Predicting regional tau accumulation with machine learning-based tau-PET and advanced radiomics.

Introduction: Alzheimer's disease is partially characterized by the progressive accumulation of aggregated tau-containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir-positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.

Early tau detection in flortaucipir images: validation in autopsy-confirmed data and implications for disease progression.

Background: There is an increasing interest in utilizing tau PET to identify patients early in Alzheimer's disease (AD). In this work, a temporal lobe composite (Eτ) volume of interest (VOI) was evaluated in a longitudinal flortaucipir cohort and compared to a previously described global neocortical VOI. In a separate autopsy-confirmed study, the sensitivity of the Eτ VOI for identifying intermediate (B2) neurofibrillary tangle (NFT) pathology was evaluated.