Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

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 and Olga Ciccarelli
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Arman Eshaghi: University College London
Alexandra L. Young: University College London
Peter A. Wijeratne: University College London
Ferran Prados: University College London
Douglas L. Arnold: McGill University
Sridar Narayanan: McGill University
Charles R. G. Guttmann: Harvard Medical School
Frederik Barkhof: University College London
Daniel C. Alexander: University College London
Alan J. Thompson: University College London
Declan Chard: University College London
Olga Ciccarelli: University College London

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22265-2

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DOI: 10.1038/s41467-021-22265-2

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