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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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-00743-7 |
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| author | Dong Hyeok Choi So Hyun Ahn Yujin Chung Jin Sung Kim Jee Hyang Jeong Hai-Jeon Yoon |
| author_facet | Dong Hyeok Choi So Hyun Ahn Yujin Chung Jin Sung Kim Jee Hyang Jeong Hai-Jeon Yoon |
| author_sort | Dong Hyeok Choi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-94eedfb69d4b4b2e99f31eea356d6540 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-94eedfb69d4b4b2e99f31eea356d65402025-08-20T03:38:11ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-00743-7Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical featuresDong Hyeok Choi0So Hyun Ahn1Yujin Chung2Jin Sung Kim3Jee Hyang Jeong4Hai-Jeon Yoon5Department of Medicine, Yonsei University College of MedicineEwha Medical Research Institute, School of Medicine, Ewha Womans UniversityDepartment of Medicine, Warren Alpert Medical School, Brown UniversityDepartment of Medicine, Yonsei University College of MedicineDepartment of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of MedicineDepartment of Nuclear Medicine, Ewha Womans University College of MedicineAbstract 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.https://doi.org/10.1038/s41598-025-00743-718F-FlorbetabenPositron emission tomographyEarly-phaseAmyloid-βCognitive status |
| spellingShingle | Dong Hyeok Choi So Hyun Ahn Yujin Chung Jin Sung Kim Jee Hyang Jeong Hai-Jeon Yoon Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features Scientific Reports 18F-Florbetaben Positron emission tomography Early-phase Amyloid-β Cognitive status |
| title | Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features |
| title_full | Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features |
| title_fullStr | Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features |
| title_full_unstemmed | Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features |
| title_short | Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features |
| title_sort | machine learning model for predicting amyloid β positivity and cognitive status using early phase 18f florbetaben pet and clinical features |
| topic | 18F-Florbetaben Positron emission tomography Early-phase Amyloid-β Cognitive status |
| url | https://doi.org/10.1038/s41598-025-00743-7 |
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