TY - GEN
T1 - Multi-stage biomarker models for progression estimation in Alzheimer’s disease
AU - Schmidt-Richberg, Alexander
AU - Guerrero, Ricardo
AU - Ledig, Christian
AU - Molina-Abril, Helena
AU - Frangi, Alejandro F.
AU - Rueckert, Daniel
AU - Alzheimer's Disease Neuroimaging Initiative
N1 - Funding Information:
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007 2013) under grant agreement no. 601055, VPH-DARE@IT.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015/6/22
Y1 - 2015/6/22
N2 - 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.
AB - 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.
KW - mild cognitive impairment
KW - quantile regression
KW - cognitive score
KW - cognitive normal
KW - disease progression rate
UR - http://www.scopus.com/inward/record.url?scp=84983528529&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19992-4_30
DO - 10.1007/978-3-319-19992-4_30
M3 - Conference contribution
C2 - 26221689
AN - SCOPUS:84983528529
SN - 9783319199917
VL - 9123
T3 - Lecture Notes in Computer Science
SP - 387
EP - 398
BT - Information Processing in Medical Imaging
A2 - Ourselin, Sebastien
A2 - Alexander, Daniel C.
A2 - Westin, Carl-Fredrik
A2 - Cardoso, M. Jorge
PB - Springer Cham
CY - Cham
T2 - 24th International Conference on Information Processing in Medical Imaging, IPMI 2015
Y2 - 28 June 2015 through 3 July 2015
ER -