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Abstract #1552

Strain-specific disease progression patterns of sporadic Creutzfeldt-Jakob disease revealed by Subtype and Stage Inference model

Riccardo Pascuzzo1, Alexandra L. Young2,3, Neil P. Oxtoby2, Janis Blevins4, Gianmarco Castelli1, Pierluigi Gambetti5, Brian S. Appleby4, Daniel C. Alexander2, and Alberto Bizzi1
1Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy, 2Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 3Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King′s College London, London, United Kingdom, 4National Prion Disease Pathology Surveillance Center, Case Western Reserve University, School of Medicine, Cleveland, OH, United States, 5Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH, United States

Transmission studies in animal models have identified four strains of sporadic Creutzfeldt-Jakob disease (sCJD). Using a data-driven approach, we aim to identify subgroups of sCJD patients with distinct diffusion-weighted MRI (DWI) abnormality patterns, and test their association with disease strains. We used an unsupervised machine-learning algorithm named Subtype and Stage Inference, that identified 5 clusters of patients each having a distinct pattern of DWI abnormality progression: one had initial involvement of the parieto-frontal cortex; two started with subcortical regions (striatum, thalamus and cerebellum); and two had cortical and limbic regions affected early. Data-driven subgroups were significantly associated with sCJD strains.

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