BACKGROUND: In a retrospective multicentre cohort study, we explored the association between brain atrophy and multiple sclerosis (MS) disability using different MRI scanners and protocols at multiple sites. METHODS: Relapse-onset MS patients were included if they had two clinical MRIs 12 months apart and ≥2 Expanded Disability Status Scale (EDSS) scores. Percentage brain volume change (PBVC), percentage grey matter change (PGMC), fluid-attenuated inversion recovery (FLAIR) lesion volume change, whole brain volume (BV), grey matter volume (GMV), FLAIR lesion volume and T1 hypointense lesion volume were assessed by icobrain. Disability was measured by EDSS scores and 6-month confirmed disability progression (CDP). RESULTS: Of the 260 relapse-onset MS patients included, 204 (78%) MRI pairs were performed in the same scanner and 56 (22%) pairs were from different scanners. 93% of patients were on treatment and mean PBVC was -0.26% (±0.52). During the median follow-up of 2.8 years from the second MRI, median EDSS change was 0.0 and 12% patients experienced 6-month CDP. Cross-sectional BV and GMV at the later MRI showed a trend for association with CDP (HR 0.99; 95% CI 0.98 to 1.00; p=0.06). Only BV at the later MRI was associated with EDSS score (β -0.03, SE 0.01, p<0.001) and the rate of EDSS change over time (β -0.001, SE 0.0003, p=0.02). There was no association between longitudinal PBVC or PGMC and CDP or EDSS (p>0.05). CONCLUSION: In this highly treated MS cohort with low disability accrual, only cross-sectional BV showed an association with future EDSS scores, while no MRI metric predicted 6-month CDP. These findings highlight the limitations of current clinical MRI measures in predicting disability worsening in real-world settings.

• Keywords
• MRI, MULTIPLE SCLEROSIS,
• Publication type
• Journal Article MeSH

Multiple sclerosis (MS) is characterized by a progressive worsening of disability over time. As many regulatory-cleared disease-modifying treatments aiming to slow down this progression are now available, a clear need has arisen for a personalized and data-driven approach to treatment optimization in order to more efficiently slow down disease progression and eventually, progressive disability worsening. This strongly depends on the availability of biomarkers that can detect and differentiate between the different forms of disease worsening, and on predictive models to estimate the disease trajectory for each patient under certain treatment conditions. To this end, we here describe a multicenter, retrospective, observational study, aimed at setting up a harmonized database to allow the development, training, optimization, and validation of such novel biomarkers and AI-based decision models. Additionally, the data will be used to develop the tools required to better monitor this progression and to generate further insights on disease worsening and progression, patient prognosis, treatment decisions and responses, and patient profiles of patients with MS.

• Keywords
• AI model, biomarker, clinical trial, data, disease worsening, multiple sclerosis, observational study, real-world data,
• Publication type
• Journal Article MeSH