Optimized classification of 18F-Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment

Introduction: Standardized uptake value ratios (SUVRs) calculated from cerebral cortical areas can be used to categorize F-18-Florbetaben (FBB) PET scans by applying appropriate cutoffs. The objective of this work was first to generate FBB SUVR cutoffs using visual assessment (VA) as standard of truth (SoT) for a number of reference regions (RR) (cerebellar gray matter (GCER), whole cerebellum (WCER), pons (PONS), and subcortical white matter (SWM)). Secondly, to validate the FBB PET scan categorization performed by SUVR cutoffs against the categorization made by post-mortem histopathological confirmation of the A beta presence. Finally, to evaluate the added value of SUVR cutoff categorization to VA. Methods: SUVR cutoffs were generated for each RR using FBB scans from 143 subjects who were visually assessed by 3 readers. SUVR cutoffs were validated in 78 end-of life subjects using VA from 8 independent blinded readers (3 expert readers and 5 non-expert readers) and histopathological confirmation of the presence of neuritic beta-amyloid plaques as SoT. Finally, the number of correctly or incorrectly classified scans according to pathology results using VA and SUVR cutoffs was compared. Results: Composite SUVR cutoffs generated were 1.43 (GCER), 0.96 (WCER), 0.78 (PONS) and 0.71 (SWM). Accuracy values were high and consistent across RR (range 83-94% for histopathology, and 85-94% for VA). SUVR cutoff performed similarly as VA but did not improve VA classification of FBB scans read either by expert readers or the majority read but provided higher accuracy than some non-expert readers. Conclusion: The accurate scan classification obtained in this study supports the use of VA as SoT to generate sitespecific SUVR cutoffs. For an elderly end of life population, VA and SUVR cutoff categorization perform similarly in classifying FBB scans as A beta-positive or A beta-negative. These results emphasize the additional contribution that SUVR cutoff classification may have compared with VA performed by non-expert readers.