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

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 predictor

SUBMITTER: Karlsson L 

PROVIDER: S-EPMC11846480 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature