Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies

作者：Vemuri Prashanthi; Gunter Jeffrey L; Senjem Matthew L; Whitwell Jennifer L; Kantarci Kejal; Knopman David S; Boeve Bradley F; Petersen Ronald C; Jack Clifford R Jr^*

来源：NeuroImage, 2008, 39(3): 1186-1197.

DOI：10.1016/j.neuroimage.2007.09.073

• support vector machines
• classification
• diagnosis
• Alzheimer's

Objective: To develop and validate a tool for Alzheimer's disease (AD) diagnosis in individual subjects using support vector machine (SVM)-based classification of structural MR (sMR) images.
Background. Libraries of sMR scans of clinically well characterized subjects can be harnessed for the purpose of diagnosing new incoming subjects.
Methods: One hundred ninety patients with probable AD were age-and gender-matched with 190 cognitively normal (CN) subjects. Three different classification models were implemented: Model I uses tissue densities obtained from sMR scans to give STructural Abnormality iNDex (STAND)-score; and Models II and III use tissue densities as well as covariates (demographics and Apolipoprotein E genotype) to give adjusted-STAND (aSTAND)-score. Data from 140 AD and 140 CN were used for training. The SVM parameter optimization and training were done by four-fold cross validation (CV). The remaining independent sample of 50 AD and 50 CN was used to obtain a minimally biased estimate of the generalization error of the algorithm.
Results: The CV accuracy of Model II and Model III aSTAND-scores was 88.5% and 89.3%, respectively, and the developed models generalized well on the independent test data sets. Anatomic patterns best differentiating the groups were consistent with the known distribution of neurofibrillary AD pathology.
Conclusions: This paper presents preliminary evidence that application of SVM-based classification of an individual sMR scan relative to a library of scans can provide useful information in individual subjects for diagnosis of AD. Including demographic and genetic information in the classification algorithm slightly improves diagnostic accuracy.

• 出版日期2008-2-1