{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:51:05Z","timestamp":1790704265279,"version":"4.1.0"},"reference-count":77,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2021,3,9]]},"abstract":"<jats:p>Background: Amyloid-\u03b2 (A\u03b2) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer\u2019s disease. However, A\u03b2 evaluation through A\u03b2 positron emission tomography (PET) is limited due to high cost and safety issues. Objective: We therefore aimed to develop and validate prediction models of A\u03b2 positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers. Methods: We recruited 529 aMCI patients from multiple centers who underwent A\u03b2 PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers). Results: Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of A\u03b2 positivity. Conclusion: Our results suggest that ML models are effective in predicting A\u03b2 positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.<\/jats:p>","DOI":"10.3233\/jad-201092","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T12:44:22Z","timestamp":1611665062000},"page":"143-157","source":"Crossref","is-referenced-by-count":26,"title":["Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment"],"prefix":"10.1177","volume":"80","author":[{"given":"Sung Hoon","family":"Kang","sequence":"first","affiliation":[{"name":"Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"},{"name":"Neuroscience Center, Samsung Medical Center, Seoul, Korea"},{"name":"Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo Kyoung","family":"Cheon","sequence":"additional","affiliation":[{"name":"Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"},{"name":"Neuroscience Center, Samsung Medical Center, Seoul, Korea"},{"name":"Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-Sun","family":"Kim","sequence":"additional","affiliation":[{"name":"Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"},{"name":"Neuroscience Center, Samsung Medical Center, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyemin","family":"Jang","sequence":"additional","affiliation":[{"name":"Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"},{"name":"Neuroscience Center, Samsung Medical Center, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hee Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"},{"name":"Neuroscience Center, Samsung Medical Center, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung Won","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Neurology, Dong-A University Medical Center, Dong-A University College of Medicine, Busan, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young","family":"Noh","sequence":"additional","affiliation":[{"name":"Department of Neurology, Gachon University Gil Medical Center, Incheon, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin San","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Neurology, Kyung Hee University Hospital, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byoung Seok","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Neurology, Severance hospital, Yonsei University School of Medicine, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duk L.","family":"Na","sequence":"additional","affiliation":[{"name":"Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"},{"name":"Neuroscience Center, Samsung Medical Center, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyejoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"},{"name":"Neuroscience Center, Samsung Medical Center, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Won","family":"Seo","sequence":"additional","affiliation":[{"name":"Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"},{"name":"Neuroscience Center, Samsung Medical Center, Seoul, Korea"},{"name":"Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea"},{"name":"Samsung Alzheimer Research Center and Center for Clinical Epidemiology Medical Center, Seoul, Korea"},{"name":"Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-201092_ref1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1001\/archneur.56.3.303","article-title":"Mild cognitive impairment: Clinical characterization and outcome","volume":"56","author":"Petersen","year":"1999","journal-title":"Arch 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optimization","volume":"18","author":"Li","year":"2017","journal-title":"J Mach Learn Res"},{"key":"10.3233\/JAD-201092_ref43","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models: A framework for some traditional and novel measures","volume":"21","author":"Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"10.3233\/JAD-201092_ref44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","article-title":"Verification of forecasts expressed in terms of probability","volume":"78","author":"Brier","year":"1950","journal-title":"Mon Weather Rev"},{"key":"10.3233\/JAD-201092_ref45","first-page":"19","article-title":"A simulator for objectively evaluating prospective drivers of the van Scott","volume":"10","author":"Hogan","year":"1982","journal-title":"Bull Prosthet 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