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Predicting conversion from clinically isolated syndrome to multiple sclerosis-An imaging-based machine learning

Haike Zhang1, Esther Alberts1, Viola Pongratz2

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Predicting multiple sclerosis (MS) conversion from clinically isolated syndrome (CIS) is possible using MRI lesion shape features. This method offers higher accuracy than current criteria, aiding early MS treatment decisions.

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Area of Science:

  • Neuroimaging
  • Neurology
  • Artificial Intelligence in Medicine

Background:

  • Clinically isolated syndrome (CIS) is the initial presentation for many individuals who may develop multiple sclerosis (MS).
  • Early prediction of MS conversion is crucial for timely therapeutic intervention.
  • Magnetic resonance imaging (MRI) is vital for evaluating CIS and identifying potential inflammatory brain lesions.

Purpose of the Study:

  • To investigate the utility of baseline MRI lesion image features for predicting conversion from CIS to MS.
  • To develop and validate a predictive model based on lesion characteristics.

Main Methods:

  • Analysis of 84 patients with CIS from a prospective observational cohort, followed for at least three years.
  • Brain lesions segmented using 3D FLAIR and 3D T1 MRI images via computer-assisted manual and automated methods.
  • Shape and brightness features extracted from segmented lesions to train an oblique random forest classifier.
  • Model performance validated using three-fold cross-validation.

Main Results:

  • Conversion to MS occurred in 79% (66/84) of patients within the follow-up period.
  • The predictive model utilizing lesion shape features achieved 84.5% accuracy in predicting conversion.
  • This shape-feature-based prediction outperformed the 2010 McDonald criteria's dissemination in space assessment (75% accuracy).
  • Intensity features did not significantly improve the prediction accuracy beyond shape features.

Conclusions:

  • Baseline MRI lesion shape parameters are valuable predictors of CIS to MS conversion.
  • A machine learning model based on lesion shape offers a more accurate early classification than current diagnostic criteria.
  • This approach supports the development of early diagnostic tools for MS, enabling prompt treatment initiation.