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Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease

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Probabilistic disease progression modeling to characterize diagnostic uncertainty : Application to staging and prediction in Alzheimer's disease. / Lorenzi, Marco; Filippone, Maurizio; Frisoni, Giovanni B.; Alexander, Daniel C.; Ourselin, Sebastien; Alzheimer's Disease Neuroimaging Initiative.

In: NeuroImage, 24.10.2017.

Research output: Contribution to journalArticle

Harvard

Lorenzi, M, Filippone, M, Frisoni, GB, Alexander, DC, Ourselin, S & Alzheimer's Disease Neuroimaging Initiative 2017, 'Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease', NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.08.059

APA

Lorenzi, M., Filippone, M., Frisoni, G. B., Alexander, D. C., Ourselin, S., & Alzheimer's Disease Neuroimaging Initiative (2017). Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease. NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.08.059

Vancouver

Lorenzi M, Filippone M, Frisoni GB, Alexander DC, Ourselin S, Alzheimer's Disease Neuroimaging Initiative. Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease. NeuroImage. 2017 Oct 24. https://doi.org/10.1016/j.neuroimage.2017.08.059

Author

Lorenzi, Marco ; Filippone, Maurizio ; Frisoni, Giovanni B. ; Alexander, Daniel C. ; Ourselin, Sebastien ; Alzheimer's Disease Neuroimaging Initiative. / Probabilistic disease progression modeling to characterize diagnostic uncertainty : Application to staging and prediction in Alzheimer's disease. In: NeuroImage. 2017.

Bibtex Download

@article{bb4a32815a564f14914b95218640008a,
title = "Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease",
abstract = "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.",
keywords = "Alzheimer's disease, Clinical trials, Diagnosis, Disease progression modeling, Gaussian process",
author = "Marco Lorenzi and Maurizio Filippone and Frisoni, {Giovanni B.} and Alexander, {Daniel C.} and Sebastien Ourselin and {Alzheimer's Disease Neuroimaging Initiative}",
year = "2017",
month = oct,
day = "24",
doi = "10.1016/j.neuroimage.2017.08.059",
language = "English",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "ACADEMIC PRESS INC ELSEVIER SCIENCE",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Probabilistic disease progression modeling to characterize diagnostic uncertainty

T2 - Application to staging and prediction in Alzheimer's disease

AU - Lorenzi, Marco

AU - Filippone, Maurizio

AU - Frisoni, Giovanni B.

AU - Alexander, Daniel C.

AU - Ourselin, Sebastien

AU - Alzheimer's Disease Neuroimaging Initiative

PY - 2017/10/24

Y1 - 2017/10/24

N2 - 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.

AB - 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.

KW - Alzheimer's disease

KW - Clinical trials

KW - Diagnosis

KW - Disease progression modeling

KW - Gaussian process

UR - http://www.scopus.com/inward/record.url?scp=85034615495&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2017.08.059

DO - 10.1016/j.neuroimage.2017.08.059

M3 - Article

AN - SCOPUS:85034615495

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -

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