UCL scientists have used artificial intelligence (AI) to identify three new multiple sclerosis (MS) subtypes. Researchers say the groundbreaking findings will help identify those people more likely to have disease progression and help target treatments more effectively.
MS affects over 2.8 million people globally and 130,000 in the UK, and is classified into four* ‘courses’ (groups), which are defined as either relapsing or progressive. Patients are categorised by a mixture of clinical observations, assisted by MRI brain images, and patients’ symptoms. These observations guide the timing and choice of treatment.
For this study, published in Nature Communications, researchers wanted to find out if there were any – as yet unidentified – patterns in brain images, which would better guide treatment choice and identify patients who would best respond to a particular therapy.
In this study, researchers applied the UCL-developed AI tool, SuStaIn (Subtype and Stage Inference), to the MRI brain scans of 6,322 MS patients. The unsupervised SuStaIn trained itself and identified three (previously unknown) patterns.
The new MS subtypes were defined as ‘cortex-led’, ‘normal-appearing white matter-led’, and ‘lesion-led.’ These definitions relate to the earliest abnormalities seen on the MRI scans within each pattern.
Once SuStaIn had completed its analysis on the training MRI dataset, it was ‘locked’ and then used to identify the three subtypes in a separate independent cohort of 3,068 patients thereby validating its ability to detect the new MS subtypes.
NIHR Research Professor Olga Ciccarelli (UCL Queen Square Institute of Neurology), the senior author of the study, said: “The method used to classify MS is currently focused on imaging changes only; we are extending the approach to including other clinical information.
“This exciting field of research will lead to an individual definition of MS course and individual prediction of treatment response in MS using AI, which will be used to select the right treatment for the right patient at the right time.”
One of the senior authors, Professor Alan Thompson, Dean of the 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.”
Researchers say the findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and can now be used to define groups of patients in interventional trials. Prospective research with clinical trials is required as the next step to confirm these findings.
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