Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data

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DOI:
| Published | English
Authors:
Karlsson, Linda ; Vogel, Jacob ; Arvidsson, Ida ; Åström, Kalle ; Strandberg, Olof ; Seidlitz, Jakob ; Bethlehem, Richard A.I. ; Stomrud, Erik ; Ossenkoppele, Rik ; Ashton, Nicholas J. ; Zetterberg, Henrik ; Blennow, Kaj
All
Department:
LU Profile Area: Proactive Ageing
Clinical Memory Research
MultiPark: Multidisciplinary research focused on Parkinson's disease
Neurodegenerative research
SciLifeLab Site@Lund
Computer Vision and Machine Learning
LU Profile Area: Natural and Artificial Cognition
LTH Profile Area: AI and Digitalization
eSSENCE: The e-Science Collaboration
ELLIIT: the Linköping-Lund initiative on IT and mobile communication
LU Profile Area: Nature-based future solutions
LU Profile Area: Light and Materials
LTH Profile Area: Engineering Health
Stroke Imaging Research group
Mathematical Imaging Group
MR Physics
Regeneration in Movement Disorders
Neurology, Lund
WCMM-Wallenberg Centre for Molecular Medicine
Brain Injury After Cardiac Arrest
Research Group:
Clinical Memory Research
SciLifeLab Site@Lund
Computer Vision and Machine Learning
Stroke Imaging Research group
Mathematical Imaging Group
MR Physics
Regeneration in Movement Disorders
Brain Injury After Cardiac Arrest
Abstract:

INTRODUCTION: Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers. METHODS: We thoroughly investigated the ability of various machine learning models to predict clinically useful tau-PET composites (load and laterality index) from low-cost and non-invasive features, for example, clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). RESULTS: Models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared = 0.66–0.69), with especially high contribution from plasma phosphorylated tau-217 (p-tau217). MRI variables were the best predictors of asymmetric tau load between the two hemispheres (best model: R-squared = 0.28–0.42). The models showed high generalizability to external test cohorts with data collected at multiple sites. Through a proof-of-concept two-step classification workflow, we also demonstrated possible model translations to a clinical setting. DISCUSSION: This study highlights the promising and limiting aspects of using machine learning to predict tau-PET from scalable cost-effective variables, with findings relevant for clinical settings and future research. Highlights: Accessible variables showed potential in estimating tau tangle load and distribution. Plasma phosphorylated tau-217 (p-tau217) and magnetic resonance imaging (MRI) were the best predictors of different tau-PET (positron emission tomography) composites. Machine learning models demonstrated high generalizability across AD cohorts.

ISSN:
1552-5260
LUP-ID:
02b0084a-5b2c-4195-a9c7-50c07b7adc1b | Link: https://lup.lub.lu.se/record/02b0084a-5b2c-4195-a9c7-50c07b7adc1b | Statistics

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