Deep learning-based prediction of regional brain Tau PET burden from MRI.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology, Stanford University, Stanford, California, USA.
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA.
- Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA.
Abstract
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. 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). 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. Predicting regional tau burden from magnetic resonance imaging (MRI) is feasible and may enable scalable tau-informed clinical AD care.