Abstract #1416

Shape and Spatial Pattern Analysis through Covariance-based Estimations of MS lesions: the SSPACE-MS study

Carmen Tur^1,2, Francesco Grussu^1,3, Ferran Prados^1,4,5, Baris Kanber⁴, Thalis Charalambous^1,6, Declan T. Chard^1,7, Olga Ciccarelli^1,7, and Claudia A.M. Gandini Wheeler-Kingshott^1,8,9

¹UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ²Neurology, Luton and Dunstable University Hospital, Luton, United Kingdom, ³Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ⁴Centre for Medical Image Computing, Medical Physics and Biomedical Engineering department, University College London, London, United Kingdom, ⁵e-Health center, Universitat Oberta de Catalunya, Barcelona, Spain, ⁶Department of Medicine, St. George’s University of London, London, United Kingdom, ⁷University College London Hospitals Biomedical Research Centre, National Institute for Health Research, London, United Kingdom, ⁸Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ⁹Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy

Lesion load is the main predictor of future disability in multiple sclerosis (MS) but its predictive power is limited possibly because key aspects of lesions, such as their spatial distribution, morphology or pathological substrate are not being considered. Here we demonstrate the relevance of shape and spatial features of white matter lesions in the development of disability in MS through a novel approach. This new methodological framework (SSPACE-MS) is based on the analysis of the covariance matrix defined by the spatial position of lesional voxels. It can be applied longitudinally and is easily implementable in clinical practice.

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