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Publication Detail
Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease.
  • Publication Type:
    Journal article
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  • Authors:
    Lorenzi M, Filippone M, Frisoni GB, Alexander DC, Ourselin S, Alzheimer's Disease Neuroimaging Initiative
  • Publication date:
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  • Keywords:
    Alzheimer's Disease Neuroimaging Initiative
  • Addresses:
    Asclepios Research Project, Université Côte d'Azur, Inria, France; Translational Imaging Group, Centre for Medical Image Computing, University College London, UK. Electronic address: marco.lorenzi@inria.fr.
Disease progression modeling (DPM) of Alzheimer's disease (AD) aims at revealing long term pathological trajectories from short term clinical data. Along with the ability of providing a data-driven description of the natural evolution of the pathology, DPM has the potential of representing a valuable clinical instrument for automatic diagnosis, by explicitly describing the biomarker transition from normal to pathological stages along the disease time axis. In this work we reformulated DPM within a probabilistic setting to quantify the diagnostic uncertainty of individual disease severity in an hypothetical clinical scenario, with respect to missing measurements, biomarkers, and follow-up information. We show that the staging provided by the model on 582 amyloid positive testing individuals has high face validity with respect to the clinical diagnosis. Using follow-up measurements largely reduces the prediction uncertainties, while the transition from normal to pathological stages is mostly associated with the increase of brain hypo-metabolism, temporal atrophy, and worsening of clinical scores. The proposed formulation of DPM provides a statistical reference for the accurate probabilistic assessment of the pathological stage of de-novo individuals, and represents a valuable instrument for quantifying the variability and the diagnostic value of biomarkers across disease stages.
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