Relating relapse and T2 lesion changes to disability progression in multiple sclerosis: a systematic literature review and regression analysis

Current knowledge on multiple sclerosis pathophysiology, disability progression assessment and treatment options, and the role of autologous hematopoietic stem cell transplantation

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that affects nearly 2.8 million people each year. MS distinguishes three main types: relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). RRMS is the most common type, with the majority of patients eventually progressing to SPMS, in which neurological development is constant, whereas PPMS is characterized by a progressive course from disease onset. New or additional insights into the role of effector and regulatory cells of the immune and CNS systems, Epstein-Barr virus (EBV) infection, and the microbiome in the pathophysiology of MS have emerged, which may lead to the development of more targeted therapies that can halt or reverse neurodegeneration. Depending on the type and severity of the disease, various disease-modifying therapies (DMTs) are currently used for RRMS/SPMS and PPMS. As a last resort, and especially in highly active RRMS that does not respond to DMTs, autologous hematopoietic stem cell transplantation (AHSCT) is performed and has shown good results in reducing neuroinflammation. Nevertheless, the question of its potential role in preventing disability progression remains open. The aim of this review is to provide a comprehensive update on MS pathophysiology, assessment of MS disability progression and current treatments, and to examine the potential role of AHSCT in preventing disability progression.

Disease progression in the first 5 years of treatment in multiple sclerosis: Predictive value of early brain and lesion volume changes

BACKGROUND

Whether the degree of inflammation (and its resolution) and neurodegeneration after treatment initiation predicts disease progression in multiple sclerosis (MS) remains unclear.

OBJECTIVES

To assess the predictive value of magnetic resonance imaging (MRI)-derived brain and lesion volume (LV) changes in years 1 and 2 of treatment for disease progression.

METHODS

Patients receiving early interferon beta-1a treatment in REFLEX/REFLEXION (N = 262) were included. Predictive regression models included new/enlarging LV (positive activity), disappearing/shrinking LV (negative activity), and global/central atrophy during years 1 and 2.

RESULTS

Faster global atrophy and/or pseudo-atrophy and positive lesion activity in years 1 and 2 related to an increased probability and faster conversion to clinically definite multiple sclerosis (CDMS). Negative lesion activity in year 1 and slower central atrophy in year 2 were predictive of confirmed disability progression (9-Hole Peg Test). Positive lesion activity in year 2 was predictive of faster global atrophy, while positive lesion activity in years 1 and 2 was predictive of faster central atrophy.

CONCLUSIONS

A higher degree of global atrophy and/or pseudo-atrophy in year 1 was predictive of CDMS. Positive lesion activity in any year was related to CDMS and neurodegeneration. Disability was related to negative lesion activity in year 1 and slower central atrophy in year 2.

Assessment of T2 lesion-based disease activity volume outcomes in predicting disease progression in multiple sclerosis over 10 years

BACKGROUND

New/enlarging T2 lesion count and T2-lesion volume (LV) are used as conventional secondary endpoints in clinical trials of patients with multiple sclerosis (PwMS). However, those outcomes may have several limitations, such as inability to account for heterogeneity of lesion formation/enlargement frequency and their dynamic volumetric behavior. Measurement of volume rather than count of new/enlarging lesions may be more representative outcome of dynamic changes over time.

OBJECTIVES

To investigate whether new/enlarging T2-LV is more predictive of confirmed disability progression (CDP), compared to total T2-LV or new/enlarging T2 lesion count over long-term follow-up.

METHODS

We studied 176 early relapsing-remitting PwMS who were followed with annual MRI examinations over 10 years. T2-LV, new/enlarging T2-LV, and new/enlarging lesion count were determined. Cumulative count/volumes were obtained. 10-year CDP was confirmed after 48-weeks. ANCOVA analysis detected MRI outcome differences in stable (n = 76) and CDP (n = 100) groups at different time points, after correction for multiple comparisons.

