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dc.contributor.authorClarke, Laura
dc.contributor.authorArnett, Simon
dc.contributor.authorBukhari, Wajih
dc.contributor.authorKhalilidehkordi, Elham
dc.contributor.authorJimenez Sanchez, Sofia
dc.contributor.authorO’Gorman, Cullen
dc.contributor.authorSun, Jing
dc.contributor.authorPrain, Kerri M
dc.contributor.authorWoodhall, Mark
dc.contributor.authorSilvestrini, Roger
dc.contributor.authorBundell, Christine S
dc.contributor.authorBhuta, Sandeep
dc.contributor.authorHeshmat, Saman
dc.contributor.authorBroadley, Simon A
dc.contributor.authoret al.
dc.date.accessioned2021-09-13T02:10:15Z
dc.date.available2021-09-13T02:10:15Z
dc.date.issued2021
dc.identifier.issn1664-2295
dc.identifier.doi10.3389/fneur.2021.722237
dc.identifier.urihttp://hdl.handle.net/10072/407880
dc.description.abstractNeuromyelitis 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.
dc.description.peerreviewedYes
dc.publisherFrontiers Media SA
dc.relation.ispartofpagefrom1554
dc.relation.ispartofjournalFrontiers in Neurology
dc.relation.ispartofvolume12
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchNeurosciences
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode3209
dc.titleMRI Patterns Distinguish AQP4 Antibody Positive Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationClarke, L; Arnett, S; Bukhari, W; Khalilidehkordi, E; Jimenez Sanchez, S; O’Gorman, C; Sun, J; Prain, KM; Woodhall, M; Silvestrini, R; Bundell, CS; Bhuta, S; Heshmat, S; Broadley, SA; et al., MRI Patterns Distinguish AQP4 Antibody Positive Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis, Frontiers in Neurology, 2021, 12, pp. 1554
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-09-12T00:27:59Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 Clarke, Arnett, Bukhari, Khalilidehkordi, Jimenez Sanchez, O'Gorman, Sun, Prain, Woodhall, Silvestrini, Bundell, Abernethy, Bhuta, Blum, Boggild, Boundy, Brew, Brownlee, Butzkueven, Carroll, Chen, Coulthard, Dale, Das, Fabis-Pedrini, Gillis, Hawke, Heard, Henderson, Heshmat, Hodgkinson, Kilpatrick, King, Kneebone, Kornberg, Lechner-Scott, Lin, Lynch, Macdonell, Mason, McCombe, Pereira, Pollard, Ramanathan, Reddel, Shaw, Spies, Stankovich, Sutton, Vucic, Walsh, Wong, Yiu, Barnett, Kermode, Marriott, Parratt, Slee, Taylor, Willoughby, Brilot, Vincent, Waters and Broadley. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
gro.hasfulltextFull Text
gro.griffith.authorSun, Jing
gro.griffith.authorBukhari, Wajih u.
gro.griffith.authorBhuta, Sandeep
gro.griffith.authorKhalilidehkordi, Ellie
gro.griffith.authorArnett, Simon
gro.griffith.authorClarke, Laura
gro.griffith.authorBroadley, Simon
gro.griffith.authorSanchez, Sofia
gro.griffith.authorO'Gorman, Cullen
gro.griffith.authorHeshmat, Sam
gro.griffith.authorJimenez Sanchez, Sofia
dc.subject.socioeconomiccode2001 Clinical health


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