Abstract
Introduction
Amyloid deposition in the brain is associated with neurodegenerative disorders, most notably Alzheimer’s disease. Efforts to assess the amyloid burden of patients are increasingly impactful with the advent of anti-amyloid therapies. One method for examining the density of amyloid deposits in the brain is amyloid positron emission tomography (PET), utilizing radiopharmaceutical compounds such as florbetapir, flutemetamol, and florbetaben which have high affinity for aggregates of fibrillar amyloid. This bound structure of radiotracer and amyloid plaques can then be detected by PET imaging and seen visually as increased tracer uptake in the cerebral cortex in patients with Alzheimer's disease. Until recent years, amyloid PET has been interpreted visually as positive or negative, with no scale for quantifying the degree of uptake of radiotracer. Moreover, comparison between different amyloid PET studies was complicated by the different radiopharmaceuticals used.
Main Body
These limitations prompted the development of Centiloid Score which allowed for quantification of the amount of amyloid disease in the brain on a scale in common among different radiotracers. This scaling method has anchor points at 0 and 100, representing typical young cognitively unimpaired patients versus patients with typical mild to moderate Alzheimer’s disease, respectively. The Centiloid scale employs standardized uptake value ratios (SUVR) and Z-scores to quantify the amyloid plaques. The SUVR compares the average uptake in the target regions of interest (ROI) to that of the uptake in the reference region, generally the cerebellum or pons. Given the Centiloid score’s reliance on comparison, it is of the utmost importance that the system chooses appropriate regions for both the areas of interest and control. However, due to factors such as high background noise, prominent brain tissue atrophy, or non-standard positioning of the head, the Centiloid values can be misrepresentative of the actual degree of amyloid deposition, which can in turn affect patients’ abilities to access anti-amyloid therapies. In this educational exhibit, cases of amyloid PET with inaccurate and unreliable Centiloid values will be presented to demonstrate how these factors can result in quantification that is misrepresentative of patients’ actual amyloid disease burden.
Conclusion
This information is important because the reader of amyloid PET must assess the validity of the computer automated ROI and Centiloid values with awareness of these pitfalls to avoid misinterpretations from inaccurate data. The increasing use of anti-amyloid therapies has necessitated quantifying amyloid disease and tracking response with amyloid PET and Centiloid scores. Further improvements in the systems used to generate quantitative assessments of the studies would be beneficial.