Classification of amyloid-positivity in controls: Comparison of visual read and quantitative approaches - Cohen, Ann D.; Mowrey, Wenzhu; Weissfeld, Lisa A.; Aizenstein, Howard J.; McDade, Eric; Mountz, James M.; Nebes, Robert D.; Saxton, Judith A.; Snitz, Beth; DeKosky, Steven; Williamson, Jeff; Lopez, Oscar L.; et al. (NeuroImage (Orlando, Fla.), 2013)
An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ 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. 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. 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. The visual read and SKM methods, applied together, may optimize the identification of early Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers. ► Limitations of the iterative outlier method for Aβ-positive cutoffs ► Use of sparse k-means as a method for identification of Aβ-positive cutoffs ► Comparison of the above objective methods to visual reads shows good agreement. ► A combination of methods may be the best approach to identify early Aβ.