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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Sowa, Piotr
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2022Clinical and MRI measures to identify non-acute MOG-antibody disease in adultscitations
- 2020Self-diffusion in a triple-defect A-B binary system : Monte Carlo simulationcitations
- 2017Atomistic origin of the thermodynamic activation energy for self-diffusion and order-order relaxation in intermetallic compounds II : Monte Carlo simulation of B2-ordering binariescitations
- 2017Atomistic origin of the thermodynamic activation energy for self-diffusion and order-order relaxation in intermetallic compounds I : analytical approachcitations
- 2014SiC (0001) and (000$bar{1}$) surfaces diffusion parameters estimated by means of atomistic Kinetic Monte Carlo simulationscitations
- 2013Self-diffusion and ‘order-order’ kinetics in B2-ordering AB binary systems with a tendency for triple-defect formation : Monte Carlo simulationcitations
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article
Clinical and MRI measures to identify non-acute MOG-antibody disease in adults
Abstract
<jats:title>Abstract</jats:title><jats:p>MRI and clinical features of myelin oligodendrocyte glycoprotein (MOG)-antibody disease may overlap with those of other inflammatory demyelinating conditions posing diagnostic challenges, especially in non-acute phases and when serologic testing for MOG-antibodies is unavailable or shows uncertain results.</jats:p><jats:p>We aimed to identify MRI and clinical markers that differentiate non-acute MOG-antibody disease from aquaporin4 (AQP4)-antibody neuromyelitis optica spectrum disorder and relapsing remitting multiple sclerosis, guiding in the identification of MOG-antibody disease patients in clinical practice.</jats:p><jats:p>In this cross-sectional retrospective study, data from 16 MAGNIMS centres were included. Data collection and analyses were conducted from 2019 to 2021. Inclusion criteria were: diagnosis of MOG-antibody disease, AQP4-neuromyelitis optica spectrum disorder and multiple sclerosis, brain and cord MRI at least 6 months from relapse, EDSS on the day of MRI. Brain white matter T2 lesions, T1-hypointense lesions, cortical and cord lesions were identified. Random-forest models were constructed to classify patients as MOG-antibody disease/AQP4-neuromyelitis optica spectrum disorder/multiple sclerosis; a leave one out cross-validation procedure assessed the performance of the models. Based on the best discriminators between diseases, we proposed a guide to target investigations for MOG-antibody disease.</jats:p><jats:p>One hundred sixty-two patients with MOG-antibody disease (99F, mean age: 41 [±14] years, median EDSS: 2 [0-7.5]), 162 with AQP4-neuromyelitis optica spectrum disorder (132F, mean age: 51 [±14] years, median EDSS: 3.5 [0-8]), 189 with multiple sclerosis (132F, mean age: 40 [±10] years, median EDSS: 2 [0-8]) and 152 healthy controls (91F) were studied. In young patients (&lt;34 years), with low disability (EDSS &lt; 3), the absence of Dawson’s fingers, temporal lobe lesions and longitudinally extensive lesions in the cervical cord pointed towards a diagnosis of MOG-antibody disease instead of the other two diseases (accuracy: 76%, sensitivity: 81%, specificity: 84%, p &lt; 0.001). In these non-acute patients, a number of brain lesions &lt; 6 predicted MOG-antibody disease versus multiple sclerosis (accuracy: 83%, sensitivity: 82%, specificity: 83%, p &lt; 0.001). An EDSS &lt; 3 and the absence of longitudinally extensive lesions in the cervical cord predicted MOG-antibody disease versus AQP4-neuromyelitis optica spectrum disorder (accuracy: 76%, sensitivity: 89%, specificity: 62%, p &lt; 0.001). A workflow with sequential tests and supporting features has been proposed to guide a better identification of MOG-antibody disease patients.</jats:p><jats:p>Adult non-acute MOG-antibody disease patients showed distinctive clinical and MRI features when compared to AQP4-neuromyelitis optica spectrum disorder and multiple sclerosis. A careful inspection of the morphology of brain and cord lesions together with clinical information, can guide for further analyses towards diagnosis of MOG-antibody disease in clinical practice.</jats:p>