{
  "abstract": "Background Multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) can share similar features, posing diagnostic challenges. In this study, we identified sets of conventional MRI lesion distribution criteria proposed for disease differentiation and investigated their clinical utility.Methods We searched five electronic databases for English-written and peer-reviewed diagnostic accuracy studies that included brain MRI at least. Hierarchical and univariate random-effects logistic regression models were employed for diagnostic accuracy meta-analysis. Heterogeneity was explored with subgroup analyses. Certainty of evidence was assessed using the GRADEpro tool.Results Three sets of criteria (‘Matthews’, ‘Cacciaguerra’, ‘MS lesion checklist’) were investigated in 11 studies (2008 patients; MS, n=1037; NMOSD, n=842; MOGAD, n=129), with low applicability concerns. Overall pooled sensitivity and specificity of the Matthews brain MRI criteria (MS vs seropositive-NMOSD differentiation) were 0.92 (0.86 to 0.96) and 0.85 (0.79 to 0.90), respectively, with higher diagnostic values in non-Caucasian populations and during follow-up. Pooled sensitivity and specificity of the Cacciaguerra brain-spinal cord criteria (seropositive-NMOSD vs MS differentiation) were 0.96 (0.76 to 0.99) and 0.83 (0.71 to 0.90), respectively. The MS lesion checklist (MS vs NMOSD/MOGAD differentiation) had lower diagnostic accuracy measures (sensitivity, specificity: 0.74, 0.79, respectively). The Matthews criteria provided the strongest moderate certainty evidence and also showed high pooled diagnostic accuracy for MS versus seronegative-NMOSD (sensitivity: 0.93 (0.84 to 0.97)); specificity: 0.90 (0.80 to 0.95)) and for MS versus MOGAD differentiation (sensitivity: 0.86 (0.81 to 0.90); specificity: 0.87 (0.76 to 0.93)).Conclusions Lesion distribution criteria can accurately discriminate between MS, NMOSD and MOGAD. Further optimised validation studies, and revisions or extensions may support sustained implementation.PROSPERO registration number CRD42023472178.",
  "authors": [
    {
      "affiliations": [
        "Department of Epidemiology and Public Health, University College London Research Department of Epidemiology and Public Health, London, England, UK",
        "Laboratory of Clinical Pharmacology, Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Makedonia Thraki, Greece",
        "Department of Neurology, Agios Pavlos General Hospital of Thessaloniki, Thessaloniki, Greece"
      ],
      "name": "Vasilis-Spyridon Tseriotis"
    },
    {
      "affiliations": [
        "Neurology-Neuroimmunology Department, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain"
      ],
      "name": "Georgina Arrambide"
    },
    {
      "affiliations": [
        "Neuroimmunology Unit, Department of Neurosciences, Hospital Alemán, Buenos Aires, Argentina",
        "CENRos, Neuroimmunology Clinic, INECO Neurociencias Oroño, Rosario, Argentina"
      ],
      "name": "Edgar Carnero Contentti"
    },
    {
      "affiliations": [
        "Neurology-Neuroimmunology Department, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain"
      ],
      "name": "Carmen Tur"
    },
    {
      "affiliations": [
        "Department of Neuroradiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany"
      ],
      "name": "Mike P Wattjes"
    },
    {
      "affiliations": [
        "Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy"
      ],
      "name": "Rosa Cortese"
    },
    {
      "affiliations": [
        "First Department of Neurology, Aeginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece"
      ],
      "name": "Dimos-Dimitrios Mitsikostas"
    },
    {
      "affiliations": [
        "Multiple Sclerosis Center, Second Department of Neurology, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece"
      ],
      "name": "Nikolaos Grigoriadis"
    },
    {
      "affiliations": [
        "Institute for Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany"
      ],
      "name": "Antonis Adamou"
    },
    {
      "affiliations": [
        "Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA"
      ],
      "name": "David-Dimitris Chlorogiannis"
    },
    {
      "affiliations": [
        "Department of Anesthesiology and Intensive Care Medicine, Hannover Medical School, Hannover, Germany"
      ],
      "name": "Eleftherios Beltsios"
    },
    {
      "affiliations": [
        "The Danish Multiple Sclerosis Registry, Department of Neurology, Copenhagen University Hospital – Rigshospitalet Glostrup, Copenhagen, Denmark",
        "Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital – Rigshospitalet Glostrup, Copenhagen, Denmark",
        "Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark"
      ],
      "name": "Melinda Magyari"
    },
    {
      "affiliations": [
        "Department of Neurology, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark"
      ],
      "name": "Thomas Clement Truelsen"
    },
    {
      "affiliations": [
        "Experimental Neurophysiology Unit, Institute of Experimental Neurology (INSPE), Scientific Institute IRCCS San Raffaele, Milan, Italy",
        "Vita-Salute San Raffaele University, Milan, Italy",
        "Department of Neurorehabilitation Sciences, Casa di Cura Igea, Milan, Italy"
      ],
      "name": "Letizia Leocani"
    },
    {
      "affiliations": [
        "Neurology-Neuroimmunology Department, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain"
      ],
      "name": "Xavier Montalban"
    }
  ],
  "title": "MRI lesion distribution criteria for MS, NMOSD and MOGAD differentiation: a systematic review and meta-analysis",
  "uid": "e5919ba9-8edd-568b-a02d-010b140f7cb2"
}