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Our objective was to determine the optimal approach for assessing amyloid disease in a cognitively normal elderly population. Methods: Dynamic 18F-flutemetamol PET scans were acquired using a coffee-break protocol (a 0- to 30-min scan and a 90- to 110-min scan) on 190 cognitively normal elderly individuals (mean age, 70.4 y; 60% female). Parametric images were generated from SUV ratio (SUVr) and nondisplaceable binding potential (BPND) methods, with cerebellar gray matter as a reference region, and were visually assessed by 3 trained readers. Interreader agreement was calculated using κ-statistics, and semiquantitative values were obtained. Global cutoffs were calculated for both SUVr and BPND using a receiver-operating-characteristic analysis and the Youden index. Visual assessment was related to semiquantitative classifications. Results: Interreader agreement in visual assessment was moderate for SUVr (κ = 0.57) and good for BPND images (κ = 0.77). There was discordance between readers for 35 cases (18%) using SUVr and for 15 cases (8%) using BPND, with 9 overlapping cases. For the total cohort, the mean (±SD) SUVr and BPND were 1.33 (±0.21) and 0.16 (±0.12), respectively. Most of the 35 cases (91%) for which SUVr image assessment was discordant between readers were classified as negative based on semiquantitative measurements. Conclusion: The use of parametric BPND images for visual assessment of 18F-flutemetamol in a population with low amyloid burden improves interreader agreement. Implementing semiquantification in addition to visual assessment of SUVr images can reduce false-positive classification in this population.
Pubmed ID: 30315145
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A project that aims to improve access to human health data by developing a common information framework (EMIF-Platform) that allows for efficient re-use of existing health data, opening up new avenues of research for scientists. To ensure immediate applicability, the project includes two specific therapeutic research topics: the onset of Alzheimer's Disease (EMIF-AD) and metabolic complications of obesity (EMIF-Metabolics). The AD Topic aims to discover and validate biomarkers of AD onset in the preclinical and prodromal phase as well as for disease progression and identify high-risk individuals for therapeutic trials for prevention. The Metabolic Topic aims to discover and evaluate biomarkers for the risk of metabolic complications in obesity and to identify high-risk populations for intervention purposes. Collaboration between the 3 topics will ensure the development and delivery of an efficient Information Framework. This initiative has combined several data sets for neuroimaging including ADNI and several others, curating them into transmart.
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