OBJECTIVETo assess functional changes in lymphocyte repertoire and subsequent clinical implications during delayed-release dimethyl fumarate (DMF) treatment in patients with multiple sclerosis.
...METHODSUsing peripheral blood from several clinical trials of DMF, immune cell subsets were quantified using flow cytometry. For some patients, lymphocyte counts were assessed after DMF discontinuation. Incidence of adverse events, including serious and opportunistic infections, was assessed.
RESULTSIn DMF-treated patients, absolute lymphocyte counts (ALCs) demonstrated a pattern of decline followed by stabilization, which also was reflected in the global reduction in numbers of circulating functional lymphocyte subsets. The relative frequencies of circulating memory T- and B-cell populations declined and naive cells increased. No increased incidence of serious infection or malignancy was observed for patients treated with DMF, even when stratified by ALC or T-cell subset frequencies. For patients who discontinued DMF due to lymphopenia, ALCs increased after DMF discontinuation; recovery time varied by ALC level at discontinuation. T-cell subsets closely correlated with ALCs in both longitudinal and cross-sectional analyses.
CONCLUSIONSDMF shifted the immunophenotype of circulating lymphocyte subsets. ALCs were closely correlated with CD4 and CD8 T-cell counts, indicating that lymphocyte subset monitoring is not required for safety vigilance. No increased risk of serious infection was observed in patients with low T-cell subset counts. Monitoring ALC remains the most effective way of identifying patients at risk of subsequently developing prolonged moderate to severe lymphopenia, a risk factor for progressive multifocal leukoencephalopathy in DMF-treated patients.
TRIAL REGISTRATION NUMBERSEUDRA CT 2015-001973-42, NCT00168701, NCT00420212, NCT00451451, and NCT00835770.
Introduction:
We report safety and efficacy in patients treated with dimethyl fumarate (DMF) for ~9 years in ENDORSE. Lymphocyte analysis data are also reported.
Methods:
Incidence of serious adverse ...events (SAEs), discontinuations due to adverse events (AEs), annualized relapse rate (ARR) and Expanded Disability Status Scale (EDSS) score were assessed. Patients were treated with DMF 240 mg twice daily (BID): placebo (PBO)/DMF (PBO for years 0–2 /DMF for years 3–9) or continuous (DMF/DMF) treatment; newly diagnosed patients were included. Annual magnetic resonance imaging (MRI) was evaluated in patients from the MRI cohort of DEFINE/CONFIRM. For the lymphocyte analysis, data from first DMF exposure were analyzed.
Results:
Of 2079 DEFINE/CONFIRM completers, 1736 enrolled and received ⩾1 dose of DMF. The MRI cohort included 530 patients. In the overall population, 527 (30%) patients experienced SAEs; most were fall and urinary tract infection. Over 9 years on DMF treatment, adjusted ARR remained low (⩽0.20). In patients treated with PBO in years 0–2, decreased ARR was apparent as early as year 3. Of DMF/DMF and PBO/DMF patients, 73% and 74%, respectively, had no 24-week confirmed disability progression. Most patients (~70%) had no new T1 or new/newly enlarging T2 lesions compared with previous MRI scans after 7 years treatment with DMF; the annual number of new T1 hypointense lesions and new/newly enlarging T2 hyperintense lesions were 0.6–0.8 and 0.9–2.0, respectively. Mean percentage brain volume change from ENDORSE baseline (6 years treatment in ENDORSE) was −1.32% (range −1.60% to −1.05%). Of the 2513 patients with lymphocyte assessments, 2470 had ⩾1 post-baseline measurement, 53 developed severe prolonged lymphopenia and were followed for up to 11 years; incidence of serious infection was not higher than in patients with absolute lymphocyte count (ALC) always ⩾ lower limit of normal (LLN). In patients with lymphopenia while on DMF and ALC < 0.91 × 109/L at discontinuation (n = 138), median time to ALC ⩾ LLN was 7 weeks post-discontinuation.
Conclusions:
Sustained safety and efficacy of DMF was observed in patients continuing on treatment for up to 11 years, supporting DMF as a long-term treatment option for patients with RRMS.
Trial registration:
ClinicalTrials.gov identifiers, NCT00835770 (ENDORSE); NCT00420212 (DEFINE); NCT00451451 (CONFIRM).
Brain volume loss measured from magnetic resonance imaging (MRI) is a marker of neurodegeneration and predictor of disability progression in MS, and is commonly used to assess drug efficacy at the ...group level in clinical trials. Whether measures of brain volume loss could be useful to help guide management of individual patients depends on the relative magnitude of the changes over a given interval to physiological and technical sources of variability.
To understand the relative contributions of neurodegeneration vs. physiological and technical sources of variability to measurements of brain volume loss in individuals.
