• T Yamane, Y Ikari, T Nishio, K Ishii, T Kato, K Ito, D H S Silverman, M Senda, T Asada, H Arai, M Sugishita, T Iwatsubo, and J-ADNI Study Group.
• From the Division of Molecular Imaging (T.Y., Y.I., T.N., M. Senda), Institute of Biomedical Research and Innovation, Kobe, Japan.
• AJNR Am J Neuroradiol. 2014 Feb 1; 35 (2): 244-9.

Background And PurposeThe role of (18)F-FDG-PET in the diagnosis of Alzheimer disease is increasing and should be validated. The aim of this study was to assess the inter-rater variability in the interpretation of (18)F-FDG-PET images obtained in the Japanese Alzheimer's Disease Neuroimaging Initiative, a multicenter clinical research project.Materials And MethodsThis study analyzed 274 (18)F-FDG-PET scans (67 mild Alzheimer disease, 100 mild cognitive impairment, and 107 normal cognitive) as baseline scans for the Japanese Alzheimer's Disease Neuroimaging Initiative, which were acquired with various types of PET or PET/CT scanners in 23 facilities. Three independent raters interpreted all PET images by using a combined visual-statistical method. The images were classified into 7 (FDG-7) patterns by the criteria of Silverman et al and further into 2 (FDG-2) patterns.ResultsAgreement among the 7 visual-statistical categories by at least 2 of the 3 readers occurred in >94% of cases for all groups: Alzheimer disease, mild cognitive impairment, and normal cognitive. Perfect matches by all 3 raters were observed for 62% of the cases by FDG-7 and 76 by FDG-2. Inter-rater concordance was moderate by FDG-7 (κ = 0.57) and substantial in FDG-2 (κ = 0.67) on average. The FDG-PET score, an automated quantitative index developed by Herholz et al, increased as the number of raters who voted for the AD pattern increased (ρ = 0.59, P < .0001), and the FDG-PET score decreased as those for normal pattern increased (ρ = -0.64, P < .0001).ConclusionsInter-rater agreement was moderate to substantial for the combined visual-statistical interpretation of (18)F-FDG-PET and was also significantly associated with automated quantitative assessment.

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