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

Abstract

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.

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 Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers.

Figure 1

(A–C) Depiction of IO for the MAN-rDVR dataset. Subjects with PiB retention above the upper-inner fence or below the lower-inner fence [circles outside the whiskers of the plot in (A)] were excluded and box plots were recalculated (B). After 2 iterations, no more high outliers remained and the upper-inner fence was taken as the IO-MAN-rDVR cutoff (C). (D) Box and whisker plots of 62 subject group for IO-Tem-rSUVR50-70 dataset (red) compared to IO-Tem-rSUVR40-60 dataset (black). (E) Box and whisker plots of 62 subject control group for IO-Tem-rSUVR50-70 dataset (red) compared to IO-152OLDER-Tem-rSUVR50-70 dataset (black). (F) Histograms showing the more bimodal 62 control dataset (MAN-rSUVR50-70; yellow) and the more continuously distributed 152 older control dataset (Tem-rSUVR50-70; blue). The IO cutoff determined with the MAN-rSUVR50-70 dataset from the 62 younger controls is shown with a solid red line and the cutoff determined with the 152OLDER-Tem-rSUVR50-70 dataset is shown with a dashed red line.

Figure 2

Schematic demonstrating agreement of PiB(+)/PiB(−) status using IO cutoffs derived from three versions of the PiB PET data (DVR, SUVR50-70 and SUVR40-60; all determined from a single acquisition) and from SUVR50-70 cutoffs derived from a distinct group of 152 older controls. All cases are arranged in the same order of increasing SUVR50-70 global cortical values with highest at the top. Manual-regional analyses (with partial-volume correction) are always shown in the left-most of the three columns with PiB(+) cases in red, template-based-regional analyses (without partial-volume correction) are always shown in the middle column with PiB(+) cases shown in brown and template-based-global analyses (without partial-volume correction) are always shown in the right-most of the three columns with PiB(+) cases shown in tan. For all analyses, PiB(−) cases are shown in gray. Manual ROI data was not available for the older control group. Note that, even when the cutoffs from the 152 older controls were used, it is the original 62 younger controls that are shown rated in this figure.

Figure 3

Regional weights determined by the SKM method for the all template-based datasets are shown in order of decreasing weights for the SUVR50-70 dataset.

Figure 4

Depiction of PiB(−) (gray) and PiB(+) (colored) subjects across datasets and analysis methods for SKM. Cases have the same arrangement and color-coding as Figure 2.

Figure 5

Schematic demonstrating agreement among baseline SKM Tem-rSUVR50-70 quantitative analysis [left; PiB(+) in brown], baseline visual reads [middle; PiB(+) in green] and longitudinal SKM TEM-rSUVR50-70 quantitative analysis [right; PiB(+) in brown]. Gray cells always indicate subjects rated as PiB(−) by the indicated method. Black cells indicate that there was not longitudinal follow-up available. All cases have the same arrangement as in figures 2 and 4.