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Updated: Sep 17, 2025
Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
Published on: April 18, 2025
Deep Learning-Based Prediction of PET Amyloid Status Using MRI
Donghoon Kim1, Jon André Ottesen1,2, Ashwin Kumar1
1From the Department of Radiology (D.K., J.A.O., A.K., B.C.H., E.B., G.Z.), Stanford University, Stanford, California.
Background And Purpose:
Identifying amyloid-beta (Aβ)-positive patients is essential for Alzheimer disease clinical trials and disease-modifying treatments but currently requires PET or CSF sampling. Previous MRI-based deep learning models using only T1-weighted (T1w) images have shown moderate performance.
Materials And Methods:
Multicontrast MRI- and PET-based quantitative Aβ deposition were retrospectively obtained from 3 public data sets: ADNI, OASIS3, and A4. Aβ positivity was defined using the recommended Centiloid threshold of each data set. Two EfficientNet models were trained to predict amyloid-positivity: one by using only T1w images and another incorporating both T1w and T2 FLAIR. Model performance was assessed using an internal held-out test set, evaluating area under the curve (AUC), accuracy, sensitivity, and specificity. External validation was conducted using an independent cohort from Stanford Alzheimer Disease Research Center. DeLong and McNemar tests were used to compare AUC and accuracy, respectively.
Results:
A total of 4056 examinations (mean age: 71.6 [SD, 6.3] years; 55% female; 55% amyloid-positive) were used for network development, and 149 examinations were used for external testing (mean age: 72.1 [SD] 9.6] years; 57% female; 56% amyloid-positive). The multicontrast model outperformed the single-technique model in the internal held-out test set (AUC: 0.67; 95% CI, 0.65-0.70; P < .001; accuracy: 0.63; 95% CI, 0.62-0.65; P < .001) compared with the T1w-only model (AUC: 0.61; accuracy: 0.59). Among cognitive subgroups, the highest performance (AUC: 0.71) was observed in mild cognitive impairment. The multicontrast model also demonstrated consistent performance in the external test set (AUC: 0.65; 95% CI, 0.60-0.71; P = .014; accuracy: 0.62; 95% CI, 0.58-0.65; P < .001).
Conclusions:
The use of multicontrast MRI, specifically incorporating T2 FLAIR in addition to T1w images, significantly improved the predictive accuracy of PET-determined amyloid status from MRIs by using a deep learning approach.
Insights
This study shows that using multi-contrast MRI scans, including T1-weighted and T2-FLAIR images, significantly improves deep learning models for predicting amyloid-beta status in Alzheimer's disease. This advancement offers a more accessible method for patient identification in clinical trials.
Area of Science:
- Neuroimaging
- Artificial Intelligence in Medicine
- Alzheimer's Disease Diagnostics
Background:
- Identifying amyloid-beta (Aβ) positivity is crucial for Alzheimer's disease (AD) clinical trials and treatments.
- Current methods like PET scans or cerebrospinal fluid sampling are invasive or costly.
- Previous MRI-based deep learning models using only T1-weighted (T1w) images showed limited performance.
Purpose of the Study:
- To develop and evaluate a deep learning model for predicting Aβ positivity using multi-contrast MRI.
- To compare the performance of a model using T1w images alone versus one using T1w and T2-FLAIR images.
Main Methods:
- Retrospective analysis of multi-contrast MRI and PET data from three public datasets (ADNI, OASIS3, A4).
- Training two EfficientNet models: one with T1w images only, and another with T1w and T2-FLAIR images.
- Performance assessment using AUC, accuracy, sensitivity, and specificity on internal and external validation cohorts.
Main Results:
- The multi-contrast model (T1w + T2-FLAIR) significantly outperformed the T1w-only model in internal testing (AUC: 0.67 vs. 0.61, accuracy: 0.63 vs. 0.59).
- The highest performance (AUC: 0.71) was observed in patients with mild cognitive impairment (MCI).
- Consistent performance was demonstrated in the external validation set (AUC: 0.65, accuracy: 0.62).
Conclusions:
- Incorporating T2-FLAIR images alongside T1w images in a deep learning approach significantly enhances the prediction of PET-determined amyloid status from MRI.
- This multi-contrast MRI-based deep learning method offers a promising, non-invasive alternative for identifying amyloid positivity in Alzheimer's disease research.
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