title: Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics.
abstract: BACKGROUND
  Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far.
  
  
  OBJECTIVE
  To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics.
  
  
  METHODS
  MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared.
  
  
  RESULTS
  Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found.
  
  
  CONCLUSIONS
  Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.
authors.affiliation: Department of Neurology, MS Center, VU University Medical Center, Amsterdam, The Netherlands. jnp.zwemmer@vumc.nl
authors.forename: J N P
authors.initials: JN
authors.lastname: Zwemmer
authors.forename: J
authors.initials: J
authors.lastname: Berkhof
authors.forename: J A
authors.initials: JA
authors.lastname: Castelijns
authors.forename: F
authors.initials: F
authors.lastname: Barkhof
authors.forename: C H
authors.initials: CH
authors.lastname: Polman
authors.forename: B M J
authors.initials: BM
authors.lastname: Uitdehaag
mesh_terms: D000328:Adult; D000906:Antibodies / Q000097:blood / Q000134:cerebrospinal fluid; D003937:Diagnosis, Differential; D005260:Female; D005293:Ferritins / Q000097:blood / Q000134:cerebrospinal fluid; D005904:Glial Fibrillary Acidic Protein / Q000097:blood / Q000134:cerebrospinal fluid; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000097:blood / Q000145:classification* / Q000473:pathology* / Q000503:physiopathology; D009414:Nerve Growth Factors / Q000097:blood / Q000134:cerebrospinal fluid; D016900:Neurofilament Proteins / Q000276:immunology; D009483:Neuropsychological Tests; D064568:S100 Calcium Binding Protein beta Subunit; D009418:S100 Proteins / Q000097:blood / Q000134:cerebrospinal fluid; D018709:Statistics, Nonparametric
chemical_list: D000906:Antibodies; D005904:Glial Fibrillary Acidic Protein; D009414:Nerve Growth Factors; D016900:Neurofilament Proteins; D064568:S100 Calcium Binding Protein beta Subunit; D009418:S100 Proteins; D005293:Ferritins