Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features

Topic: Amyloid, tau and blood-test diagnosis

This study developed machine learning models to predict Aβ positivity in Alzheimer’s disease by integrating early-phase 18 F-Florbetaben PET and clinical data to improve diagnostic accuracy. Furthermore, the study explored machine learning models to predict cognitive status from early-phase PET, maximizing the clinical utility of dual-phase imaging protocols. 176 subjects who completed dual-phase 18 F-FBB PET scanning including 38 with normal cognition, 94 with mild cognitive impairment, and 44 with dementia were enrolled. Aβ status was determined from delayed-phase 18 F-FBB PET scans (90–110 min post-injection). To develop a machine learning model for predicting Aβ positivity, we utilized early-phase PET and clinical features. From early-phase 18 F-FBB PET scans (0–10 min post-injection), we extracted brain region-specific standardized uptake value ratios (SUVR) as imaging features. Various classifiers, including Random Forest, Gradient Boosting, and XGBoost, were trained and evaluated using accuracy, ROC AUC, recall, and F1 scores. Feature importance was assessed to identify key predictors, and the importance of features that most significantly influenced each model’s results was calculated. The early-phase PET alone showed moderate performance (80.56% accuracy with Random Forest), with hippocampus (importance: 0.086), isthmus of cingulate (0.051), and entorhinal (0.038) SUVR values as top predictors. The combined PET and clinical data model achieved the highest accuracy (88.89%) using Gradient Boosting, with key predictors including APOE genotype (importance: 0.2485), Medial Orbitofrontal SUVR (0.0996), and hippocampal SUVR (0.0663). In predicting cognitive status using early-phase PET, most classifiers achieved high accuracy (> 80%) and F1 scores (0.82–0.90), with Decision Tree showing the highest accuracy of 83.33%. Machine learning models combining PET and clinical data demonstrated superior predictive accuracy for Aβ positivity prediction, while early-phase PET alone showed robust performance in predicting cognitive status, highlighting the synergistic potential of multimodal data and versatile utility of early-phase PET imaging.

Key findings

• Feature importance was assessed to identify key predictors, and the importance of features that most significantly influenced each model’s results was calculated.
• Machine learning models combining PET and clinical data demonstrated superior predictive accuracy for Aβ positivity prediction, while early-phase PET alone showed robust performance in predicting cognitive status, highlighting the synergistic potential of multimodal data and versatile utility of early-phase PET imaging.

Why it matters

This study developed machine learning models combining early-phase PET and clinical data to predict amyloid-beta positivity and cognitive status. It is a diagnostic approach only and does not test any treatment, cure or prevention.

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Study details

• Participants176
• Effect size80.56%; 88.89%; 83.33%
• Evidence strengthLow

Read the original

• DOI: 10.1038/s41598-025-00743-7
• PubMed: 40594040
• Original source →
• Free full text: Scientific Reports →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{choi2025-40594040,
  author  = {Choi, Dong Hyeok and Ahn, So Hyun and Chung, Yujin and Kim, Jin Sung and Jeong, Jee Hyang and Yoon, Hai‐Jeon},
  title   = {{Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features}},
  journal = {Scientific Reports},
  year    = {2025},
  doi     = {10.1038/s41598-025-00743-7},
  pmid    = {40594040},
  url     = {https://doi.org/10.1038/s41598-025-00743-7}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Cerebral amyloid-β PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicentre phase 2 diagnostic study The Lancet. Neurology · 2011-04-08 · usefulness score 70/100 · Getting a diagnosis
• Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade The Lancet Neurology · 2009-12-15 · usefulness score 64/100 · Biomarkers
• NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease Alzheimer's & dementia : the journal of the Alzheimer's Association · 2018-04-01 · usefulness score 64/100 · Getting a diagnosis
• Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer's disease dementia, mild cognitive impairment, and normal aging Alzheimer's & dementia : the journal of the Alzheimer's Association · 2013-01-30 · usefulness score 63/100 · Caregiving
• Imaging of amyloid beta in Alzheimer's disease with 18F-BAY94-9172, a novel PET tracer: proof of mechanism The Lancet. Neurology · 2008-01-10 · usefulness score 59/100 · Getting a diagnosis
• Phosphorylated Tau in Alzheimer's Disease and Other Tauopathies International journal of molecular sciences · 2022-10-25 · usefulness score 56/100 · Lewy body dementia
• Neuroimaging predictors of brain amyloidosis in mild cognitive impairment Annals of neurology · 2013-08-01 · usefulness score 52/100 · Biomarkers

Later studies that cite this one

1 study in the alzhub library lists this one in its references.

• Correlation and Interchangeability of Amyloid, Tau, and Glucose Metabolism PET in Mild Cognitive Impairment and Alzheimer: A Review Brain Sciences · 2025-11-26 · usefulness score 37/100 · Biomarkers

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• Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer's disease Archives of neurology · 1993-09-01 · usefulness score 50/100 · Vascular contributions
• A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks Journal of medical systems · 2019-12-18 · usefulness score 64/100 · Long-term cohort studies
• Hippocampal CA1 apical neuropil atrophy in mild Alzheimer disease visualized with 7-T MRI Neurology · 2010-10-01 · usefulness score 58/100 · Brain scans and imaging

Similar studies

• Determining Amyloid-β Positivity Using ¹⁸F-AZD4694 PET Imaging 2021 · Biomarkers
• Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer's disease: Phase 3 study 2015 · Brain scans and imaging
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• Visual Versus Fully Automated Analyses of 18F-FDG and Amyloid PET for Prediction of Dementia Due to Alzheimer Disease in Mild Cognitive Impairment 2016 · Biomarkers
• Defining the Lowest Threshold for Amyloid-PET to Predict Future Cognitive Decline and Amyloid Accumulation 2021 · Biomarkers
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