Amyloid-beta statuses prediction with free water MR imaging features in Alzheimer's disease using machine learning models.
Authors
Affiliations (11)
Affiliations (11)
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
- Department of Neurology, The Second Medical Centre, National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
- Medical School of Chinese PLA, Beijing, China.
- Cognitive Science and Allied Health School, Beijing Language and Culture University, Beijing, China.
- Department of Radiology & Precision and Intelligence Medical Imaging Lab, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
- Population Health Sciences, German Centre for Neurodegenerative Diseases, Bonn, Germany.
- Institute of Geriatrics, The Second Medical Centre, National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
- Department of Radiology, The Second Medical Centre, National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China. [email protected].
- Institute of Geriatrics, The Second Medical Centre, National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China. [email protected].
- School of Biomedical Engineering, Capital Medical University, Beijing, China. [email protected].
Abstract
This study aimed to identify the effectiveness of free water MRI (FW-MRI) features for predicting amyloid-beta (Aβ) statuses in Alzheimer’s disease (AD) by constructing diagnostic models using machine learning analysis. This study retrospectively included 96 patients of mild cognitive impairment (MCI) and AD (69 Aβ-positive and 27 Aβ-negative). Clinical characteristics, FW-corrected and standard diffusion indices, and structural MRI indices were collected. Three supervised machine learning algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were adopted to construct a diagnostic model for distinguishing Aβ deposition in AD. SHapley Additive exPlanation (SHAP) value was used as an interpretable algorithm to identify influential characteristics based on the best-performing model. In the single-modality model, FW-DTI achieved better classification performance than conventional DTI, which obtained accuracies all above 80% among three machine learning approaches on the internal dataset (RF = 0.800, SVM = 0.867, XGB = 0.800). In the multi-modality model, the XGB model integrated FW-DTI, voxel-based morphometry, and clinical features outperformed the RF and SVM models, achieving an accuracy of 86.7% and an area under the curves (AUC) value 93.2% in the training cohort, and an accuracy of 77.8% and AUC value of 83.1% in the external testing cohort. The model demonstrated high sensitivity but relatively low specificity, indicating a tendency toward positive predictions. Furthermore, FW-DTI indices were shown to have the highest predictive value for Aβ deposition. Integrating FW-DTI with structural and clinical features effectively differentiated Aβ positivity in AD, with FW-DTI indices contributing the highest predictive risks, demonstrating the potential of FW-DTI in AD diagnosis. The online version contains supplementary material available at 10.1186/s12880-026-02380-6.