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Updated: Sep 2, 2026
Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
Published on: April 18, 2025
Machine learning for the prediction of amyloid PET positivity using plasma biomarkers, cognition, APOE genotype, and
Thony Yan Liang1, Xueting Cui1, Micheal Adeyosoye1
1Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
Purpose:
Machine learning to enable precise, non-invasive detection of cerebral amyloid-beta (Aβ) pathology by integrating cognitive assessments, plasma biomarkers, and structural neuroimaging.
Methods:
We developed an explainable multimodal machine-learning framework to predict amyloid PET visual read status using plasma biomarkers, cognitive assessments, APOE genotype, demographic variables, and structural MRI measurements. The development cohort consisted of 170 participants from the 1Florida Alzheimer's Disease Research Center (ADRC) and 129 participants from the ADNI4 cohort. Machine-learning pipelines were evaluated using combinations of five classifiers and multiple feature-selection approaches with Bayesian hyperparameter optimization and stratified five-fold cross-validation. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).
Results:
Ensemble methods consistently outperformed linear and distance-based classifiers. The optimal pipeline, XGBoost with mutual-information feature selection, achieved a mean AUC of 0.891 ± 0.048 and a recall of 0.84. SHAP analyses identified the plasma p-tau217/Aβ42 ratio as the most influential predictor, followed by plasma p-tau217, p-tau181, MMSE, and APOE ε4 status. Structural MRI variables provided complementary predictive information. ADNI4 evaluation yielded a mean AUC of 0.545, while training on ADRC and testing on ADNI4 achieved an AUC of 0.636 and an overall accuracy of 63%. Importantly, plasma p-tau217/Aβ42 and p-tau217 remained the dominant predictors across cohorts.
Conclusion:
Multimodal machine learning can predict amyloid PET status while providing biologically interpretable explanations. Plasma tau and amyloid-related biomarkers carried the strongest predictive signal, while cognition, APOE genotype, and MRI refined classification decisions. External validation highlighted the challenges of cohort heterogeneity and domain shift but demonstrated preservation of biologically meaningful biomarker relationships.