Abstract #3376

Multiparametric MRI neuroimaging signatures predict cognitive decline in Multiple Sclerosis: a 5-year longitudinal study

Oun Al-iedani^1,2, Stasson Lea², Abdulaziz Alshehri^2,3,4, Vicki E. Maltby^2,5,6, Rodney Lea^2,7, Saadallah Ramadan^2,3, and Jeannette Lechner-Scott^2,5,6

¹School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia, ²Hunter Medical Research Institute, New Lambton Heights, Australia, ³School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia, ⁴Department of Radiology, King Fahad University Hospital, Dammam, Saudi Arabia, ⁵Department of Neurology, John Hunter Hospital, New Lambton Heights, Australia, ⁶School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia, ⁷School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia

Synopsis

Keywords: Multiple Sclerosis, RadiomicsThis novel longitudinal study evaluates multiparametric MRI signature for predicting cognitive decline in multiple sclerosis (MS) cohort followed for 5-years using a penalised regression machine learning approach (GLMnet). 43 MS participants were assessed at baseline and 5-years follow-up. Baseline (input) data consisted of 76 multiparametric MRI measures for different brain regions and tissues. The best performing model was for a change in tARCS (15 features; r=0.7±0.07), which was substantially higher than that for SDMT (r=0.496±0.08). These findings highlight the importance of using measures from multiple MR modalities analysed in combination with machine learning techniques when assessing cognitive decline.

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