title: Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning.
abstract: OBJECTIVE
  A novel characterization of Clinically Isolated Syndrome (CIS) patients according to lesion patterns is proposed. More specifically, patients are classified according to the nature of inflammatory lesions patterns. It is expected that this characterization can infer new prospective figures from the earliest imaging signs of Multiple Sclerosis (MS), since it can provide a classification of different types of lesions across patients.
  
  
  METHODS
  The method is based on a two-tiered classification. Initially, the spatio-temporal lesion patterns are classified. The discovered lesion patterns are then used to characterize groups of patients. The patient groups are validated using statistical measures and by correlations at 24-month follow-up with hypointense lesion loads.
  
  
  RESULTS
  The methodology identified 3 statistically significantly different clusters of lesion patterns showing p-values smaller than 0.01. Moreover, these patterns defined at baseline correlated with chronic hypointense lesion volumes by follow-up with an R(2) score of 0.90.
  
  
  CONCLUSIONS
  The proposed methodology is capable of identifying three major different lesion patterns that are heterogeneously present in patients, allowing a patient classification using only two MRI scans. This finding may lead to more accurate prognosis and thus to more suitable treatments at early stage of MS.
authors.affiliation: INRIA-Atlantique, Campus de Beaulieu, Rennes, France.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Crimi
authors.affiliation: INRIA-Atlantique, Campus de Beaulieu, Rennes, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Commowick
authors.affiliation: CRMBM-CNRS-Aix-Marseille University, Marseille, France; CHU Reims, Reims, France.
authors.forename: Adil
authors.initials: A
authors.lastname: Maarouf
authors.affiliation: CHU Rennes, Rennes, France.
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Ferré
authors.affiliation: CHU Rennes, Rennes, France.
authors.forename: Elise
authors.initials: E
authors.lastname: Bannier
authors.affiliation: CHU Reims, Reims, France.
authors.forename: Ayman
authors.initials: A
authors.lastname: Tourbah
authors.affiliation: CHU Toulouse, Toulouse, France.
authors.forename: Isabelle
authors.initials: I
authors.lastname: Berry
authors.affiliation: CRMBM-CNRS-Aix-Marseille University, Marseille, France.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Ranjeva
authors.affiliation: CHU Rennes, Rennes, France.
authors.forename: Gilles
authors.initials: G
authors.lastname: Edan
authors.affiliation: INRIA-Atlantique, Campus de Beaulieu, Rennes, France.
authors.forename: Christian
authors.initials: C
authors.lastname: Barillot
mesh_terms: D000328:Adult; D000465:Algorithms; D003287:Contrast Media; D003911:Dextrans; D005260:Female; D005682:Gadolinium; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D058185:Magnetite Nanoparticles; D008297:Male; D009103:Multiple Sclerosis / Q000175:diagnosis*; D013997:Time Factors
chemical_list: D003287:Contrast Media; D003911:Dextrans; D058185:Magnetite Nanoparticles; C097921:ferumoxtran-10; D005682:Gadolinium