Semi-quantification and grading of amyloid PET: A project of the European Alzheimer's Disease Consortium (EADC)

Abstract

Background: amyloid-PET reading has been classically implemented as a binary assessment, although the clinical experience has shown that the number of borderline cases is non negligible not only in epidemiological studies of asymptomatic subjects but also in naturalistic groups of symptomatic patients attending memory clinics. In this work we develop a model to compare and integrate visual reading with two independent semi-quantification methods in order to obtain a tracer-independent multi-parametric evaluation.

Methods: We retrospectively enrolled three cohorts of cognitively impaired patients submitted to 18F-florbetaben (53 subjects), 18F-flutemetamol (62 subjects), 18F-florbetapir (60 subjects) PET/CT respectively, in 6 European centres belonging to the EADC. The 175 scans were visually classified as positive/negative following approved criteria and further classified with a 5-step grading as negative, mild negative, borderline, mild positive, positive by 5 independent readers, blind to clinical data. Scan quality was also visually assessed and recorded. Semi-quantification was based on two quantifiers: the standardized uptake value (SUVr) and the ELBA method. We used a sigmoid model to relate the grading with the quantifiers. We measured the readers accord and inconsistencies in the visual assessment as well as the relationship between discrepancies on the grading and semi-quantifications.

Conclusion: It is possible to construct a map between different tracers and different quantification methods without resorting to ad-hoc acquired cases. We used a 5-level visual scale which, together with a mathematical model, delivered cut-offs and transition regions on tracers that are (largely) independent from the population. All fluorinated tracers appeared to have the same contrast and discrimination ability with respect to the negative-to-positive grading. We validated the integration of both visual reading and different quantifiers in a more robust framework thus bridging the gap between a binary and a user-independent continuous scale.

Keywords: Amyloid PET; Semi-quantification; Visual assessment.

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Fig. 1

Quantification-visual assessment relationship for the 3 fluorinated tracers. On the x-axis: z-score of the two quantifiers; on the y-axis the average visual grading. Dots represents scans, continuous line is the sigmoid model. Intersection of model with mild and borderline evaluations is projected onto the scores to define a transition region (gray area) and the cutoff (gray line).

Fig. 2

Relationship between quantification score and evaluation latitude. Each circle represents a group of scans sharing the same average grading. The group position on the x-axis is the average score of its members on the first PCA axis (PCA computed on the z-score quantifiers). The group position in the y-axis is the average grading. Vertical lines show the group evaluation latitude, that is, the lowest to highest grading received on any of the group's member.

Fig. 3

ELBA-SUVr scatter-plot with binary visual assessment. Dots represent scans, colors are according to the combination of negative and positive evaluations given by the 5 readers.

Fig. 4

Relationship between the binary evaluation and the latitude. Each box shows the number of scans grouped by binary class and maximum grading difference received in the 5-step visual assessment.

Fig. 5

Left: distribution of the quality flags in the PCA plane; dots represents all scans, color is proportional to the number of quality issues raised by the 5 readers; marginal distribution shows the kernel density estimation. Right: heatmap of quality and latitude, showing the fraction of scans normalized on the quality and the actual number of scans sharing the same quality interpretation and evaluation latitude (within brackets).

Fig. 6

Matrix plot of all between-tracers models and container-cohort fits. Dots represent the average quantification on the cohort containers, lines crossing the dots are the standard deviations. The thick line is the model mapping, the dashed thin line is the linear regression based on the average quantification values (container mapping). Cut-offs are based on the model mapping.