratentorial lesions. There was no statistically significant difference between the inter-rater SEN and the SEN of the CNN with respect to the raters. For supratentorial lesions, the CNN featured an intra-rater intra-scanner SEN of 0.97 (R1 = 0.90, R2 = 0.84) and for infratentorial lesion a SEN of 0.93 (R1 = 0.61, R2 = 0.73).CONCLUSION:The performance of the CNN improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input and matches the detection performance of experienced raters. Furthermore, for infratentorial lesions the CNN was more robust against repeated scans than experienced raters.KEY POINTS:• A 3D convolutional neural network was trained on MRI data from 1809 patients (156 different scanners) for the quantification of supratentorial and infratentorial multiple sclerosis lesions. • Inter-rater variability was higher for infratentorial lesions than for supratentorial lesions. The performance of the 3D convolutional neural network (CNN) improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input. • The detection performance of the CNN matches the detection performance of experienced raters.

PURPOSE:Hyperreflective foci are poorly understood transient elements seen on optical coherence tomography (OCT) of the retina in both healthy and diseased eyes. Systematic studies may benefit from the development of automated tools that can map and track such foci. The outer nuclear layer (ONL) of the retina is an attractive layer in which to study hyperreflective foci as it has no fixed hyperreflective elements in healthy eyes. In this study, we intended to evaluate whether automated image analysis can identify, quantify and visualize hyperreflective foci in the ONL of the retina.METHODS:This longitudinal exploratory study investigated 14 eyes of seven patients including six patients with optic neuropathy and one with mild non-proliferative diabetic retinopathy. In total, 2596 OCT B-scan were obtained. An image analysis blob detector algorithm was used to detect candidate foci, and a convolutional neural network (CNN) trained on a manually labelled subset of data was then used to select those candidate foci in the ONL that fitted the characteristics of the reference foci best.RESULTS:In the manually labelled data set, the blob detector found 2548 candidate foci, correctly detecting 350 (89%) out of 391 manually labelled reference foci. The accuracy of CNN classifier was assessed by manually splitting the 2548 candidate foci into a training and validation set. On the validation set, the classifier obtained an accuracy of 96.3%, a sensitivity of 88.4% and a specificity of 97.5% (AUC 0.989).CONCLUSION:This study demonstrated that automated image analysis and machine learning methods can be used to successfully identify, quantify and visualize hyperreflective foci in the ONL of the retina on OCT scans.

The lack of understanding of the cellular and molecular basis of clinical and genetic heterogeneity in progressive multiple sclerosis (MS) has hindered the search for new effective therapies. Here, to address this gap, we analysed 632,000 single nuclei RNAseq profiles of 156 brain tissue samples, comprising white matter (WM) lesions, normal appearing WM, grey matter (GM) lesions and normal appearing GM from 54 MS patients and 26 controls. We observed the expected changes in overall neuronal and glial numbers previously described within the classical lesion subtypes. We found highly cell type-specific gene expression changes in MS tissue, with distinct differences between GM and WM areas, confirming different pathologies. However, surprisingly, we did not observe distinct gene expression signatures for the classical different WM lesion types, rather a continuum of change. This indicates that classical lesion characterization better reflects changes in cell abundance than changes in cell type gene expression, and indicates a global disease effect. Furthermore, the major biological determinants of variability in gene expression in MS WM samples relate to individual patient effects, rather than to lesion types or other metadata. We identify four subgroups of MS patients with distinct WM glial gene expression signatures and patterns of oligodendrocyte stress and/or maturation, suggestive of engagement of different pathological processes, with an additional more variable regenerative astrocyte signature. The discovery of these patterns, which were also found in an independent MS patient cohort, provides a framework to use molecular biomarkers to stratify patients for optimal therapeutic approaches for progressive MS, significantly advances our mechanistic understanding of progressive MS, and highlights the need for precision-medicine approaches to address heterogeneity among MS patients.

