Comparison of 18F-florbetaben quantification results using the standard Centiloid, MR-based, and MR-less CapAIBL® approaches: Validation against histopathology.

INTRODUCTION: 18F-florbetaben is currently approved for the visual rule out of ß-amyloid (Aß) pathology. It is also used for recruitment and as an outcome measure in therapeutic trials, requiring accurate and reproducible quantification of Aß burden in the brain. METHODS: Data from eighty-eight subjects (52 male subjects, aged 79.8 ± 10.6 years) who underwent antemortem 18F-florbetaben positron emission tomography scan and magnetic resonance imaging less than a year before neuropathological assessment at autopsy were evaluated. Image analysis was performed using the standard Centiloid (CL) statistical parametric mapping approach and CapAIBL®. Imaging results were compared against autopsy data. RESULTS: Against combined Bielschowsky silver staining and immunohistochemistry histopathological scores, statistical parametric mapping had 96% sensitivity, 96% specificity, and 95% accuracy, whereas magnetic resonance-less CapAIBL standardized uptake value ratioWhole Cerebellum had 94% sensitivity, 96% specificity, and 95% accuracy. Based on the combined histopathological scores, a CL threshold band of 19 ± 7 CL was determined. DISCUSSION: Quantification of 18F-florbetaben positron emission tomography scans using magnetic resonance-based and magnetic resonance-less CapAIBL® approaches showed high agreement, establishing a pathology-based threshold in CL.

Parallel Design of Browsing Scheme and Data Model for Multi-Level Hierarchical Application Devoted to Management of Patient with Infectious Disease in Primary Care.

Many decision systems are based on a hierarchical approach, enriching the known context used to finally choose the right potential action. Designing the scheme for browsing the clinical guidelines is a task devoted to expert in infectious diseases. Designing the data model is a task devoted to the expert in data modeling. As a consequence, browsing scheme and data model generally differ in terms of abstraction levels. While the browsing scheme proposes to navigate into depth, the data model stays flat. We propose here a novel method to design in parallel the browsing scheme and the data model so that both of them reflect the different abstraction levels in decision process.