Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques

Article type: Research Article

Authors: Ezzati, Ali^{a; b; *} | Harvey, Danielle J.^c | Habeck, Christian^d | Golzar, Ashkan^e | Qureshi, Irfan A.^{a; f} | Zammit, Andrea R.^a | Hyun, Jinshil^a | Truelove-Hill, Monica^f | Hall, Charles B.^e | Davatzikos, Christos^f | Lipton, Richard B.^{a; b} | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA | [b] Department of Neurology, Montefiore Medical Center, Bronx, NY, USA | [c] Department of Public Health Sciences, University of California-Davis, Davis, CA, USA | [d] Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA | [e] Element AI, Montreal, Quebec, QC, Canada | [f] Biohaven Pharmaceuticals, New Haven, CT, USA | [g] Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA

Correspondence: [*] Correspondence to: Ali Ezzati, MD, Albert Einstein College of Medicine, 1225 Morris Park Avenue, Bronx, NY, 10461, USA. Tel.: +1 718 430 3885; Fax: +1 718 430 3870; E-mail: aezzati@einstein.yu.edu.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Abstract: Background:Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer’s disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. Objective:The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. Methods:We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. Results: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. Conclusions:Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.

Keywords: Alzheimer’s disease, amyloid imaging, machine learning, mild cognitive impairment, predictive analytics

DOI: 10.3233/JAD-191038

Journal: Journal of Alzheimer's Disease, vol. 73, no. 3, pp. 1211-1219, 2020