dc.creator | Schmidt-Richberg, Alexander | es |
dc.creator | Guerrero, Ricardo | es |
dc.creator | Ledig, Christian | es |
dc.creator | Molina Abril, Helena | es |
dc.creator | Frangi, Alejandro F. | es |
dc.creator | Rueckert, Daniel | es |
dc.date.accessioned | 2020-02-26T11:01:58Z | |
dc.date.available | 2020-02-26T11:01:58Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Schmidt-Richberg, A., Guerrero, R., Ledig, C., Molina Abril, H., Frangi, A.F. y Rueckert, D. (2015). Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease. En IPMI 2015: 24th International Conference on Information Processing in Medical Imaging (387-398), Sabhal Mor Ostaig, Isle of Skye, UK: Springer. | |
dc.identifier.isbn | 978-3-319-19991-7 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/93658 | |
dc.description.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. | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | IPMI 2015: 24th International Conference on Information Processing in Medical Imaging (2015), p 387-398 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007%2F978-3-319-19992-4_30 | es |
dc.identifier.doi | 10.1007/978-3-319-19992-4_30 | es |
idus.format.extent | 12 | es |
dc.publication.initialPage | 387 | es |
dc.publication.endPage | 398 | es |
dc.eventtitle | IPMI 2015: 24th International Conference on Information Processing in Medical Imaging | es |
dc.eventinstitution | Sabhal Mor Ostaig, Isle of Skye, UK | es |
dc.relation.publicationplace | Cham, Switzerland | es |