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Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using
Esther E Bron1, Marion Smits2, Janne M Papma3
1Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus MC, Office Na2502, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands. e.bron@erasmusmc.nl.
Objectives:
To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls.
Methods:
This retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions.
Results:
Combining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information.
Conclusions:
ASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level.
Key Points:
• Multiparametric MRI is promising for computer-aided diagnosis of early-onset AD and FTD. • Diagnosis is driven by different brain regions when using different MRI methods. • Combining structural MRI, ASL, and DTI may improve differential diagnosis of dementia.
Insights
Adding arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI enhances computer-aided diagnosis for Alzheimer's disease (AD) and frontotemporal dementia (FTD). This multiparametric approach improves differential diagnosis accuracy for these neurodegenerative conditions.
Area of Science:
- Neuroimaging
- Radiology
- Neurology
Background:
- Alzheimer's disease (AD) and frontotemporal dementia (FTD) are distinct neurodegenerative disorders.
- Accurate differential diagnosis is crucial for effective patient management.
- Structural MRI is a standard tool, but its diagnostic performance can be limited.
Purpose of the Study:
- To evaluate the additional diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI.
- To assess computer-aided classification of early-onset AD, FTD, and controls.
- To investigate the contribution of different MRI modalities to differential diagnosis.
Main Methods:
- Retrospective analysis of MRI data from early-onset AD patients, FTD patients, and controls.
- Voxel-wise feature maps derived from structural MRI, ASL, and DTI.
- Support vector machine (SVM) classification for AD vs. controls (AD-CN), FTD vs. controls (FTD-CN), AD vs. FTD (AD-FTD), and multi-class (AD-FTD-CN).
- Performance assessment using area under the receiver-operating-characteristic curve (AUC) and accuracy.
Main Results:
- Combining ASL and DTI with structural MRI significantly improved differential diagnosis performance (AUC = 84%) compared to structural MRI alone (AUC = 72%).
- Individual ASL and DTI performance did not surpass structural MRI.
- Different brain regions contributed to classification for ASL and DTI versus structural MRI, indicating complementary information.
Conclusions:
- Multiparametric MRI combining structural MRI, ASL, and DTI shows promise for computer-aided diagnosis of early-onset AD and FTD.
- These advanced MRI techniques may enhance the differential diagnosis of dementia on a single-subject level.
- The distinct regional contributions highlight the value of integrating multiple MRI sequences.
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