Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment

Background In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer’s disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may ther...

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Published in:	European journal of nuclear medicine and molecular imaging Vol. 49; no. 13; pp. 4478 - 4489
Main Authors:	Doering, E., Hoenig, M. C., Bischof, G. N., Bohn, K. P., Ellingsen, L. M., van Eimeren, T., Drzezga, A.
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-11-2022 Springer Nature B.V
Subjects:	Age Algorithms Alzheimer Disease - diagnostic imaging Alzheimer Disease - genetics Alzheimer's disease Amyloid Amyloid beta-Peptides Amyloidosis Apolipoprotein E Apolipoprotein E4 Apolipoprotein E4 - genetics Biomarkers Cardiology Classifiers Clinical trials Cognitive ability Cognitive Dysfunction - diagnostic imaging Dementia Dementia disorders Fluorodeoxyglucose F18 Gatekeeping Genotypes Humans Imaging Impairment Learning algorithms Machine learning Medicine Medicine & Public Health Neurodegenerative diseases Nuclear Medicine Oncology Original Original Article Orthopedics Patients Positron emission tomography Radiology Reproducibility of Results Risk analysis Risk factors Sex β-Amyloid Neurodegeneration Machine learning
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Summary:	Background In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer’s disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning–based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype 18 F-FDG PET, age, and sex. Methods Three hundred and forty-two MCI patients were used to train different machine learning classifiers to predict A-status majority classes among APOE-ε4 non-carriers (APOE4-nc; majority class: amyloid negative (Aβ-)) and carriers (APOE4-c; majority class: amyloid positive (Aβ +)) from 18 F-FDG-PET, age, and sex. Classifiers were tested on two different datasets. Finally, frequencies of progression to dementia were compared between gold standard and predicted A-status. Results Aβ- in APOE4-nc and Aβ + in APOE4-c were predicted with a precision of 87% and a recall of 79% and 51%, respectively. Predicted A-status and gold standard A-status were at least equally indicative of risk of progression to dementia. Conclusion We developed an algorithm allowing approximation of A-status in MCI with good reliability using APOE-genotype, 18 F-FDG PET, age, and sex information. The algorithm could enable better estimation of individual risk for developing AD based on existing biomarker information, and support efficient selection of patients who would benefit most from further etiological clarification. Further potential utility in clinical routine and clinical trials is discussed.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1619-7070 1619-7089
DOI:	10.1007/s00259-022-05879-6