von Gumberz J, Mahmoudi M, Young K, Schippling S, Martin R, Heesen C, Siemonsen S, Stellmann JP.BACKGROUND: Magnetic resonance imaging (MRI) is the best biomarker of inflammatory disease activity in relapsing remitting Multiple Sclerosis (RRMS) so far but the association with disability is weak. Appearance of new MRI-lesions is used to evaluate response to immunotherapies in individual patients as well as being the most common primary outcome in phase-2 trials. Measurements of brain atrophy show promising outcomes in natural cohort studies and some phase-2 trials. From a theoretical perspective they might represent irreversible neurodegeneration and be more closely associated with disability. However, these atrophy measurements are not yet established as prognostic factors in real-life clinical routine. High field MRI has improved image quality and resolution and new methods to measure atrophy dynamics have become available. OBJECTIVE: To investigate the predictive value of MRI classification criteria in to high/low atrophy and inflammation groups, and to explore predictive capacity of two consecutive routine MRI scans for disability progression in RRMS in a real-life prospective cohort. METHODS: 82 RRMS-patients (40 untreated, 42 treated with immunotherapies, mean age 40 years, median Expanded Disability Status Scale (EDSS) of 2, underwent two clinically indicated MRI scans (3 Tesla) within 5-14 months, and EDSS assessment after a mean of 3.0 (1.5-4.2) years. We investigated the predictive value of predefined classifications in low/high inflammatory and atrophy groups for EDSS progression (≥1.5 if baseline EDSS = 0, ≥1.0 if baseline EDSS <5, ≥0.5 for other) by chi-square tests and by analysis of variance (ANOVA). The classifications were based on current scientific or clinical recommendation (e.g., treatment response criteria). Brain atrophy was assessed with three different methods (SIENA, SIENAX, and FreeSurfer). Post-hoc analyses aimed to explore clinical data and dynamics of MRI outcomes as predictors in multivariate linear and logit models. RESULTS: Progression was observed in 24% of patients and was independent from treatment status. None of the predefined classifications were predictive for progression. Explorative post-hoc analyses found lower baseline EDSS and higher grey matter atrophy (FreeSurfer) as best predictors (R (2) = 0.29) for EDSS progression and the accuracy was overall good (Area under the curve = 0.81). CONCLUSION: Beside EDSS at baseline, short-term grey matter atrophy is predictive for EDSS progression in treated and untreated RRMS. The development of atrophy measurements for individual risk counselling and evaluation of treatment response seems possible, but needs further validation in larger cohorts. MRI-atrophy estimates from the FreeSurfer toolbox seem to be more reliable than older methods.
PeerJ. 2016 Sep 20;4:e2442
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