Multiple T1-weighted 3D MPRAGE images were acquired from a healthy volunteer and MS patient over varying time intervals: 7 times on the first day (before breakfast at 7:30AM and then every 2 h for 12 h), each day for the next 6 working days, and 6 times over the remainder of the year, on 2 Siemens MRI scanners: 1.5T Sonata (S1) and 3.0T TIM Trio (S2). Scan-reposition-rescan data were acquired on S2 for daily, monthly and 1-year visits. Percent brain volume change (PBVC) was measured from baseline to each follow-up scan using FSL/SIENA. We estimated the effect of physiologic fluctuations on brain volume using linear regression of the PBVC values over hourly and daily intervals. The magnitude of the physiological effect was estimated by comparing the root-mean-square error (RMSE) of the regression of all the data points relative to the regression line, for the hourly scans vs the daily scans. Variance due to technical sources was assessed as the RMSE of the regression over time using the intracranial volume as a reference.
The RMSE of PBVC over 12 h, for both scanners combined, (“Hours”, 0.15%), was similar to the day-to-day variation over 1 week (“Days”, 0.14%), and both were smaller than the RMS error over the year (0.21%). All of these variations, however, were smaller than the scan-reposition-rescan RMSE (0.32%). The variability of PBVC for the individual scanners followed the same trend. The standard error of the mean (SEM) for PBVC was 0.26 for S1, and 0.22 for S2. From these values, we computed the minimum detectable change (MDC) to be 0.7% on S1 and 0.6% on S2. The location of the brain along the z-axis of the magnet inversely correlated with brain volume change for hourly and daily brain volume fluctuations (p < 0.01).
Consistent diurnal brain volume fluctuations attributable to physiological shifts were not detectable in this small study. Technical sources of variation dominate measured changes in brain volume in individuals until the volume loss exceeds around 0.6–0.7%. Reliable interpretation of measured brain volume changes as pathological (greater than normal aging) in individuals over 1 year requires changes in excess of about 1.1% (depending on the scanner). Reliable brain atrophy detection in an individual may be feasible if the rate of brain volume loss is large, or if the measurement interval is sufficiently long.
•Consistent diurnal brain volume fluctuations attributable to physiological shifts were not detectable in this study.•Technical sources of variation dominate measured changes in brain volume until the volume loss exceeds around 0.6 – 0.7%.•Previous work suggests a true annual brain volume loss of <0.4% is within the normal range.•Thus, a measured brain volume loss of 1.1% or more is needed to confirm pathological brain atrophy.•Brain atrophy in an individual may be detectable if sufficiently large, due to rapid atrophy or long measurement interval.
Summary Background Multiple sclerosis (MS) diagnostic criteria incorporate MRI features that can be used to predict later diagnosis of MS in adults with acute CNS demyelination. To identify MRI ...predictors of a subsequent MS diagnosis in a paediatric population, we created a standardised scoring method and applied it to MRI scans from a national prospective incidence cohort of children with CNS demyelination. Methods Clinical and MRI examinations were done at the onset of acute CNS demyelination and every 3 months in the first year after that, and at the time of a second demyelinating attack. MS was diagnosed on the basis of clinical or MRI evidence of relapsing disease. Baseline MRI scans were assessed for the presence of 14 binary response parameters. Parameters were assessed with a multiple tetrachoric correlation matrix. Univariate analyses and multivariable Cox proportional hazards models were used to identify predictors of MS. Findings Between Sept 1, 2004, and June 30, 2010, 332 children and adolescents were assessed for eligibility. 1139 scans were available from 284 eligible participants who had been followed up for 3·9 (SD 1·7) years. 57 (20%) were diagnosed with MS after a median of 188 (IQR 144–337) days. Seven of 14 binary response parameters were retained. The presence of either one or more T1-weighted hypointense lesions (hazard ratio 20·6, 95% CI 5·46–78·0) or one or more periventricular lesions (3·34, 1·27–8·83) was associated with an increased likelihood of MS diagnosis (sensitivity 84%, specificity 93%, positive predictive value 76%, negative predictive value 96%). Risk for MS diagnosis was highest when both parameters were present (34·27, 16·69–70·38). Although the presence of contrast enhancement, cerebral white matter, intracallosal, and brainstem lesions was associated with MS in the univariate analyses, these parameters were not retained in the multivariable models. Interpretation Specific MRI parameters can be used to predict diagnosis of MS in children with incident demyelination of the CNS. The ability to promptly identify children with MS will enhance timely access to care and will be important for future clinical trials in paediatric MS. Funding Canadian Multiple Sclerosis Scientific Research Foundation.