RESULTS

PwMS with CDP had greater cumulative new/enlarging T2-LV at 4 years (p = 0.049), and enlarging T2-LV at 4- (p = 0.039) and 6-year follow-up (p = 0.032), compared to stable patients. PwMS with CDP did not differ from stable ones in new/enlarging T2 lesion count or total T2-LV at any of the study timepoints. PwMS with Expanded Disability Status Scale change >2.0 had significantly greater enlarging T2 lesion count (p = 0.01) and enlarging T2-LV (p = 0.038) over the 10-year follow-up.

CONCLUSION

Enlargement of T2 lesions is more strongly associated with long-term disability progression compared to other conventional T2 lesion-based outcomes.

Developing a clinical-environmental-genotypic prognostic index for relapsing-onset multiple sclerosis and clinically isolated syndrome

Our inability to reliably predict disease outcomes in multiple sclerosis remains an issue for clinicians and clinical trialists. This study aims to create, from available clinical, genetic and environmental factors; a clinical–environmental–genotypic prognostic index to predict the probability of new relapses and disability worsening. The analyses cohort included prospectively assessed multiple sclerosis cases (N = 253) with 2858 repeated observations measured over 10 years. N = 219 had been diagnosed as relapsing-onset, while N = 34 remained as clinically isolated syndrome by the 10th-year review. Genotype data were available for 199 genetic variants associated with multiple sclerosis risk. Penalized Cox regression models were used to select potential genetic variants and predict risk for relapses and/or worsening of disability. Multivariable Cox regression models with backward elimination were then used to construct clinical–environmental, genetic and clinical–environmental–genotypic prognostic index, respectively. Robust time-course predictions were obtained by Landmarking. To validate our models, Weibull calibration models were used, and the Chi-square statistics, Harrell’s C-index and pseudo-R² were used to compare models. The predictive performance at diagnosis was evaluated using the Kullback–Leibler and Brier (dynamic) prediction error (reduction) curves. The combined index (clinical–environmental–genotypic) predicted a quadratic time-dynamic disease course in terms of worsening (HR = 2.74, CI: 2.00–3.76; pseudo-R²=0.64; C-index = 0.76), relapses (HR = 2.16, CI: 1.74–2.68; pseudo-R² = 0.91; C-index = 0.85), or both (HR = 3.32, CI: 1.88–5.86; pseudo-R² = 0.72; C-index = 0.77). The Kullback–Leibler and Brier curves suggested that for short-term prognosis (≤5 years from diagnosis), the clinical–environmental components of disease were more relevant, whereas the genetic components reduced the prediction errors only in the long-term (≥5 years from diagnosis). The combined components performed slightly better than the individual ones, although their prognostic sensitivities were largely modulated by the clinical–environmental components. We have created a clinical–environmental–genotypic prognostic index using relevant clinical, environmental, and genetic predictors, and obtained robust dynamic predictions for the probability of developing new relapses and worsening of symptoms in multiple sclerosis. Our prognostic index provides reliable information that is relevant for long-term prognostication and may be used as a selection criterion and risk stratification tool for clinical trials. Further work to investigate component interactions is required and to validate the index in independent data sets.

Assessment of automatic decision-support systems for detecting active T2 lesions in multiple sclerosis patients

BACKGROUND

Active (new/enlarging) T2 lesion counts are routinely used in the clinical management of multiple sclerosis. Thus, automated tools able to accurately identify active T2 lesions would be of high interest to neuroradiologists for assisting in their clinical activity.

OBJECTIVE

To compare the accuracy in detecting active T2 lesions and of radiologically active patients based on different visual and automated methods.

METHODS

One hundred multiple sclerosis patients underwent two magnetic resonance imaging examinations within 12 months. Four approaches were assessed for detecting active T2 lesions: (1) conventional neuroradiological reports; (2) prospective visual analyses performed by an expert; (3) automated unsupervised tool; and (4) supervised convolutional neural network. As a gold standard, a reference outcome was created by the consensus of two observers.

RESULTS

The automated methods detected a higher number of active T2 lesions, and a higher number of active patients, but a higher number of false-positive active patients than visual methods. The convolutional neural network model was more sensitive in detecting active T2 lesions and active patients than the other automated method.

