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Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest

Eshaghi, A; Wottschel, V; Cortese, R; Calabrese, M; Sahraian, MA; Thompson, AJ; Alexander, DC; (2016) Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest. Neurology , 87 (23) pp. 2463-2470. 10.1212/WNL.0000000000003395. Green open access

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Abstract

OBJECTIVE: We tested whether brain gray matter (GM) imaging measures can differentiate between multiple sclerosis (MS) and neuromyelitis optica (NMO) using random-forest classification. METHODS: Ninety participants (25 patients with MS, 30 patients with NMO, and 35 healthy controls [HCs]) were studied in Tehran, Iran, and 54 (24 patients with MS, 20 patients with NMO, and 10 HCs) in Padua, Italy. Participants underwent brain T1 and T2/fluid-attenuated inversion recovery MRI. Volume, thickness, and surface of 50 cortical GM regions and volumes of the deep GM nuclei were calculated and used to construct 3 random-forest models to classify patients as either NMO or MS, and separate each patient group from HCs. Clinical diagnosis was the gold standard against which the accuracy was calculated. RESULTS: The classifier distinguished patients with MS, who showed greater atrophy especially in deep GM, from those with NMO with an average accuracy of 74% (sensitivity/specificity: 77/72; p < 0.01). When we used thalamic volume (the most discriminating GM measure) together with the white matter lesion volume, the accuracy of the classification of MS vs NMO was 80%. The classifications of MS vs HCs and NMO vs HCs achieved higher accuracies (92% and 88%). CONCLUSIONS: GM imaging biomarkers, automatically obtained from clinical scans, can be used to distinguish NMO from MS, even in a 2-center setting, and may facilitate the differential diagnosis in clinical practice. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that GM imaging biomarkers can distinguish patients with NMO from those with MS.

Type: Article
Title: Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1212/WNL.0000000000003395
Publisher version: http://doi.org/10.1212/WNL.0000000000003395
Language: English
Additional information: © 2016 American Academy of Neurology. Unauthorized reproduction of this article is prohibited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1524380
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