Application of deep-learning to the seronegative side of the NMO spectrum.

Autor: Cacciaguerra L; Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.; Vita-Salute San Raffaele University, Milan, Italy., Storelli L; Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy., Radaelli M; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy., Mesaros S; Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia., Moiola L; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy., Drulovic J; Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia., Filippi M; Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.; Vita-Salute San Raffaele University, Milan, Italy.; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.; Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy., Rocca MA; Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy. rocca.mara@hsr.it.; Vita-Salute San Raffaele University, Milan, Italy. rocca.mara@hsr.it.; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy. rocca.mara@hsr.it.
Jazyk: angličtina
Zdroj: Journal of neurology [J Neurol] 2022 Mar; Vol. 269 (3), pp. 1546-1556. Date of Electronic Publication: 2021 Jul 30.
DOI: 10.1007/s00415-021-10727-y
Abstrakt: Objectives: To apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-like manifestations and assess whether their structural features are similar to aquaporin-4-seropositive NMOSD or multiple sclerosis (MS) patients.
Patients and Methods: We analyzed 228 T2- and T1-weighted brain MRIs acquired from aquaporin-4-seropositive NMOSD (n = 85), MS (n = 95), aquaporin-4-seronegative NMOSD [n = 11, three with anti-myelin oligodendrocyte glycoprotein antibodies (MOG)], and aquaporin-4-seronegative patients with NMOSD-like manifestations (idiopathic recurrent optic neuritis and myelitis, n = 37), who were recruited from February 2010 to December 2019. Seventy-three percent of aquaporin-4-seronegative patients with NMOSD-like manifestations also had a clinical follow-up (median duration of 4 years). The deep-learning neural network architecture was based on four 3D convolutional layers. It was trained and validated on MRI scans of aquaporin-4-seropositive NMOSD and MS patients and was then applied to aquaporin-4-seronegative NMOSD and NMOSD-like manifestations. Assignment of unclassified aquaporin-4-seronegative patients was compared with their clinical follow-up.
Results: The final algorithm differentiated aquaporin-4-seropositive NMOSD and MS patients with an accuracy of 0.95. All aquaporin-4-seronegative NMOSD and 36/37 aquaporin-4-seronegative patients with NMOSD-like manifestations were classified as NMOSD. Anti-MOG patients had a similar probability of being NMOSD or MS. At clinical follow-up, one unclassified aquaporin-4-seronegative patient evolved to MS, three developed NMOSD, and the others did not change phenotype.
Conclusions: Our findings support the inclusion of aquaporin4-seronegative patients into NMOSD and suggest a possible expansion to aquaporin-4-seronegative unclassified patients with NMOSD-like manifestations. Anti-MOG patients are likely to have intermediate brain features between NMOSD and MS.
(© 2021. Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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