Utilizing the Centiloid scale in cross-sectional and longitudinal PiB PET studies

Yi Su, Shaney Flores, Russ C. Hornbeck, Benjamin Speidel, Andrei G. Vlassenko, Brian A. Gordon, Robert A. Koeppe, William E. Klunk, Chengjie Xiong, John C.Morris, Tammie L.S. Benzinger. NeuroImage: Clinical, Volume 19, 2018, Pages 406-416 Read More

Abstract

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. Successful use of this tool is limited by the lack of a common standard in the quantification of amyloid imaging data. The Centiloid approach was recently proposed to address this problem and in this work, we report our implementation of this approach and evaluate the impact of differences in underlying image analysis methodologies using both cross-sectional and longitudinal datasets. The Centiloid approach successfully converts quantitative amyloid burden measurements into a common Centiloid scale (CL) and comparable dynamic range. As expected, the Centiloid values derived from different analytical approaches inherit some of the inherent benefits and drawbacks of the underlying approaches, and these differences result in statistically significant (p < 0.05) differences in the variability and group mean values. Because of these differences, even after expression in CL, the 95% specificity amyloid positivity thresholds derived from different analytic approaches varied from 5.7 CL to 11.9 CL, and the reliable worsening threshold varied from −2.0 CL to 11.0 CL. Although this difference is in part due to the dependency of the threshold determination methodology on the statistical characteristics of the measurements. When amyloid measurements obtained from different centers are combined for analysis, one should not expect Centiloid conversion to eliminate all the differences in amyloid burden measurements due to variabilities in underlying acquisition protocols and analysis techniques. © 2018 The Authors

Full Text

.

Posted on May 22, 2018
Posted in: HPAN, Neurodegeneration, Publications Authors: , ,