Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI

• Published in: European radiology Vol. 27; no. 8; pp. 3372 - 3382
• Main Authors: Bron, Esther E., Smits, Marion, Papma, Janne M., Steketee, Rebecca M. E., Meijboom, Rozanna, de Groot, Marius, van Swieten, John C., Niessen, Wiro J., Klein, Stefan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017; Springer Nature B.V
• Subjects: Adult; Age; Aged; Aged, 80 and over; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Brain; Brain - diagnostic imaging; Brain mapping; Classification; Computer Applications; Control methods; Dementia; Dementia disorders; Diagnosis; Diagnosis, Computer-Assisted - methods; Diagnosis, Differential; Diagnostic Radiology; Diagnostic systems; Differential diagnosis; Diffusion Tensor Imaging - methods; Disease control; Early Diagnosis; Feature maps; Female; Frontotemporal dementia; Frontotemporal Dementia - diagnostic imaging; Humans; Imaging; Internal Medicine; Interventional Radiology; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical diagnosis; Medicine; Medicine & Public Health; Middle Aged; Neurodegenerative diseases; Neuroimaging; Neuroradiology; Radiology; Retrospective Studies; ROC Curve; Spin labeling; Spin Labels; Support Vector Machine; Support vector machines; Ultrasound; Differential diagnosis; Perfusion; Classification; Dementia; Diffusion tensor imaging
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-016-4691-x

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.