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.

The Centiloid Project: Standardizing quantitative amyloid plaque estimation by PET

“…A “mild” outlier was defined as PiB retention that exceeded the third quartile by >1.5 times the interquartile range (Q3+1.5×IQR) or was beneath the first quartile by an amount <1.5 times the interquartile range (Q1-1.5×IQR), that is, any observation outside the “inner fences” of the boxand-whisker plot [17]. This approach has previously been used in the “Iterative Outlier” method of defining PiB-positive cutoffs [13; 18]. As described below, this resulted in the exclusion of two AD subjects with low PiB retention (no AD subjects were outliers due to high PiB retention) leaving 45 AD-100 subjects for further analysis.…”

Performance of 11C-Pittsburgh Compound B PET Binding Potential Images in the Detection of Amyloid Deposits on Equivocal Static Images

“…In other words, focal or slight cortical 11 C-PiB uptake showed low quantitative values in the 11 C-PiB-equivocal or 11 C-PiB-positive groups. Therefore, visual assessments can detect amyloid b earlier than quantitative analyses, as described by Cohen et al when they compared visual findings with quantitative analysis of 11 C-PiB PET in healthy controls (23). Third, white matter retention was visually assessed to be greater in static images than in BP images.…”

Arterial Stiffness and β-Amyloid Progression in Nondemented Elderly Adults

“…5 Recent advances in determining Aβ positivity show improved classification using a sparse k-means approach 10 (SKM, averaged over 6 regions, standardized uptake value ratio of >1.67), as opposed to the iterative outlier method (IO, averaged over 5 regions, standardized uptake value ratio of >1.57), 11 which we used in our baseline study. 8 We conducted all analyses using both classification approaches and found them to be similar.…”