The retinal ganglion cells (RGC) may be considered an easily accessible pathophysiological site of degenerative processes in neurological diseases, such as the RGC damage detectable in multiple sclerosis (MS) patients with (HON) and without a history of optic neuritis (NON). We aimed to assess and interrelate RGC functional and structural damage in different retinal layers and retinal sites. We included 12 NON patients, 11 HON patients and 14 healthy controls for cross-sectional multifocal pattern electroretinography (mfPERG) and optical coherence tomography (OCT) measurements. Amplitude and peak times of the mfPERG were assessed. Macula and disc OCT scans were acquired to determine macular retinal layer and peripapillary retinal nerve fiber layer (pRNFL) thickness. In both HON and NON patients the foveal N2 amplitude of the mfPERG was reduced compared to controls. The parafoveal P1 peak time was significantly reduced in HON only. For OCT, parafoveal (pfGCL) and perifoveal (pGCL) ganglion cell layer thicknesses were decreased in HON vs. controls, while pRNFL in the papillomacular bundle sector (PMB) showed reductions in both NON and HON. As the mfPERG derived N2 originates from RGC axons, these findings suggest foveal axonal dysfunction not only in HON, but also in NON patients.

Abstract Objective To evaluate changes over 3 years in the thickness of inner retinal layers including the peripapillary retinal nerve fiber layer (pRNFL), and combined macular ganglion cell and inner plexiform layers (mGCIPL), in individuals with relapsing‐remitting multiple sclerosis (RRMS) versus healthy controls; to determine whether optical coherence tomography (OCT) is sufficiently sensitive and reproducible to detect small degrees of neuroaxonal loss over time that correlate with changes in brain volume and disability progression as measured by the Expanded Disability Status Scale (EDSS). Methods Individuals with RRMS from 28 centers (n = 333) were matched with 64 healthy participants. OCT scans were performed on Heidelberg Spectralis machines (at baseline; 1 month; 6 months; 6‐monthly thereafter). Results OCT measurements were highly reproducible between baseline and 1 month (intraclass correlation coefficient >0.98). Significant inner retinal layer thinning was observed in individuals with multiple sclerosis (MS) compared with controls regardless of previous MS‐associated optic neuritis––group differences (95% CI) over 3 years: pRNFL: −1.86 (−2.54, −1.17) µm; mGCIPL: −2.03 (−2.78, −1.28) µm (both p < 0.0001; effect sizes 0.39 and 0.34). Greater inner retinal layer atrophy was observed in individuals diagnosed with RRMS <3 years versus >5 years (pRNFL: p < 0.05; mGCIPL: p < 0.01). Brain volume decreased by 1.3% in individuals with MS over 3 years compared to 0.5% in control subjects (effect size 0.76). mGCIPL atrophy correlated with brain atrophy (p < 0.0001). There was no correlation of OCT data with disability progression. Interpretation OCT has potential to estimate rates of neurodegeneration in the retina and brain. The effect size for OCT, smaller than for magnetic resonance imaging based on Heidelberg Spectralis data acquired in this study, was increased in early disease.

Background Researchers have in recent years begun to investigate ophthalmological manifestations of multiple sclerosis (MS) other than optic neuritis (ON), and it is now clear that changes to retinal function (measured using the electroretinogram, ERG) and structure (measured using optical coherence tomography, OCT) are found in MS patients irrespective of prior ON episodes. ERG results are consistent with dysfunctional bipolar cells, as in other autoimmune diseases. To date, studies have presented only cross-sectional data regarding ERG and OCT. We, therefore, studied the longitudinal course of ERG and OCT in patients with MS, as well as the effect of disability changes and non-ON clinical relapses on these functional and structural measures. Methods MS patients ( n = 23) participating in an ongoing longitudinal observational study were invited to take part in a 3-year ophthalmological substudy. ERG and OCT were performed, and measures of MS-related disability and relapse history were obtained. Study visits were repeated annually. ERG peak times, rod b-wave amplitude, mixed rod/cone and cone b-/a-wave amplitude ratios, thickness of the peripapillary retinal nerve fibre layer, and volumes of the segmented retinal layers/complexes were analysed. Using generalised estimating equation models adjusted for age, ON, and MS treatment status, we assessed changes to ERG and OCT over the study duration, the effect of changes in disability and recent non-ON MS relapses on ERG and OCT, and the effect of selected OCT parameters on corresponding ERG parameters. Results At the group level, small fluctuations of several ERG peak times were recorded, with OCT values remaining stable. Increased disability between visits was associated with significant prolongation of mixed rod-cone ERG b-wave peak times. No evidence of associations between OCT and ERG parameters was observed. Conclusions Retinal bipolar cell function may be affected by changes in disability in patients with MS; however, recent non-ON MS clinical relapses appear not to affect ERG or OCT results. As ERG changes in MS patients over 3 years are likely to be small and of uncertain clinical relevance, longitudinal studies of retinal function in MS should be planned over an extended period.

