Table_3_MRI Patterns Distinguish AQP4 Antibody Positive Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis.pdf (94.17 kB)

Table_3_MRI Patterns Distinguish AQP4 Antibody Positive Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis.pdf

dataset

posted on 2021-09-09, 04:28 authored by Laura Clarke, Simon Arnett, Wajih Bukhari, Elham Khalilidehkordi, Sofia Jimenez Sanchez, Cullen O'Gorman, Jing Sun, Kerri M. Prain, Mark Woodhall, Roger Silvestrini, Christine S. Bundell, David A. Abernethy, Sandeep Bhuta, Stefan Blum, Mike Boggild, Karyn Boundy, Bruce J. Brew, Wallace Brownlee, Helmut Butzkueven, William M. Carroll, Cella Chen, Alan Coulthard, Russell C. Dale, Chandi Das, Marzena J. Fabis-Pedrini, David Gillis, Simon Hawke, Robert Heard, Andrew P. D. Henderson, Saman Heshmat, Suzanne Hodgkinson, Trevor J. Kilpatrick, John King, Christopher Kneebone, Andrew J. Kornberg, Jeannette Lechner-Scott, Ming-Wei Lin, Christopher Lynch, Richard A. L. Macdonell, Deborah F. Mason, Pamela A. McCombe, Jennifer Pereira, John D. Pollard, Sudarshini Ramanathan, Stephen W. Reddel, Cameron P. Shaw, Judith M. Spies, James Stankovich, Ian Sutton, Steve Vucic, Michael Walsh, Richard C. Wong, Eppie M. Yiu, Michael H. Barnett, Allan G. K. Kermode, Mark P. Marriott, John D. E. Parratt, Mark Slee, Bruce V. Taylor, Ernest Willoughby, Fabienne Brilot, Angela Vincent, Patrick Waters, Simon A. Broadley

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.