Paper Info
Title
Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease
Abstract
The estimation of disease progression in Alzheimer's disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification.
Year
DOI
Venue
2015
10.1007/978-3-319-19992-4_30
IPMI
Field
DocType
Volume
Disease,Longitudinal study,Biomarker (medicine),Statistical model,Probabilistic logic,Physical medicine and rehabilitation,Neuroimaging,Cognition,Medicine,Quantile regression
Conference
24
ISSN
Citations 
PageRank 
1011-2499
1
0.38
References 
Authors
3
6
Name
Order
Citations
PageRank
Alexander Schmidt-Richberg122624.43
Ricardo Guerrero210010.35
Christian Ledig348927.08
Helena Molina-Abril48210.87
Alejandro F. Frangi54333309.21
Daniel Rueckert69338637.58