Purpose of review Multiple sclerosis (MS) is a clinically heterogeneous disease, which complicates expectant management as well as treatment decisions. This review provides an overview of both well established and emerging predictors of disability worsening, including clinical factors, imaging factors, biomarkers and treatment strategies. Recent findings In addition to well known clinical predictors (age, male sex, clinical presentation, relapse behaviour), smoking, obesity, vascular and psychiatric comorbidities are associated with subsequent disability worsening in persons with MS. A number of imaging features are predictive of disability worsening and are present to varying degrees in relapsing and progressive forms of MS. These include brain volumes, spinal cord atrophy, lesion volumes and optical coherence tomography features. Cerebrospinal and more recently blood biomarkers including neurofilament light show promise as more easily attainable biomarkers of future disability accumulation. Importantly, recent observational studies suggest that initiation of early-intensive therapy, as opposed to escalation based on breakthrough disease, is associated with decreased accumulation of disability overall, although randomized controlled trials investigating this question are underway. Summary Understanding risk factors associated with disability progression can help to both counsel patients and enhance the clinician's availability to provide evidence-based treatment recommendations.

Background: REFINE was an exploratory, dose- and frequency-blinded, prospective, randomized, dose-ranging study in relapsing–remitting multiple sclerosis (RRMS) patients. Objective: To examine the efficacy, safety, and tolerability of natalizumab administered via various regimens in RRMS patients. Methods: Clinically stable RRMS patients previously treated with 300 mg natalizumab intravenously for ⩾12 months were randomized to one of six natalizumab regimens over 60 weeks: 300 mg administered intravenously or subcutaneously every 4 weeks (Q4W), 300 mg intravenously or subcutaneously every 12 weeks (Q12W), or 150 mg intravenously or subcutaneously Q12W. The primary endpoint was the mean cumulative number of combined unique active magnetic resonance imaging (MRI) lesions at week 60. Results: In total, 290 patients were enrolled. All Q12W dosing arms were associated with increased clinical and MRI disease activity and closed early; ⩾39.5% of patients in each Q12W arm met rescue criteria. In the 300 mg intravenous and subcutaneous Q4 W arms, the mean cumulative number of combined unique active MRI lesions was 0.23 and 0.02, respectively; annualized relapse rates were 0.07 and 0.08, respectively; and trough natalizumab serum levels and α4-integrin saturation were comparable. Conclusion: Natalizumab 300 mg subcutaneous Q4W was comparable to 300 mg intravenous Q4W dosing with respect to efficacy, pharmacokinetics/pharmacodynamics, and safety.

Introduction: Several recent studies indicate that deep gray matter or thalamic volume loss (VL) might be promising surrogate markers of disease activity in multiple sclerosis (MS) patients. To allow applying these markers to individual MS patients in clinical routine, age-dependent cut-offs distinguishing physiological from pathological VL and an estimation of the measurement error, which provides the confidence of the result, are to be defined. Methods: Longitudinal MRI scans of the following cohorts were analyzed in this study: 189 healthy controls (HC) (mean age 54 years, 22% female), 98 MS patients from Zurich university hospital (mean age 34 years, 62% female), 33 MS patients from Dresden university hospital (mean age 38 years, 60% female), and publicly available reliability data sets consisting of 162 short-term MRI scan-rescan pairs with scan intervals of days or few weeks. Percentage annualized whole brain volume loss (BVL), gray matter (GM) volume loss (GMVL), deep gray matter volume loss (deep GMVL), and thalamic volume loss (ThalaVL) were computed deploying the Jacobian integration (JI) method. BVL was additionally computed using Siena, an established method used in many Phase III drug trials. A linear mixed effect model was used to estimate the measurement error as the standard deviation (SD) of model residuals of all 162 scan-rescan pairs For estimation of age-dependent cut-offs, a quadratic regression function between age and the corresponding annualized VL values of the HC was computed. The 5th percentile was defined as the threshold for pathological VL per year since 95% of HC subjects exhibit a less pronounced VL for a given age. For the MS patients BVL, GMVL, deep GMVL, and ThalaVL were mutually compared and a paired t-test was used to test whether there are systematic differences in VL between these brain regions. Results: Siena and JI showed a high agreement for BVL measures, with a median absolute difference of 0.1% and a correlation coefficient of r = 0.78. Siena and GMVL showed a similar standard deviation (SD) of the scan-rescan error of 0.28% and 0.29%, respectively. For deep GMVL, ThalaVL the SD of the scan-rescan error was slightly higher (0.43% and 0.5%, respectively). Among the HC the thalamus showed the highest mean VL (-0.16%, -0.39%, and -0.59% at ages 35, 55, and 75, respectively). Corresponding cut-offs for a pathological VL/year were -0.68%, -0.91%, and -1.11%. The MS cohorts did not differ in BVL and GMVL. However, both MS cohorts showed a significantly (p = 0.05) stronger deep GMVL than BVL per year. Conclusion: It might be methodologically feasible to assess deep GMVL using JI in individual MS patients. However, age and the measurement error need to be taken into account. Furthermore, deep GMVL may be used as a complementary marker to BVL since MS patients exhibit a significantly stronger deep GMVL than BVL.

