New MS Subtypes Identified Using Artificial Intelligence

Priscilla Lynch 

April 13, 2021

UK scientists have used artificial intelligence (AI) to identify three new multiple sclerosis (MS) subtypes.

The findings, published in  Nature Communications,  suggest that magnetic resonance imaging (MRI)-based subtypes can predict MS disability progression and response to treatment, and be used to define groups of patients in interventional trials.

Currently, MS is clinically classified into four groups largely based on patient symptoms: clinically isolated syndrome, relapsing-remitting MS, primary-progressive MS or secondary progressive MS.

The researchers sought to discover if there were any as yet unidentified patterns in brain images, which would better guide MS treatment choice and identify who would best respond to a particular therapy.

To classify MS subtypes based on pathological features, they applied unsupervised machine learning to brain MRI scans acquired in previously published studies. They used 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, they defined 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.

Study author Prof Alan Thompson, Dean, University College London (UCL) Faculty of Brain Sciences, said: “We are aware of the limitations of the current descriptors of MS which can be less than clear when applied to prescribing treatment. Now with the help of AI and large datasets, we have made the first step towards a better understanding of the underlying disease mechanisms which may inform our current clinical classification. This is a fantastic achievement and has the potential to be a real game-changer, informing both disease evolution and selection of patients for clinical trials.”

Prospective research with clinical trials will be the next step to confirm these findings.

Eshaghi A, Young AL, Wijeratne PA, Prados F, Arnold DL, Narayanan S, Guttmann CRG, Barkhof F, Alexander DC, Thompson AJ, Chard D, Ciccarelli O. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun. 2021;12(1):2078. doi: 10.1038/s41467-021-22265-2. PMID: 33824310 View full text

This article originally appeared on Univadis, part of the Medscape Professional Network.

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