AI gets real: using routine clinical data and Artificial Intelligence to predict worsening of Multiple Sclerosis despite treatment (AIMS)

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Multiple sclerosis (MS) can be a severely disabling disease. Widespread use of MRI and revisions to MS diagnosis have enabled earlier identification and considerable progress in developing therapies for MS. Access to the treatments in MS has been improved recently, but although most people with relapsing MS can benefit from them and outcomes are improving as a result, no single treatment is right for everybody. Some people with MS will relapse, and over the long term will gather psychical and cognitive disability. Despite progress in assessing response to treatments, individual prediction of MS outcomes over the long-term is still inaccurate; the need for information on individualised long-term prognosis for MS patients' forecasting is frequently unmet.A wealth of clinical and MRI data from patients with MS are acquired every year in clinical practice, but only part of these data are used for clinical decision making. MRI is paramount in MS diagnosis and monitoring, but most often the only feature of clinical use are the MS lesions. However, the structure of MS brains visualised on imaging is likely related to several aspects of MS biology. We propose that a set of structural characteristics extracted from the MRI brain scans of people with MS are related to biological changes which are meaningful to MS, and may therefore act as predictive markers for outcome. Computational imaging approaches using artificial intelligence (AI) have achieved successes in automatically quantifying lesions. AI-based classification of the scan's features referred to as 'radiomics' can provide more detailed characterisation than is possible by the naked eye and can offer the means to extract more information from the whole-image MRI brain scans.Radiomics-based biomarkers (indicators) have shown success in cancer treatment, but are still in early development in MS. In this study, we aim to use whole-image brain MRI scans processed with AI techniques and detect the clinical and MRI profiles that predict accumulation of MS-related disability or cognitive impairment. We will take advantage of our MS clinic which is one of the largest in England, and the Nottingham MS Society Register. We will draw on a unique environment of research experts in MS, clinical trials, MRI, computational imaging and predictive modelling who work collaboratively with patients and carers within the NIHR Nottingham Biomedical Research Centre. We will use individual data about patients' clinical condition, their demographics and their scans, and analyse it by the means of AI. Using clinical information which patients have consented for us to use, and the MRI images before starting treatment, we'll train a computer to use mathematical models to predict whether a person's MS will determine accumulating disability or cognitive impairment over the long term. Furthermore, we seek to see if the profile can predict development of disability in other patient groups, by validating the models in large sets of MRI scans obtained from other groups of people with MS using different scanners. We will use a large group of patients from the United States, and also align with a clinical trial ongoing in UK and US, which compares treatments for MS with different strengths.At least a third of people with MS starting on a first-line MS treatment require subsequent escalation to a stronger therapy. By identifying early, at diagnosis, who is likely to fare worse over the long term, we could offer them a more tailored treatment approach. This is a crucial step towards "personalised medicine", which means we'll be able to prescribe the right medication for the right person at the right time.