Abstract #1552

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

Riccardo Pascuzzo¹, Alexandra L. Young^2,3, 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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