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| 024 | 7 | _ | |a 10.1007/s00259-022-05879-6 |2 doi |
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| 100 | 1 | _ | |a Doering, E. |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment |
| 260 | _ | _ | |a Heidelberg [u.a.] |c 2022 |b Springer-Verl. |
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| 520 | _ | _ | |a 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 18F-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 18F-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, 18F-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.Keywords: Machine learning; Neurodegeneration. |
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| 700 | 1 | _ | |a Hönig, Merle |0 P:(DE-Juel1)178642 |b 1 |
| 700 | 1 | _ | |a Bischof, G. N. |0 P:(DE-Juel1)166265 |b 2 |
| 700 | 1 | _ | |a Bohn, K. P. |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Ellingsen, L. M. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a van Eimeren, T. |0 P:(DE-Juel1)169110 |b 5 |
| 700 | 1 | _ | |a Drzezga, A. |0 P:(DE-Juel1)177611 |b 6 |
| 773 | _ | _ | |a 10.1007/s00259-022-05879-6 |g Vol. 49, no. 13, p. 4478 - 4489 |0 PERI:(DE-600)2098375-X |n 13 |p 4478 - 4489 |t European journal of nuclear medicine and molecular imaging |v 49 |y 2022 |x 1619-7070 |
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