CONCLUSION

Automated convolutional neural network models show potential as an aid to neuroradiological assessment in clinical practice, although visual supervision of the outcomes is still required.

Confirmed disability progression provides limited predictive information regarding future disease progression in multiple sclerosis

Background

Although confirmed disability progression (CDP) is a common outcome in multiple sclerosis (MS) clinical trials, its predictive value for long-term outcomes is uncertain.

Objective

To investigate whether CDP at month 24 predicts subsequent disability accumulation in MS.

Methods

The Comprehensive Longitudinal Investigation of Multiple Sclerosis at Brigham and Women's Hospital includes participants with relapsing-remitting MS or clinically isolated syndrome with Expanded Disability Status Scale (EDSS) scores ≤5 (N = 1214). CDP was assessed as a predictor of time to EDSS score 6 (EDSS 6) and to secondary progressive MS (SPMS) using a Cox proportional hazards model; adjusted models included additional clinical/participant characteristics. Models were compared using Akaike's An Information Criterion.

Results

CDP was directionally associated with faster time to EDSS 6 in univariate analysis (HR = 1.61 [95% CI: 0.83, 3.13]). After adjusting for month 24 EDSS, CDP was directionally associated with slower time to EDSS 6 (adjusted HR = 0.65 [0.32, 1.28]). Models including CDP had worse fit statistics than those using EDSS scores without CDP. When models included clinical and magnetic resonance imaging measures, T2 lesion volume improved fit statistics. Results were similar for time to SPMS.

Conclusions

CDP was less predictive of time to subsequent events than other MS clinical features.

Predicting disability worsening in relapsing and progressive multiple sclerosis

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.

Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks

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A fully automated segmentation of new or enlarged multiple sclerosis (MS) lesions.

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3D convolutional neural network (CNN) with U-net-like encoder-decoder architecture.

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Simultaneous processing of baseline and follow-up scan of the same patient.

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Trained on 3253 patient data from over 103 different MR scanners.

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Fast (<1min), robust algorithm with segmentation results in inter-rater variability.

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.

Effect of Nonmyeloablative Hematopoietic Stem Cell Transplantation vs Continued Disease-Modifying Therapy on Disease Progression in Patients With Relapsing-Remitting Multiple Sclerosis: A Randomized Clinical Trial

IMPORTANCE

Hematopoietic stem cell transplantation (HSCT) represents a potentially useful approach to slow or prevent progressive disability in relapsing-remitting multiple sclerosis (MS).

OBJECTIVE

To compare the effect of nonmyeloablative HSCT vs disease-modifying therapy (DMT) on disease progression.

DESIGN, SETTING, AND PARTICIPANTS

Between September 20, 2005, and July 7, 2016, a total of 110 patients with relapsing-remitting MS, at least 2 relapses while receiving DMT in the prior year, and an Expanded Disability Status Scale (EDSS; score range, 0-10 [10 = worst neurologic disability]) score of 2.0 to 6.0 were randomized at 4 US, European, and South American centers. Final follow-up occurred in January 2018 and database lock in February 2018.

INTERVENTIONS

Patients were randomized to receive HSCT along with cyclophosphamide (200 mg/kg) and antithymocyte globulin (6 mg/kg) (n = 55) or DMT of higher efficacy or a different class than DMT taken during the previous year (n = 55).

MAIN OUTCOMES AND MEASURES

The primary end point was disease progression, defined as an EDSS score increase after at least 1 year of 1.0 point or more (minimal clinically important difference, 0.5) on 2 evaluations 6 months apart, with differences in time to progression estimated as hazard ratios.

RESULTS

Among 110 randomized patients (73 [66%] women; mean age, 36 [SD, 8.6] years), 103 remained in the trial, with 98 evaluated at 1 year and 23 evaluated yearly for 5 years (median follow-up, 2 years; mean, 2.8 years). Disease progression occurred in 3 patients in the HSCT group and 34 patients in the DMT group. Median time to progression could not be calculated in the HSCT group because of too few events; it was 24 months (interquartile range, 18-48 months) in the DMT group (hazard ratio, 0.07; 95% CI, 0.02-0.24; P < .001). During the first year, mean EDSS scores decreased (improved) from 3.38 to 2.36 in the HSCT group and increased (worsened) from 3.31 to 3.98 in the DMT group (between-group mean difference, -1.7; 95% CI, -2.03 to -1.29; P < .001). There were no deaths and no patients who received HSCT developed nonhematopoietic grade 4 toxicities (such as myocardial infarction, sepsis, or other disabling or potential life-threatening events).

