Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis

• Published in: Radiology Vol. 279; no. 3; pp. 838 - 848
• Main Authors: Möller, Christiane, Pijnenburg, Yolande A. L., van der Flier, Wiesje M., Versteeg, Adriaan, Tijms, Betty, de Munck, Jan C., Hafkemeijer, Anne, Rombouts, Serge A. R. B., van der Grond, Jeroen, van Swieten, John, Dopper, Elise, Scheltens, Philip, Barkhof, Frederik, Vrenken, Hugo, Wink, Alle Meije
• Format: Journal Article
• Language: English
• Published: United States 01.06.2016
• Subjects: Alzheimer Disease - classification; Alzheimer Disease - pathology; Atrophy; Diagnosis, Differential; Female; Frontotemporal Dementia - classification; Frontotemporal Dementia - pathology; Gray Matter - pathology; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Neuropsychological Tests; ROC Curve; Support Vector Machine
• ISSN: 0033-8419; 1527-1315; 1527-1315
• DOI: 10.1148/radiol.2015150220

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.