Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

Autor:	Zhuang X; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA.; Interdisciplinary Neuroscience PhD Program, University of Nevada, Las Vegas, NV, USA.; Laboratory of Neurogenetics and Precision Medicine, University of Nevada, Las Vegas, NV, USA., Cordes D; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA.; University of Colorado Boulder, Boulder, CO, USA., Bender AR; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA., Nandy R; Department of Biostatistics and Epidemiology, School of Public Health, University of North Texas Health Science Center, Fort Worth, TX, USA., Oh EC; Interdisciplinary Neuroscience PhD Program, University of Nevada, Las Vegas, NV, USA.; Laboratory of Neurogenetics and Precision Medicine, University of Nevada, Las Vegas, NV, USA.; Department of Internal Medicine, School of Medicine, University of Nevada, Las Vegas, NV, USA., Kinney J; Interdisciplinary Neuroscience PhD Program, University of Nevada, Las Vegas, NV, USA.; Department of Brain Health, Chambers-Grundy Center for Transformative Neuroscience, School of Integrated Health Sciences, University of Nevada, Las Vegas, NV, USA., Caldwell JZK; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA., Cummings J; Department of Brain Health, Chambers-Grundy Center for Transformative Neuroscience, School of Integrated Health Sciences, University of Nevada, Las Vegas, NV, USA., Miller J; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA.
Jazyk:	angličtina
Zdroj:	Journal of Alzheimer's disease : JAD [J Alzheimers Dis] 2024; Vol. 100 (3), pp. 843-862.
DOI:	10.3233/JAD-231321
Abstrakt:	Background: Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack validation against postmortem AD neuropathological data. Objective: Train machine learning models to classify the presence or absence of autopsy-confirmed severe AD neuropathology using clinically available features. Methods: AD neuropathological status are assessed at postmortem for participants from the National Alzheimer's Coordinating Center (NACC). Clinically available features are utilized, including demographics, Apolipoprotein E(APOE) genotype, and cortical thicknesses derived from ante-mortem MRI scans encompassing AD meta regions of interest (meta-ROI). Both logistic regression and random forest models are trained to identify linearly and nonlinearly separable features between participants with the presence (N = 91, age-at-MRI = 73.6±9.24, 38 women) or absence (N = 53, age-at-MRI = 68.93±19.69, 24 women) of severe AD neuropathology. The trained models are further validated in an external data set against in vivo amyloid biomarkers derived from PET imaging (amyloid-positive: N = 71, age-at-MRI = 74.17±6.37, 26 women; amyloid-negative: N = 73, age-at-MRI = 71.59±6.80, 41 women). Results: Our models achieve a cross-validation accuracy of 84.03% in classifying the presence or absence of severe AD neuropathology, and an external-validation accuracy of 70.14% in classifying in vivo amyloid positivity status. Conclusions: Our models show that clinically accessible features, including APOE genotype and cortical thinning encompassing AD meta-ROIs, are able to classify both postmortem confirmed AD neuropathological status and in vivo amyloid status with reasonable accuracies. These results suggest the potential utility of AD meta-ROIs in determining AD neuropathological status in living persons.
Databáze:	MEDLINE
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