Background: Cognitive dysfunction, including slowed cognitive processing speed (CPS), is one of the most disabling symptoms of multiple sclerosis (MS). The Symbol Digit Modalities Test (SDMT) is a preferred measure of CPS for MS trials and routine screening. Based on encouraging SDMT results in the phase 3 SUNBEAM trial, these post hoc, exploratory analyses were conducted to further compare effects of the sphingosine 1-phosphate receptor modulator ozanimod versus intramuscular interferon ll-1a on CPS in participants with relapsing multiple sclerosis (RMS). Methods: In the phase 3, double-blind, double-dummy, SUNBEAM study, adults (aged 18-55 years) with RMS (N=1,346) were randomized to once-daily oral ozanimod 0.92 or 0.46 mg, or weekly intramuscular interferon ll-1a 30 & micro;g. The study continued until the last participant was treated for 12 months. CPS was measured as part of a secondary endpoint using the SDMT. Exploratory, post hoc analyses evaluated SDMT change and percentages of participants with clinically meaningful (>= 4-point) SDMT improvement or worsening at months 6 and 12, and relationship between SDMT and brain volume on magnetic resonance imaging. Results: Ozanimod improved SDMT scores compared with interferon ll-1a at months 6 and 12. At month 12, least squares mean difference in SDMT z-scores for ozanimod 0.92 mg versus interferon ll-1a was 0.102 (95% CI, 0.031-0.174, nominal p = 0.0051; standardized mean difference = 0.1376). A greater percentage of ozanimod 0.92 mg-treated participants had clinically meaningful improvements in SDMT scores versus interferon ll-1a at month 6 (30.0% versus 22.2%) and month 12 (35.6% versus 27.9%). Of those with SDMT improvement at month 6, 66.4% of those treated with ozanimod 0.92 mg and 55.9% of those treated with interferon ll-1a had sustained

The quantification of new or enlarged lesions from follow-up MRI scans is an important surrogate of clinical disease activity in patients with multiple sclerosis (MS). Not only is manual segmentation time consuming, but inter-rater variability is high. Currently, only a few fully automated methods are available. We address this gap in the field by employing a 3D convolutional neural network (CNN) with encoder-decoder architecture for fully automatic longitudinal lesion segmentation.Input data consist of two fluid attenuated inversion recovery (FLAIR) images (baseline and follow-up) per patient. Each image is entered into the encoder and the feature maps are concatenated and then fed into the decoder. The output is a 3D mask indicating new or enlarged lesions (compared to the baseline scan). The proposed method was trained on 1809 single point and 1444 longitudinal patient data sets and then validated on 185 independent longitudinal data sets from two different scanners. From the two validation data sets, manual segmentations were available from three experienced raters, respectively. The performance of the proposed method was compared to the open source Lesion Segmentation Toolbox (LST), which is a current state-of-art longitudinal lesion segmentation method.The mean lesion-wise inter-rater sensitivity was 62%, while the mean inter-rater number of false positive (FP) findings was 0.41 lesions per case. The two validated algorithms showed a mean sensitivity of 60% (CNN), 46% (LST) and a mean FP of 0.48 (CNN), 1.86 (LST) per case. Sensitivity and number of FP were not significantly different (p < 0.05) between the CNN and manual raters.New or enlarged lesions counted by the CNN algorithm appeared to be comparable with manual expert ratings. The proposed algorithm seems to outperform currently available approaches, particularly LST. The high inter-rater variability in case of manual segmentation indicates the complexity of identifying new or enlarged lesions. An automated CNN-based approach can quickly provide an independent and deterministic assessment of new or enlarged lesions from baseline to follow-up scans with acceptable reliability.