Visual-statistical interpretation of 18F-FDG-PET images for characteristic alzheimer patterns in a multicenter study: inter-rater concordance and relationship to automated quantitative evaluation
Open accessAbstract
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.
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Reduction in glucose metabolism in the brain is common in Alzheimer's patients.
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- Authors
- Yamane, T., Ikari, Y., Nishio, T., Ishii, K., Ishii, K., Kato, T., Ito, K., Silverman, D. H. S., Senda, M., Asada, T., Arai, H., Sugishita, M., Iwatsubo, T., & the J-ADNI Study Group
- Affiliations
- Alzheimer’s Disease Neuroimaging Initiative (US)
- University of California, Los Angeles (US)
- University of Tsukuba (JP)
- Tohoku University (JP)
- Institute of Brain and Blood Vessels (JP)
- Venue
- American Journal of Neuroradiology
- Published
- 2013-08-01
- Volume
- 35
- Issue
- 2
- Pages
- 244-249
- ISSN
- 0195-6108 (ISSN-L)
- 1936-959X
- PMC
- PMC7965770
- OpenAlex
- W2157391751
- Cited by
- 48
- References
- 16
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 3 · 2020: 5 · 2019: 5 · 2018: 5 · 2017: 7 · 2016: 3 · 2015: 6 · 2014: 5 · 2013: 1
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- American Journal of Neuroradiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Eli Lilly and Company
- Alzheimer's Disease Neuroimaging Initiative
- Ministry of Health, Labour and Welfare
- Eli Lilly Japan
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- 18F-FDG PET, Alzheimer's disease, mild cognitive impairment, inter-rater concordance, PET/CT
- MeSH terms
- Algorithms, Alzheimer Disease, Artificial Intelligence, Data Interpretation, Statistical, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Japan, Male, Middle Aged, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Observer Variation, Radiopharmaceuticals, Fluorodeoxyglucose F18, Positron-Emission Tomography, Cognitive Dysfunction
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