BACKGROUND AND PURPOSE: Recently, voxel-based statistical parametric images have been developed as additional diagnostic tools for AD. However these methods require the generation of a data base of healthy brain images. The purpose of this study was to produce and evaluate an automatic method using a data base-independent estimation system for the diagnosis of mild AD. MATERIALS AND METHODS: We retrospectively selected 66 subjects, including 33 patients with early AD and 33 age-matched healthy volunteers. Individual brain metabolic images were obtained by using FDG-PET. These were transformed by using 3D-SSP. We then produced CADDIES, which compares the parietal and sensorimotor metabolic counts by using t tests. If parietal metabolism was significantly lower than the sensorimotor metabolism, the subject was automatically diagnosed as having AD. The FDG-PET images were also analyzed by using a previous automatic diagnosis system (CAAD) that is dependent on the construction of a "normal data base" of healthy brain images. Diagnostic performance was compared between the 2 methods. RESULTS: The CADDIES demonstrated a sensitivity of 88%, specificity of 79%, and accuracy of 85%, while the CAAD system demonstrated a sensitivity of 70%, specificity of 94%, and accuracy of 82%. The area under the ROC curve of CADDIES was 0.964. The areas under ROC curves of the CAAD method in the parietal and posterior cingulate gyri were 0.843 and 0.939, respectively. CONCLUSIONS: The CADDIES method demonstrated a diagnostic accuracy similar to that of the CAAD system. Our results indicate that this method can be applied to the detection of patients with early AD in routine clinical examinations, with the benefit of not requiring the generation of a normal data base.
BACKGROUND AND PURPOSE: Recently, voxel-based statistical parametric images have been developed as additional diagnostic tools for AD. However these methods require the generation of a data base of healthy brain images. The purpose of this study was to produce and evaluate an automatic method using a data base-independent estimation system for the diagnosis of mild AD. MATERIALS AND METHODS: We retrospectively selected 66 subjects, including 33 patients with early AD and 33 age-matched healthy volunteers. Individual brain metabolic images were obtained by using FDG-PET. These were transformed by using 3D-SSP. We then produced CADDIES, which compares the parietal and sensorimotor metabolic counts by using t tests. If parietal metabolism was significantly lower than the sensorimotor metabolism, the subject was automatically diagnosed as having AD. The FDG-PET images were also analyzed by using a previous automatic diagnosis system (CAAD) that is dependent on the construction of a "normal data base" of healthy brain images. Diagnostic performance was compared between the 2 methods. RESULTS: The CADDIES demonstrated a sensitivity of 88%, specificity of 79%, and accuracy of 85%, while the CAAD system demonstrated a sensitivity of 70%, specificity of 94%, and accuracy of 82%. The area under the ROC curve of CADDIES was 0.964. The areas under ROC curves of the CAAD method in the parietal and posterior cingulate gyri were 0.843 and 0.939, respectively. CONCLUSIONS: The CADDIES method demonstrated a diagnostic accuracy similar to that of the CAAD system. Our results indicate that this method can be applied to the detection of patients with early AD in routine clinical examinations, with the benefit of not requiring the generation of a normal data base.
Authors: Igor Yakushev; Christian Landvogt; Hans-Georg Buchholz; Andreas Fellgiebel; Alexander Hammers; Armin Scheurich; Irene Schmidtmann; Alexander Gerhard; Mathias Schreckenberger; Peter Bartenstein Journal: Psychiatry Res Date: 2008-10-19 Impact factor: 3.222