Visual-statistical interpretation of (18)F-FDG-PET images for characteristic Alzheimer patterns in a multicenter study: inter-rater concordance and relationship to automated quantitative evaluation

Abstract

Background and purpose: The 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 methods: This 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.

Results: Agreement 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).

Conclusions: Inter-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.

Fig 1.

Breakdown of the ¹⁸F-FDG-PET cases into degree of match by 3 raters in a combined visual-statistical human classification into 7 (FDG-7) (A) or 2 (FDG-2) (B) categories. A perfect match by the 3 raters is observed for 62% of the cases for FDG-7 and 76% for FDG-2 in total. The AD group shows the highest concordance followed by the MCI and NC groups, in this order, both for FDG-7 and FDG-2.

Fig 2.

Boxplots of the FDG-PET score against the number of raters who interpreted the ¹⁸F-FDG-PET images as P1 (A) and N1 (B) based on the FDG-7 criteria. The FDG-PET score gradually increases as the number of P1 (AD pattern) interpretations increases (Spearman rank correlation coefficient: ρ = 0.59, P < .0001). On the other hand, FDG-PET score gradually decreases as the number of N1 (normal pattern) interpretations increases (ρ = −.64, P < .0001).

Fig 3.

Scatterplot of the FDG-PET score as contrasted with the combined visual-statistical interpretation determined by the consensus read of ¹⁸F-FDG-PET for each clinical group (A, NC; B, MCI; and C, AD). The horizontal line indicates the cutoff level of 0.67 derived by receiver operating characteristic analysis on P1 and N1 cases.