Learning biomarker models for progression estimation of Alzheimer's disease

Schmidt-Richberg A, Ledig C, Guerrero R, Molina-Abril H, Frangi A, Rueckert D (2016)

Publication Type: Journal article

Publication year: 2016

Journal

PLoS ONE Public Library of Science

Book Volume: 11

Article Number: e0153040

Journal Issue: 4

DOI: 10.1371/journal.pone.0153040

Abstract

Being able to estimate a patient's progress in the course of Alzheimer's disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and - employing cognitive scores and image-based biomarkers - real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression. Copyright:

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University of Sheffield GB United Kingdom (GB)

How to cite

APA:

Schmidt-Richberg, A., Ledig, C., Guerrero, R., Molina-Abril, H., Frangi, A., & Rueckert, D. (2016). Learning biomarker models for progression estimation of Alzheimer's disease. PLoS ONE, 11(4). https://doi.org/10.1371/journal.pone.0153040

MLA:

Schmidt-Richberg, Alexander, et al. "Learning biomarker models for progression estimation of Alzheimer's disease." PLoS ONE 11.4 (2016).

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