Learning Biomarker Models for Progression Estimation of Alzheimer's Disease.
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.
Biomedical Image Analysis Group, Imperial College London, London, United Kingdom.
Alexander
http://orcid.org/0000-0002-0633-4832
A
Schmidt-Richberg
Biomedical Image Analysis Group, Imperial College London, London, United Kingdom.
Christian
C
Ledig
Biomedical Image Analysis Group, Imperial College London, London, United Kingdom.
Ricardo
R
Guerrero
Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, United Kingdom.
Helena
H
Molina-Abril
Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, United Kingdom.
Alejandro
A
Frangi
Biomedical Image Analysis Group, Imperial College London, London, United Kingdom.
Daniel
D
Rueckert
D000544:Alzheimer Disease / Q000175:diagnosis / Q000473:pathology* / Q000503:physiopathology; D015415:Biomarkers / Q000032:analysis; D001921:Brain / Q000473:pathology* / Q000503:physiopathology; D018450:Disease Progression; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D008954:Models, Biological; D011336:Probability; D012044:Regression Analysis
D015415:Biomarkers