In recent years, the use of magnetic resonance imaging (MRI) for the diagnostic work-up of multiple sclerosis (MS) has evolved considerably. The 2017 McDonald criteria show high sensitivity and ...accuracy in predicting a second clinical attack in patients with a typical clinically isolated syndrome and allow an earlier diagnosis of MS. They have been validated, are evidence-based, simplify the clinical use of MRI criteria and improve MS patients’ management. However, to limit the risk of misdiagnosis, they should be applied by expert clinicians only after the careful exclusion of alternative diagnoses. Recently, new MRI markers have been proposed to improve diagnostic specificity for MS and reduce the risk of misdiagnosis. The central vein sign and chronic active lesions (i.e., paramagnetic rim lesions) may increase the specificity of MS diagnostic criteria, but further effort is necessary to validate and standardize their assessment before implementing them in the clinical setting. The feasibility of subpial demyelination assessment and the clinical relevance of leptomeningeal enhancement evaluation in the diagnostic work-up of MS appear more limited. Artificial intelligence tools may capture MRI attributes that are beyond the human perception, and, in the future, artificial intelligence may complement human assessment to further ameliorate the diagnostic work-up and patients’ classification. However, guidelines that ensure reliability, interpretability, and validity of findings obtained from artificial intelligence approaches are still needed to implement them in the clinical scenario. This review provides a summary of the most recent updates regarding the application of MRI for the diagnosis of MS.
Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach ...where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.
The Canadian Multiple Sclerosis Working Group (CMSWG) developed practical recommendations in 2004 to assist clinicians in optimizing the use of disease-modifying therapies (DMT) in patients with ...relapsing multiple sclerosis. The CMSWG convened to review how disease activity is assessed, propose a more current approach for assessing suboptimal response, and to suggest a scheme for switching or escalating treatment. Practical criteria for relapses, Expanded Disability Status Scale (EDSS) progression and MRI were developed to classify the clinical level of concern as Low, Medium and High. The group concluded that a change in treatment may be considered in any RRMS patient if there is a high level of concern in any one domain (relapses, progression or MRI), a medium level of concern in any two domains, or a low level of concern in all three domains. These recommendations for assessing treatment response should assist clinicians in making more rational choices in their management of relapsing MS patients.
Objective:
The diagnosis of multiple sclerosis (MS) rests on confirmation of central nervous system inflammatory disease that is disseminated in space and time, as evidenced clinically or by magnetic ...resonance imaging (MRI). The 2010 McDonald criteria simplified MRI requirements, and newly proposed that the criteria are also suitable for the diagnosis of pediatric MS.
Methods:
In a national prospective incident cohort study of children with acute demyelination observed for a minimum of 24 months, baseline and serial clinical and MRI examinations were used to retrospectively evaluate the 2010 and 2005 McDonald criteria using clinically relapsing disease as the gold standard.
Results:
Of 212 eligible participants, 34 experienced 2 or more clinical attacks, 58 met the 2010 criteria, and 42 met 2005 McDonald criteria. The 2010 criteria demonstrated high sensitivity (100%), specificity (86%), positive predictive value (76%), and negative predictive value (100%) for children older than 11 years with non–acute disseminated encephalomyelitis (ADEM) presentations, as did the 2005 McDonald criteria. In younger children with a non‐ADEM presentation, PPV of the 2010 criteria was only 55%. None of the 50 children with ADEM met clinical criteria for MS, but 10 met 2010 and 4 met 2005 criteria.
Interpretation:
Both 2005 and 2010 McDonald criteria identify children with relapsing–remitting MS, although caution is suggested when applying these criteria in younger children. The 2010 McDonald criteria are simple and enable an early diagnosis of MS, but are not suited for application in the context of ADEM‐like presentations. ANN NEUROL 2012;72:211–223.
Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology presents several challenges to common models. These ...challenges impede the integration of deep learning models into real clinical workflows, where the customary process of cascading deterministic outputs from a sequence of image-based inference steps (e.g. registration, segmentation) generally leads to an accumulation of errors that impacts the accuracy of downstream inference tasks. In this paper, we propose that by embedding uncertainty estimates across cascaded inference tasks, performance on the downstream inference tasks should be improved. We demonstrate the effectiveness of the proposed approach in three different clinical contexts: (i) We demonstrate that by propagating T2 weighted lesion segmentation results and their associated uncertainties, subsequent T2 lesion detection performance is improved when evaluated on a proprietary large-scale, multi-site, clinical trial dataset acquired from patients with Multiple Sclerosis. (ii) We show an improvement in brain tumour segmentation performance when the uncertainty map associated with a synthesised missing MR volume is provided as an additional input to a follow-up brain tumour segmentation network, when evaluated on the publicly available BraTS-2018 dataset. (iii) We show that by propagating uncertainties from a voxel-level hippocampus segmentation task, the subsequent regression of the Alzheimer's disease clinical score is improved.
Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive ...worsening using data-driven methods.
We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures.
We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001).
GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials.
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