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.
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References
Atchison, T.B., Bradshaw, M., Massman, P.J.: Investigation of profile difference between Alzheimer’s disease patients declining at different rates: examination of baseline neuropsychological data. Arch. Clin. Neuropsychol. 19(8), 1007–1015 (2004)
Delor, I., Charoin, J.E., Gieschke, R., Retout, S., Jacqmin, P.: Modeling Alzheimer’s disease progression using disease onset time and disease trajectory concepts applied to CDR-SOB scores from ADNI. CPT Pharmacomet. Syst. Pharmacol. 2(10), e78 (2013)
Doody, R.S., Pavlik, V., Massman, P., Rountree, S., Darby, E., Chan, W.: Predicting progression of Alzheimer’s disease. Alzheimers Res. Ther. 2(1), 2 (2010)
Fonteijn, H.M., Modat, M., Clarkson, M.J., Barnes, J., Lehmann, M., Hobbs, N.Z., Scahill, R.I., Tabrizi, S.J., Ourselin, S., Fox, N.C., Alexander, D.C.: An event-based model for disease progression and its application in familial Alzheimer’s disease and Huntington’s disease. NeuroImage 60(3), 1880–1889 (2012)
Green, C., Shearer, J., Ritchie, C.W., Zajicek, J.P.: Model-based economic evaluation in Alzheimer’s disease: a review of the methods available to model Alzheimer’s disease progression. Value Health 14(5), 621–630 (2011)
Guerrero, R., Ledig, C., Rueckert, D.: Manifold alignment and transfer learning for classification of Alzheimer’s disease. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 77–84. Springer, Heidelberg (2014)
Ledig, C., Guerrero, R., Tong, T., Gray, K., Schmidt-Richberg, A., Makropoulos, A., Heckemann, R., Rueckert, D.: Alzheimer’s disease state classification using structural volumetry, cortical thickness and intensity features. In: MICCAI Workshop Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data, pp. 55–64 (2014)
Ledig, C., Heckemann, R.A., Makropoulos, A., Hammers, A., Lötjönen, J., Menon, D., Rueckert, D.: Robust whole-brain segmentation: application to traumatic brain injury. Med. Image Anal. 21(1), 40–58 (2015)
Trzepacz, P.T., Yu, P., Sun, J., Schuh, K., Case, M., Witte, M.M., Hochstetler, H., Hake, A.: Alzheimer’s disease neuroimaging initiative: comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer’s dementia. Neurobiol. Aging 35(1), 143–151 (2014)
Yang, E., Farnum, M., Lobanov, V., Schultz, T., Verbeeck, R., Raghavan, N., Samtani, M.N., Novak, G., Narayan, V., DiBernardo, A.: Alzheimer’s disease neuroimaging initiative: quantifying the pathophysiological timeline of Alzheimer’s disease. J Alzheimers Dis. 26(4), 745–753 (2011)
Yee, T.W.: Quantile regression via vector generalized additive models. Stat. Med. 23(14), 2295–2315 (2004)
Young, A.L., Oxtoby, N.P., Daga, P., Cash, D.M., Fox, N.C., Ourselin, S., Schott, J.M., Alexander, D.C.: Alzheimer’s disease neuroimaging initiative: a data-driven model of biomarker changes in sporadic Alzheimer’s disease. Brain 137(Pt 9), 2564–2577 (2014)
Acknowledgements
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.
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Schmidt-Richberg, A. et al. (2015). Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_30
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DOI: https://doi.org/10.1007/978-3-319-19992-4_30
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