CONCLUSIONS AND RELEVANCE

In this preliminary study of patients with relapsing-remitting MS, nonmyeloablative HSCT, compared with DMT, resulted in prolonged time to disease progression. Further research is needed to replicate these findings and to assess long-term outcomes and safety.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00273364.

Estimation of annual probabilities of changing disability levels in Australians with relapsing-remitting multiple sclerosis

BACKGROUND

Transition probabilities are the engine within many health economics decision models. However, the probabilities of progression of disability due to multiple sclerosis (MS) have not previously been estimated in Australia.

OBJECTIVES

To estimate annual probabilities of changing disability levels in Australians with relapsing-remitting MS (RRMS).

METHODS

Combining data from Ausimmune/Ausimmune Longitudinal (2003-2011) and Tasmanian MS Longitudinal (2002-2005) studies (n = 330), annual transition probabilities were obtained between no/mild (Expanded Disability Status Scale (EDSS) levels 0-3.5), moderate (EDSS 4-6.0) and severe (EDSS 6.5-9.5) disability.

RESULTS

From no/mild disability, 6.4% (95% confidence interval (CI): 4.7-8.4) and 0.1% (0.0-0.2) progressed to moderate and severe disability annually, respectively. From moderate disability, 6.9% (1.0-11.4) improved (to no/mild state) and 2.6% (1.1-4.5) worsened. From severe disability, 0.0% improved to moderate and no/mild disability. Male sex, age at onset, longer disease duration, not using immunotherapies greater than 3 months and a history of relapse were related to higher probabilities of worsening.

CONCLUSION

We have estimated probabilities of changing disability levels in Australians with RRMS. Probabilities differed between various subgroups, but due to small sample sizes, results should be interpreted with caution. Our findings will be helpful in predicting long-term disease outcomes and in health economic evaluations of MS.

A double-blind, placebo-controlled, single ascending-dose study of remyelinating antibody rHIgM22 in people with multiple sclerosis

Objective

The objective of this paper is to assess, in individuals with clinically stable multiple sclerosis (MS), the safety, tolerability, pharmacokinetics (PK) and exploratory pharmacodynamics of the monoclonal recombinant human antibody IgM22 (rHIgM22).

Methods

Seventy-two adults with stable MS were enrolled in a double-blind, randomized, placebo-controlled, single ascending-dose, Phase 1 trial examining rHIgM22 from 0.025 to 2.0 mg/kg. Assessments included MRI, MR spectroscopy, plasma PK, and changes in clinical status, laboratory values and adverse events for three months. The final cohort had additional clinical, ophthalmologic, CSF collection and exploratory biomarker evaluations. Participants were monitored for six months.

Results

rHIgM22 was well tolerated with no clinically significant safety signals. Noncompartmental PK modeling demonstrated linear dose-proportionality both of C_max and AUC_0–Last. The steady-state apparent volume of distribution of approximately 58 ml/kg suggested primarily vascular compartmentalization. CSF:plasma rHIgM22 concentration increased from 0.003% on Day 2 for both 1.0 and 2.0 mg/kg to 0.056% and 0.586% for 1.0 and 2.0 mg/kg, respectively, on Day 29. No statistically significant treatment-related changes were observed in exploratory pharmacodynamic outcome measures included for the 21 participants of the extension cohort.

Conclusions

Single doses of rHIgM22 were well tolerated and exhibited linear PK, and antibody was detected in the CSF.

Is impaired cerebral vasoreactivity an early marker of cognitive decline in multiple sclerosis patients?

