Three new subtypes of multiple sclerosis (MS) have been identified by using artificial intelligence (AI).
The new MS subtypes were defined as ‘cortex-led’, ‘normal-appearing white matter-led’, and ‘lesion-led.’
The study, by scientists at UCL, has been hailed as potentially being key in identifying those people more likely to have disease progression and help target treatments more effectively.
“Currently MS is classified broadly into progressive and relapsing groups, which are based on patient symptoms; it does not directly rely on the underlying biology of the disease, and therefore cannot assist doctors in choosing the right treatment for the right patients,” says Dr Arman Eshaghi, from UCL Queen Square Institute of Neurology.
“Here, we used artificial intelligence and asked the question: can AI find MS subtypes that follow a certain pattern on brain images?
“Our AI has uncovered three data-driven MS subtypes that are defined by pathological abnormalities seen on brain images.”
MS affects over 2.8 million people globally and 130,000 in the UK, and is classified into four ‘courses’, 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, 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.
They used 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.
Once SuStaIn had completed its analysis on the training MRI dataset, it was then used to identify the three subtypes in a separate independent cohort of 3,068 patients, validating its ability to detect the new MS subtypes.
“We did a further retrospective analysis of patient records to see how people with the newly identified MS subtypes responded to various treatments,” adds Dr Eshaghi.
“While further clinical studies are needed, there was a clear difference, by subtype, in patients’ response to different treatments and in accumulation of disability over time. This is an important step towards predicting individual responses to therapies.”
NIHR Research Professor Olga Ciccarelli, of UCL Queen Square Institute of Neurology, and the senior author of the study, adds: “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.”
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.
