{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T09:38:37Z","timestamp":1789983517783,"version":"4.0.1"},"reference-count":76,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2015,11,23]],"date-time":"2015-11-23T00:00:00Z","timestamp":1448236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2016,1]]},"abstract":"<jats:p>\n                    A variety of imaging, neuropsychological, and genetic biomarkers have been suggested as potential biomarkers for the identification of mild cognitive impairment (MCI) in patients who later develop Alzheimer\u2019s disease (AD). Here, we systematically evaluated the most promising combinations of these biomarkers regarding discrimination between stable and converter MCI and reflection of disease staging. Alzheimer\u2019s Disease Neuroimaging Initiative data of AD (\n                    <jats:italic>n<\/jats:italic>\n                    \u200a=\u200a144), controls (\n                    <jats:italic>n<\/jats:italic>\n                    \u200a=\u200a112), stable (\n                    <jats:italic>n<\/jats:italic>\n                    \u200a=\u200a265) and converter (\n                    <jats:italic>n<\/jats:italic>\n                    \u200a=\u200a177) MCI, for which apolipoprotein E status, neuropsychological evaluation, and structural, glucose, and amyloid imaging were available, were included in this study. Na\u00efve Bayes classifiers were built on AD and controls data for all possible combinations of these biomarkers, with and without stratification by amyloid status. All classifiers were then applied to the MCI cohorts. We obtained an accuracy of 76% for discrimination between converter and stable MCI with glucose positron emission tomography as a single biomarker. This accuracy increased to about 87% when including further imaging modalities and genetic information. We also identified several biomarker combinations as strong predictors of time to conversion. Use of amyloid validated training data resulted in increased sensitivities and decreased specificities for differentiation between stable and converter MCI when amyloid was included as a biomarker but not for other classifier combinations. Our results indicate that fully independent classifiers built only on AD and controls data and combining imaging, genetic, and\/or neuropsychological biomarkers can more reliably discriminate between stable and converter MCI than single modality classifiers. Several biomarker combinations are identified as strongly predictive for the time to conversion to AD.\n                  <\/jats:p>","DOI":"10.3233\/jad-150570","type":"journal-article","created":{"date-parts":[[2015,12,29]],"date-time":"2015-12-29T09:23:47Z","timestamp":1451381027000},"page":"1143-1159","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":63,"title":["Accurate Prediction of Conversion to Alzheimer\u2019s Disease using Imaging, Genetic, and Neuropsychological Biomarkers"],"prefix":"10.1177","volume":"49","author":[{"given":"Juergen","family":"Dukart","sequence":"first","affiliation":[{"name":"F. Hoffmann-La Roche, Roche Innovation Centre Basel, Basel, Switzerland"},{"name":"Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Sambataro","sequence":"additional","affiliation":[{"name":"F. Hoffmann-La Roche, Roche Innovation Centre Basel, Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Bertolino","sequence":"additional","affiliation":[{"name":"F. 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