| Title: | Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data |
| Journal: | Nature Communications |
| Published: | 6 Apr 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33824310/ |
| DOI: | https://doi.org/10.1038/s41467-021-22265-2 |
| URL: | https://www.nature.com/articles/s41467-021-22265-2.pdf |
Publication 9626
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Abstract
Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.</p>
12 Authors
- Arman Eshaghi
- Alexandra L. Young
- Peter A. Wijeratne
- Ferran Prados
- Douglas L. Arnold
- Sridar Narayanan
- Charles R. G. Guttmann
- Frederik Barkhof
- Daniel C. Alexander
- Alan J. Thompson
- Declan Chard
- Olga Ciccarelli
1 Application
| Application ID | Title |
|---|---|
| 47233 | Developing a reference normative sample of the volumetric and myelin density of the spinal cord and brain imaging data in the UK Biobank |
Enabling scientific discoveries that improve human health