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Updated: Nov 10, 2025

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
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Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.

Arman Eshaghi1,2, Alexandra L Young3,4, Peter A Wijeratne3

  • 1Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK. a.eshaghi@ucl.ac.uk.

Nature Communications
|April 7, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning identified three multiple sclerosis (MS) subtypes from MRI scans: cortex-led, normal-appearing white matter-led, and lesion-led. The lesion-led subtype shows the highest disability progression and relapse rates.

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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence

Background:

  • Multiple sclerosis (MS) classification into four phenotypes lacks clear pathophysiological boundaries, hindering treatment stratification.
  • Machine learning offers a method to identify patient subgroups with similar features from complex datasets.

Purpose of the Study:

  • To classify multiple sclerosis (MS) subtypes using pathological features identified via unsupervised machine learning on brain MRI scans.
  • To validate MRI-based MS subtypes in an independent patient cohort.

Main Methods:

  • Unsupervised machine learning applied to brain MRI scans from a training dataset of 6322 MS patients.
  • Independent validation performed on a cohort of 3068 MS patients.
  • Subtypes defined based on the location of earliest pathological abnormalities: cortex-led, normal-appearing white matter-led, and lesion-led.

Main Results:

  • Three distinct MRI-based MS subtypes were identified: cortex-led, normal-appearing white matter-led, and lesion-led.
  • The lesion-led MS subtype demonstrated the highest risk of confirmed disability progression (CDP) and the highest relapse rate.
  • Patients with the lesion-led MS subtype showed a positive treatment response in specific clinical trials.

Conclusions:

  • MRI-based MS subtypes can predict disease disability progression and treatment response.
  • These subtypes may serve as valuable criteria for patient stratification in future interventional trials.
  • This approach enhances understanding of MS heterogeneity and personalized medicine strategies.