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Peer-reviewedCC BY-NC-NDAlzheimer's & dementia : the journal of the Alzheimer's Association

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

  • Donghoon Kim1
  • Brandon C Ho1
  • Henk van Voorst1
  • Yongkai Liu1
  • Tammie L S Benzinger2
  • Sharon Sha3
  • Elizabeth Mormino3
  • Victor W Henderson4
  • Greg Zaharchuk1
4 affiliations
  1. 1Department of Radiology, Stanford University, Stanford, California, USA.
  2. 2Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
  3. 3Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA.
  4. 4Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA.
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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.

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Cite

BibTeXRIS
BibTeX
@article{kim2026deep,
  title = {{Deep learning-based prediction of regional brain Tau PET burden from MRI.}},
  author = {Donghoon Kim and Brandon C Ho and Henk van Voorst and Yongkai Liu and Tammie L S Benzinger and Sharon Sha and Elizabeth Mormino and Victor W Henderson and Greg Zaharchuk},
  journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
  year = {2026},
  month = oct,
  doi = {10.1002/alz.71911},
  url = {https://europepmc.org/article/MED/42963328},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Deep learning-based prediction of regional brain Tau PET burden from MRI.
AU  - Donghoon Kim
AU  - Brandon C Ho
AU  - Henk van Voorst
AU  - Yongkai Liu
AU  - Tammie L S Benzinger
AU  - Sharon Sha
AU  - Elizabeth Mormino
AU  - Victor W Henderson
AU  - Greg Zaharchuk
PY  - 2026
DA  - 2026/10/01
JO  - Alzheimer's & dementia : the journal of the Alzheimer's Association
DO  - 10.1002/alz.71911
UR  - https://europepmc.org/article/MED/42963328
AB  - 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.
SN  - 1552-5260
SN  - 1552-5279
ER  -