XXXX and @entity146 : Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

@entity1 @entity146 @entity195
UNASSIGNED: Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between @entity195 ( @entity195 ) and @entity146 ( @entity146 ) in individual @entity1 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 @entity1 with @entity195 , 51 @entity1 with @entity146 , 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 @entity1 with @entity195 versus control subjects, 72% for @entity1 with @entity146 versus control subjects, and 79% for @entity1 with @entity195 versus @entity1 with @entity146 (P  <= .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for @entity1 with @entity195 versus control subjects, 85% for @entity1 with @entity146 versus control subjects, and 82% for @entity1 with @entity195 versus @entity1 with @entity146 , with a good to excellent AUC (range, 0.81-0.95; P  <= .001). Machine learning-based categorization of @entity195 versus @entity146 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 @entity1 with @entity195 and those with @entity146 as compared with normal aging by using common T1-weighted structural MR imaging. (  ) RSNA, 2015.