OBJECTIVE

The link between cerebral vasoreactivity and cognitive status in multiple sclerosis remains unclear. The aim of the present study was to investigate a potential decrease of cerebral vasoreactivity in multiple sclerosis patients and correlate it with cognitive status.

METHODS

Thirty-three patients with multiple sclerosis (nine progressive and 24 remitting forms, median age: 39 years, 12 males) and 22 controls underwent MRI with a hypercapnic challenge to assess cerebral vasoreactivity and a neuropsychological assessment. Cerebral vasoreactivity, measured as the cerebral blood flow percent increase normalised by end-tidal carbon dioxide variation, was assessed globally and by regions of interest using the blood oxygen level-dependent technique. Non-parametric statistics tests were used to assess differences between groups, and associations were estimated using linear models.

RESULTS

Cerebral vasoreactivity was lower in patients with cognitive impairment than in cognitively normal patients (p=0.004) and was associated with education level in patients (R² = 0.35; p = 0.047). There was no decrease in cerebral vasoreactivity between patients and controls.

CONCLUSIONS

Cognitive impairment in multiple sclerosis may be mediated through decreased cerebral vasoreactivity. Cerebral vasoreactivity could therefore be considered as a marker of cognitive decline in multiple sclerosis.

KEY POINTS

• Cerebral vasoreactivity does not differ between multiple sclerosis patients and controls. • Cerebral vasoreactivity measure is linked to cognitive impairment in multiple sclerosis. • Cerebral vasoreactivity is linked to level of education in multiple sclerosis.

MRI FLAIR lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation?

Introduction

Magnetic resonance imaging (MRI) has become key in the diagnosis and disease monitoring of patients with multiple sclerosis (MS). Both, T2 lesion load and Gadolinium (Gd) enhancing T1 lesions represent important endpoints in MS clinical trials by serving as a surrogate of clinical disease activity. T2- and fluid-attenuated inversion recovery (FLAIR) lesion quantification - largely due to methodological constraints – is still being performed manually or in a semi-automated fashion, although strong efforts have been made to allow automated quantitative lesion segmentation. In 2012, Schmidt and co-workers published an algorithm to be applied on FLAIR sequences. The aim of this study was to apply the Schmidt algorithm on an independent data set and compare automated segmentation to inter-rater variability of three independent, experienced raters.

Methods

MRI data of 50 patients with RRMS were randomly selected from a larger pool of MS patients attending the MS Clinic at the Brain and Mind Centre, University of Sydney, Australia. MRIs were acquired on a 3.0T GE scanner (Discovery MR750, GE Medical Systems, Milwaukee, WI) using an 8 channel head coil. We determined T2-lesion load (total lesion volume and total lesion number) using three versions of an automated segmentation algorithm (Lesion growth algorithm (LGA) based on SPM8 or SPM12 and lesion prediction algorithm (LPA) based on SPM12) as first described by Schmidt et al. (2012). Additionally, manual segmentation was performed by three independent raters. We calculated inter-rater correlation coefficients (ICC) and dice coefficients (DC) for all possible pairwise comparisons.

Results

We found a strong correlation between manual and automated lesion segmentation based on LGA SPM8, regarding lesion volume (ICC = 0.958 and DC = 0.60) that was not statistically different from the inter-rater correlation (ICC = 0.97 and DC = 0.66). Correlation between the two other algorithms (LGA SPM12 and LPA SPM12) and manual raters was weaker but still adequate (ICC = 0.927 and DC = 0.53 for LGA SPM12 and ICC = 0.949 and DC = 0.57 for LPA SPM12). Variability of both manual and automated segmentation was significantly higher regarding lesion numbers.

Conclusion

Automated lesion volume quantification can be applied reliably on FLAIR data sets using the SPM based algorithm of Schmidt et al. and shows good agreement with manual segmentation.

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Fully automated and manual MS lesion segmentation on FLAIR images were compared.

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Automated FLAIR lesion volume segmentation holds up with manual annotation.

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When using DC and ICC, SPM8 based algorithm performed better than recent updates.

