Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning

PLoS One. 2014 Apr 1;9(4):e93024. doi: 10.1371/journal.pone.0093024. eCollection 2014.

Abstract

Objectives: 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Contrast Media
  • Dextrans*
  • Female
  • Gadolinium*
  • Humans
  • Machine Learning*
  • Magnetic Resonance Imaging*
  • Magnetite Nanoparticles*
  • Male
  • Multiple Sclerosis / diagnosis*
  • Time Factors

Substances

  • Contrast Media
  • Dextrans
  • Magnetite Nanoparticles
  • ferumoxtran-10
  • Gadolinium

Grants and funding

The study was supported by ARSEP (Foundation pour l′Aide a la Recherche sur la Scelerose en Plaques). The scholarship of Dr. Crimi has been financed by ERCIM (European Research Consortium for Informatics and Mathematics). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.