Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment
DC Field | Value | Language |
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dc.contributor.author | Kang, Sung Hoon | - |
dc.contributor.author | Cheon, Bo Kyoung | - |
dc.contributor.author | Kim, Ji-Sun | - |
dc.contributor.author | Jang, Hyemin | - |
dc.contributor.author | Kim, Hee Jin | - |
dc.contributor.author | Park, Kyung Won | - |
dc.contributor.author | Noh, Young | - |
dc.contributor.author | San Lee, Jin | - |
dc.contributor.author | Ye, Byoung Seok | - |
dc.contributor.author | Na, Duk L. | - |
dc.contributor.author | Lee, Hyejoo | - |
dc.contributor.author | Seo, Sang Won | - |
dc.date.accessioned | 2021-12-08T05:41:37Z | - |
dc.date.available | 2021-12-08T05:41:37Z | - |
dc.date.created | 2021-08-30 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1387-2877 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/130257 | - |
dc.description.abstract | Background: Amyloid-beta (A beta) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, A beta evaluation through A beta 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 beta positivity foraMCIusing optimal interpretable machine learning (ML) approaches utilizing multimodal markers. Methods: We recruited 529 aMCI patients from multiple centers who underwent A beta 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 crossvalidation, and fair accuracy (AUROC0.765) 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 beta positivity. Conclusion: Our results suggest that ML models are effective in predicting A beta positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IOS PRESS | - |
dc.subject | ALZHEIMERS ASSOCIATION WORKGROUPS | - |
dc.subject | TEMPORAL-LOBE ATROPHY | - |
dc.subject | DIAGNOSTIC GUIDELINES | - |
dc.subject | NATIONAL INSTITUTE | - |
dc.subject | CORTICAL THICKNESS | - |
dc.subject | CLINICAL-TRIALS | - |
dc.subject | DISEASE | - |
dc.subject | BETA | - |
dc.subject | PET | - |
dc.subject | DEMENTIA | - |
dc.title | Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kang, Sung Hoon | - |
dc.identifier.doi | 10.3233/JAD-201092 | - |
dc.identifier.scopusid | 2-s2.0-85102965252 | - |
dc.identifier.wosid | 000627617100011 | - |
dc.identifier.bibliographicCitation | JOURNAL OF ALZHEIMERS DISEASE, v.80, no.1, pp.143 - 157 | - |
dc.relation.isPartOf | JOURNAL OF ALZHEIMERS DISEASE | - |
dc.citation.title | JOURNAL OF ALZHEIMERS DISEASE | - |
dc.citation.volume | 80 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 143 | - |
dc.citation.endPage | 157 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.subject.keywordPlus | ALZHEIMERS ASSOCIATION WORKGROUPS | - |
dc.subject.keywordPlus | TEMPORAL-LOBE ATROPHY | - |
dc.subject.keywordPlus | DIAGNOSTIC GUIDELINES | - |
dc.subject.keywordPlus | NATIONAL INSTITUTE | - |
dc.subject.keywordPlus | CORTICAL THICKNESS | - |
dc.subject.keywordPlus | CLINICAL-TRIALS | - |
dc.subject.keywordPlus | DISEASE | - |
dc.subject.keywordPlus | BETA | - |
dc.subject.keywordPlus | PET | - |
dc.subject.keywordPlus | DEMENTIA | - |
dc.subject.keywordAuthor | A beta PET | - |
dc.subject.keywordAuthor | amnestic mild cognitive impairment | - |
dc.subject.keywordAuthor | A beta positivity | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | magnetic resonance imaging features | - |
dc.subject.keywordAuthor | neuropsychological tests | - |
dc.subject.keywordAuthor | prediction model | - |
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