果冻影院

MS affects over 2.8 million people globally and 130,000 in the UK, and is classified into four* 鈥榗ourses鈥� (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鈥� symptoms. These observations guide the timing and choice of treatment.

For this study, published in听Nature Communications, researchers wanted to find out if there were any 鈥� as yet unidentified 鈥撎齪atterns in brain images, which would better guide treatment choice and identify patients who would best respond to a particular therapy.听听

Explaining the research, lead author Dr Arman Eshaghi (果冻影院 Queen Square Institute of Neurology) said: 鈥淐urrently 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.

鈥淗ere, 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.鈥�

In this study, researchers applied the 果冻影院-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 鈥榗ortex-led鈥�, 鈥榥ormal-appearing white matter-led鈥�, and 鈥榣esion-led.鈥� 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 鈥榣ocked鈥� 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.

Dr Eshaghi added: 鈥淲e did a further retrospective analysis of patient records to see how people with the newly identified MS subtypes responded to various treatments.

While further clinical studies are needed, there was a clear difference, by subtype, in patients鈥� response to different treatments and in accumulation of disability over time. This is an important step towards predicting individual responses to therapies.鈥�

NIHR Research Professor Olga Ciccarelli (果冻影院 Queen Square Institute of Neurology), the senior author of the study, said:听鈥淭he method used to classify MS is currently focused on imaging changes only; we are extending the approach to including other clinical information.

鈥淭his 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.鈥�

One of the senior authors, Professor Alan Thompson, Dean of the 果冻影院 Faculty of Brain Sciences,听said: 鈥淲e 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.鈥�

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.

Dr Clare Walton, Head of Research at the MS Society, said: 鈥淲e鈥檙e delighted to have helped fund this study through our work with the International Progressive MS Alliance. MS is unpredictable and different for everyone, and we know one of our community鈥檚 main concerns is how their condition might develop. Having an MRI-based model to help predict future progression and tailor your treatment plan accordingly could be hugely reassuring to those affected. These findings also provide valuable insight into what drives progression in MS, which is crucial to finding new treatments for everyone. We鈥檙e excited to see what comes next.鈥�

MS is a neurological (nerve) condition听and is one of the most common causes of disability in young people. It arises when the immune system mistakenly attacks the coating (myelin sheaths) that wrap around nerves in the brain and spinal cord. This results in the electrical signals, which pass messages along the nerves, to be disrupted, travel more slowly, or fail to get through at all.

Most people听are听diagnosed between the ages of 20 and 50, however the first signs of MS often start years earlier. Common early signs include tingling, numbness, a loss of balance and problems with vision, but because other conditions cause the same symptoms, it can take time to reach a definitive diagnosis.

Many patients have relapsing MS at first, a form of the disease where symptoms come and go as nerves are damaged, repaired and damaged again. But about half have a progressive form of the condition in which nerve damage steadily accumulates and causes ever worsening disability. Patients may experience tremors, speech problems and muscle stiffness or spasms, and may need walking aids or a wheelchair.

The study was carried out with researchers at: Montreal Neurological Institute, McGill University, Canada; Harvard Medical School, USA; and VU University Medical Centre, the Netherlands.

* In present clinical guidelines, MS is currently categorised as either clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), primary-progressive MS (PPMS) or secondary progressive MS (SPMS).

Links

• 果冻影院 Queen Square Institute of Neurology
• 果冻影院 Faculty of Brain Sciences

Image

• MRI-based subtypes, published in

Media contact

Henry Killworth

Tel: +44 (0) 7881 833274

E: h.killworth [at] ucl.ac.uk