Brain health: time matters in multiple sclerosis

INTRODUCTION

We present international consensus recommendations for improving diagnosis, management and treatment access in multiple sclerosis (MS). Our vision is that these will be used widely among those committed to creating a better future for people with MS and their families.

METHODS

Structured discussions and literature searches conducted in 2015 examined the personal and economic impact of MS, current practice in diagnosis, treatment and management, definitions of disease activity and barriers to accessing disease-modifying therapies (DMTs).

RESULTS

Delays often occur before a person with symptoms suggestive of MS sees a neurologist. Campaigns to raise awareness of MS are needed, as are initiatives to improve access to MS healthcare professionals and services. We recommend a clear treatment goal: to maximize neurological reserve, cognitive function and physical function by reducing disease activity. Treatment should start early, with DMT and lifestyle measures. All parameters that predict relapses and disability progression should be included in the definition of disease activity and monitored regularly when practical. On suboptimal control of disease activity, switching to a DMT with a different mechanism of action should be considered. A shared decision-making process that embodies dialogue and considers all appropriate DMTs should be implemented. Monitoring data should be recorded formally in registries to generate real-world evidence. In many jurisdictions, access to DMTs is limited. To improve treatment access the relevant bodies should consider all costs to all parties when conducting economic evaluations and encourage the continuing investigation, development and use of cost-effective therapeutic strategies and alternative financing models.

CONCLUSIONS

The consensus findings of an international author group recommend a therapeutic strategy based on proactive monitoring and shared decision-making in MS. Early diagnosis and improved treatment access are also key components.

Balancing Early Aggression Against Risk of Progression in Multiple Sclerosis

Multiple sclerosis is a chronic demyelinating disease characterized by focal and diffuse inflammation of the central nervous system resulting in significant physical and cognitive disabilities. Disease-modifying therapies targeting the dysfunctional immune response are most effective in the first few years after disease onset, indicating that there is a limited time window for therapy to influence the disease course. No evidence of disease activity is emerging as a new standard for treatment response and may be associated with improved long-term disability outcomes. An aggressive management strategy, including earlier use of more potent immunomodulatory agents and close monitoring of the clinical and radiologic response to treatment, is recommended to minimize early brain volume loss and slow the progression of physical and cognitive impairments in patients with relapsing-remitting multiple sclerosis.

[Criteria of ineffectiveness of treatment with first-line therapy: how to use MRI results in decision making?]

The importance of MRI stuides in the control over treatment efficacy in multiple sclerosis and appropriate recommendations on drug substitution during treatment are discussed. We suggest low, middle or high risk in respect to the efficacy of current treatment. Accordingly, drug substitution can be related with the low level of fears for all three criteria or the moderate level for any two criteria or the high level for any one criterion. Since MRI criteria are important, this model appears to be the most rational because the physician can make a decision about treatment escalation if the patient has ≥3 new T2-lesions or ≥3 contrast-enhanced T1-lesions.

Evolving role of MRI in optimizing the treatment of multiple sclerosis: Canadian Consensus recommendations

Background

Magnetic resonance imaging (MRI) is increasingly important for the early detection of suboptimal responders to disease-modifying therapy for relapsing–remitting multiple sclerosis. Treatment response criteria are becoming more stringent with the use of composite measures, such as no evidence of disease activity (NEDA), which combines clinical and radiological measures, and NEDA-4, which includes the evaluation of brain atrophy.

Methods

The Canadian MRI Working Group of neurologists and radiologists convened to discuss the use of brain and spinal cord imaging in the assessment of relapsing–remitting multiple sclerosis patients during the treatment course.

Results

Nine key recommendations were developed based on published sources and expert opinion. Recommendations addressed image acquisition, use of gadolinium, MRI requisitioning by clinicians, and reporting of lesions and brain atrophy by radiologists. Routine MRI follow-ups are recommended beginning at three to six months after treatment initiation, at six to 12 months after the reference scan, and annually thereafter. The interval between scans may be altered according to clinical circumstances.

Conclusions

The Canadian recommendations update the 2006 Consortium of MS Centers Consensus revised guidelines to assist physicians in their management of MS patients and to aid in treatment decision making.