MRI Patterns Distinguish AQP4 Antibody Positive Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis Clarke, L, Arnett, S, Bukhari, W, Khalilidehkordi, E, Jimenez Sanchez, S, O'Gorman, C, Sun, J, Prain, KM, Woodhall, M, Silvestrini, R, Bundell, CS, Abernethy, DA, Bhuta, S, Blum, S, Boggild, M, Boundy, K, Brew, BJ, Brownlee, W, Butzkueven, H, Carroll, WM, Chen, C, Coulthard, A, Dale, RC, Das, C, Fabis-Pedrini, MJ, Gillis, D, Hawke, S, Heard, R, Henderson, APD, Heshmat, S, Hodgkinson, S, Kilpatrick, TJ, King, J, Kneebone, C, Kornberg, AJ, Lechner-Scott, J, Lin, MW, Lynch, C, Macdonell, RAL, Mason, DF, McCombe, PA, Pereira, J, Pollard, JD, Ramanathan, S, Reddel, SW, Shaw, Cameron, Spies, JM, Stankovich, J, Sutton, I, Vucic, S, Walsh, M, Wong, RC, Yiu, EM, Barnett, MH, Kermode, AGK, Marriott, MP, Parratt, JDE, Slee, M, Taylor, BV, Willoughby, E, Brilot, F, Vincent, A, Waters, P and Broadley, SA 2021, MRI Patterns Distinguish AQP4 Antibody Positive Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis, Frontiers in Neurology, vol. 12, pp. 1-24, doi: 10.3389/fneur.2021.722237. Attached Files Name Description MIMEType Size Downloads Title MRI Patterns Distinguish AQP4 Antibody Positive Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis Author(s) Clarke, L Arnett, S Bukhari, W Khalilidehkordi, E Jimenez Sanchez, S O'Gorman, C Sun, J Prain, KM Woodhall, M Silvestrini, R Bundell, CS Abernethy, DA Bhuta, S Blum, S Boggild, M Boundy, K Brew, BJ Brownlee, W Butzkueven, H Carroll, WM Chen, C Coulthard, A Dale, RC Das, C Fabis-Pedrini, MJ Gillis, D Hawke, S Heard, R Henderson, APD Heshmat, S Hodgkinson, S Kilpatrick, TJ King, J Kneebone, C Kornberg, AJ Lechner-Scott, J Lin, MW Lynch, C Macdonell, RAL Mason, DF McCombe, PA Pereira, J Pollard, JD Ramanathan, S Reddel, SW Shaw, Cameron orcid.org/0000-0002-1966-5200 Spies, JM Stankovich, J Sutton, I Vucic, S Walsh, M Wong, RC Yiu, EM Barnett, MH Kermode, AGK Marriott, MP Parratt, JDE Slee, M Taylor, BV Willoughby, E Brilot, F Vincent, A Waters, P Broadley, SA Journal name Frontiers in Neurology Volume number 12 Article ID 722237 Start page 1 End page 24 Total pages 24 Publisher Frontiers Place of publication Lausanne, Switzerland Publication date 2021-09 ISSN 1664-2295 1664-2295 Keyword(s) BRAIN ABNORMALITIES Clinical Neurology CLINICALLY ISOLATED SYNDROMES diagnosis EXTENSIVE TRANSVERSE MYELITIS FOLLOW-UP INFLAMMATORY DEMYELINATING DISORDERS Life Sciences & Biomedicine magnetic resonance imaging MATTER INVOLVEMENT multiple sclerosis neuromyelitis optica Neurosciences Neurosciences & Neurology NMOSD PREDICT CONVERSION RESONANCE-IMAGING FINDINGS REVERSIBLE ENCEPHALOPATHY SYNDROME Science & Technology SPINAL-CORD LESIONS Summary Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are inflammatory diseases of the CNS. Overlap in the clinical and MRI features of NMOSD and MS means that distinguishing these conditions can be difficult. With the aim of evaluating the diagnostic utility of MRI features in distinguishing NMOSD from MS, we have conducted a cross-sectional analysis of imaging data and developed predictive models to distinguish the two conditions. NMOSD and MS MRI lesions were identified and defined through a literature search. Aquaporin-4 (AQP4) antibody positive NMOSD cases and age- and sex-matched MS cases were collected. MRI of orbits, brain and spine were reported by at least two blinded reviewers. MRI brain or spine was available for 166/168 (99%) of cases. Longitudinally extensive (OR = 203), “bright spotty” (OR = 93.8), whole (axial; OR = 57.8) or gadolinium (Gd) enhancing (OR = 28.6) spinal cord lesions, bilateral (OR = 31.3) or Gd-enhancing (OR = 15.4) optic nerve lesions, and nucleus tractus solitarius (OR = 19.2), periaqueductal (OR = 16.8) or hypothalamic (OR = 7.2) brain lesions were associated with NMOSD. Ovoid (OR = 0.029), Dawson's fingers (OR = 0.031), pyramidal corpus callosum (OR = 0.058), periventricular (OR = 0.136), temporal lobe (OR = 0.137) and T1 black holes (OR = 0.154) brain lesions were associated with MS. A score-based algorithm and a decision tree determined by machine learning accurately predicted more than 85% of both diagnoses using first available imaging alone. We have confirmed NMOSD and MS specific MRI features and combined these in predictive models that can accurately identify more than 85% of cases as either AQP4 seropositive NMOSD or MS Language eng DOI 10.3389/fneur.2021.722237 Field of Research 1103 Clinical Sciences 1109 Neurosciences 1701 Psychology HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30156480 Document type: Journal Article Collections: Faculty of Health School of Medicine Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. 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