Deep learning‐based prediction of regional brain Tau PET burden from MRI

Abstract INTRODUCTION Regional tau is strongly associated with Alzheimer's disease (AD) symptoms, but tau positron emission tomography (PET) is not routinely available, limiting early risk stratification.

Methods

We trained a deep learning model to predict regional tau PET standardized uptake value ratios (SUVRs) from magnetic resonance imaging (MRI) with and without basic demographics using public cohorts (Alzheimer's Disease Neuroimaging Initiative [ADNI], Open Access Series of Imaging Studies 3 [OASIS3‐tau], Anti‐Amyloid Treatment in Asymptomatic Alzheimer's Disease Study [A4], Harvard Aging Brain Study [HABS]). Mean absolute error (MAE), Pearson r, area‐under‐the‐receiver‐operating‐curve (AUC), and comparison with cognitive testing were assessed in external test sets (SCAN, Stanford University).

Results

The model achieved an MAE of 0.14 (95% confidence interval [CI]: 0.12–0.16) and r = 0.72 for an early uptake region (Braak I‐IV), and an MAE of 0.14 (95% CI: 0.12–0.17) and r = 0.71 for a late uptake region (Braak V‐VI). AUCs for binary tau positivity in these regions were 0.87 (95% CI: 0.83–0.90) and 0.88 (95% CI: 0.84–0.91), respectively.

Discussion

Predicting regional tau burden from magnetic resonance imaging (MRI) is feasible and may enable scalable tau‐informed clinical AD care.