Evidence map›Paper›PMID 27807185›Full record

ArticleNeurology2016

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

Arman Eshaghi, Viktor Wottschel, Rosa Cortese, Massimiliano Calabrese, Mohammad Ali Sahraian, Alan J Thompson, Daniel C Alexander, Olga Ciccarelli

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. CD11cNeurology(R) neuroimmunology & neuroinflammation · 2024
    Observational
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  20. Advanced MRI quantification of neuroinflammatory disorders.Journal of neuroscience research · 2022
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Arman EshaghiFrom the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK. arman.eshaghi.14@ucl.ac.uk.
Viktor WottschelFrom the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
Rosa CorteseFrom the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
Massimiliano CalabreseFrom the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
Mohammad Ali SahraianFrom the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
Alan J ThompsonFrom the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
Daniel C AlexanderFrom the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
Olga CiccarelliFrom the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe tested whether brain gray matter (GM) imaging measures can differentiate between multiple sclerosis (MS) and neuromyelitis optica (NMO) using random-forest classification.

methodsNinety 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.

resultsThe 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%).

conclusionsGM 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.

Indexed as

AdultAtrophyBrainDiagnosis, DifferentialDisability EvaluationFemaleGray MatterHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMultiple Sclerosis, Relapsing-RemittingNeuromyelitis OpticaOrgan SizeRetrospective Studies

Identifiers

PMID27807185
PMCPMC5177679

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