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dc.contributor.authorMoscoso Rial, Alexis
dc.contributor.authorSilva Rodríguez, Jesús
dc.contributor.authorAldrey Vázquez, José Manuel
dc.contributor.authorCortés Hernández, Julia
dc.contributor.authorFernández Ferreiro, Anxo
dc.contributor.authorGómez Lado, Noemí
dc.contributor.authorRuibal Morell, Álvaro
dc.contributor.authorAguiar Fernández, Pablo
dc.date.accessioned2020-04-15T17:58:08Z
dc.date.available2020-04-15T17:58:08Z
dc.date.issued2019
dc.identifier.citationMoscoso, A., Silva-Rodríguez, J., Aldrey, J.M., Cortés, J. et al. (2019). Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models, "NeuroImage: Clinical", vol. 23, 101837
dc.identifier.urihttp://hdl.handle.net/10347/21445
dc.description.abstractMagnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of in-cipientAlzheimer'sDisease(AD)dementiainmildcognitiveimpairment(MCI).Focusedonprovidinganearlierand more accurate diagnosis, sophisticated MRI machine learning algorithms have been developed over therecentyears,mostofthemlearningtheirnon-diseasepatternsfromMCIthatremainedstableover2–3years.Inthis work, we analyzed whether these stable MCI over short-term periods are actually appropriate trainingexamples of non-disease patterns. To this aim, we compared the diagnosis of MCI patients at 2 and 5years offollow-up and investigated its impact on the predictive performance of baseline volumetric MRI measures pri-marily involved in AD, i.e., hippocampal and entorhinal cortex volumes. Predictive power was evaluated interms ofthe areaunder the ROCcurve(AUC), sensitivity,andspecificity inatrialsample of248 MCIpatientsfollowed-up over 5years. We further compared the sensitivity in those MCI that converted before 2years andthose that converted after 2years. Our results indicate that 23% of the stable MCI at 2years progressed in thenextthreeyearsandthatMRIvolumetricmeasuresaregoodpredictorsofconversiontoADdementiaevenatthemid-term, showing a better specificity and AUC as follow-up time increases. The combination of hippocampusand entorhinal cortex yielded an AUC that was significantly higher for the 5-year follow-up (AUC=73% at2yearsvs.AUC=84%at5years),aswellasforspecificity(56%vs.71%).Sensitivityshowedanon-significantslightdecrease(81%vs.78%).Remarkably,theperformanceofthismodelwascomparabletomachinelearningmodels at the same follow-up times. MRI correctly identified most of the patients that converted after 2years(with sensitivity>60%), and these patients showed a similar degree of abnormalities to those that convertedbefore 2years. This implies that most of the MCI patients that remained stable over short periods and subse-quentlyprogressedtoADdementiahadevidentatrophiesatbaseline.Therefore,machinelearningmodelsthatuse these patients to learn non-disease patterns are including an important fraction of patients with evidentpathological changes related to the disease, something that might result in reduced performance and lack ofbiological interpretability.
dc.description.sponsorshipThis work was partially supported by the project PI16/01416(ISCIIIco-fundedFEDER) and RYC-2015/17430 (RamónyCajal,Pablo Aguiar). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI)(National Institutes of Health Grant U01AG024904) and DODADNI (Department of Defense award number W81XWH-12-2-0012)
dc.language.isoeng
dc.publisherElsevier
dc.rights© 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLate MCI
dc.subjectMCI
dc.subjectAlzheimer
dc.subjectMachine learning
dc.subjectMRI
dc.titlePrediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models
dc.typeinfo:eu-repo/semantics/article
dc.identifier.DOI10.1016/j.nicl.2019.101837
dc.relation.publisherversionhttps://doi.org/10.1016/j.nicl.2019.101837
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.e-issn2213-1582
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Psiquiatría, Radioloxía, Saúde Pública, Enfermaría e Medicina
dc.description.peerreviewedSI


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© 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/)
Except where otherwise noted, this item's license is described as  © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/)





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