Classification of amyloid-positivity in controls: Comparison of visual read and quantitative approaches

An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (A beta) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant A beta deposition from those in which A beta deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(-)] subjects. Methods: We tested the performance of IO using late-summed tissue ratio data with atrophy correction or with an automated template method without atrophy correction and tested the robustness of the method when applied to a cohort of older subjects in which separation between PiB(+) and PiB(-) subjects was not so distinct. Results: The IO method did not perform consistently across analyses and performed particularly poorly when separation was less clear. We found that a sparse k-means (SKM) cluster analysis approach performed significantly better; performing more consistently across methods and subject cohorts. We also compared SKM to a consensus visual read approach and found very good correspondence. Conclusion: The visual read and SKM methods, applied together, may optimize the identification of early A beta deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers. (C) 2013 Elsevier Inc. All rights reserved.