{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T16:33:03Z","timestamp":1789749183360,"version":"4.0.1"},"reference-count":39,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2019,12,26]],"date-time":"2019-12-26T00:00:00Z","timestamp":1577318400000},"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":[[2020,2,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Amyloid-\u03b2 positivity (A\u03b2+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer\u2019s disease (AD), particularly in trials for amyloid-targeted therapy. Predicting A\u03b2 positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of A\u03b2+ based on gold-standard of PET imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of A\u03b2 status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3233\/jad-191038","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T11:08:26Z","timestamp":1577444906000},"page":"1211-1219","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":36,"title":["Predicting Amyloid-\u03b2 Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques"],"prefix":"10.1177","volume":"73","author":[{"given":"Ali","family":"Ezzati","sequence":"first","affiliation":[{"name":"Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA"},{"name":"Department of Neurology, Montefiore Medical Center, Bronx, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle J.","family":"Harvey","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, University of California-Davis, Davis, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Habeck","sequence":"additional","affiliation":[{"name":"Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashkan","family":"Golzar","sequence":"additional","affiliation":[{"name":"Element AI, Montreal, Quebec, QC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irfan A.","family":"Qureshi","sequence":"additional","affiliation":[{"name":"Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA"},{"name":"Biohaven Pharmaceuticals, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea R.","family":"Zammit","sequence":"additional","affiliation":[{"name":"Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinshil","family":"Hyun","sequence":"additional","affiliation":[{"name":"Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Truelove-Hill","sequence":"additional","affiliation":[{"name":"Biohaven Pharmaceuticals, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles B.","family":"Hall","sequence":"additional","affiliation":[{"name":"Element AI, Montreal, Quebec, QC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Davatzikos","sequence":"additional","affiliation":[{"name":"Biohaven Pharmaceuticals, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard B.","family":"Lipton","sequence":"additional","affiliation":[{"name":"Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA"},{"name":"Department of Neurology, Montefiore Medical Center, Bronx, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging 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