Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features
Abstract This study developed machine learning models to predict Aβ positivity in Alzheimer’s disease by integrating early-phase 18F-Florbetaben PET and clinical data to improve diagnostic accuracy. Furthermore, the study explored machine learning models to predict cognitive status from early-phase...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-00743-7 |
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| Summary: | Abstract This study developed machine learning models to predict Aβ positivity in Alzheimer’s disease by integrating early-phase 18F-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 18F-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 18F-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 18F-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. |
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| ISSN: | 2045-2322 |