Use of Standardized Uptake Value Ratios Decreases Interreader Variability of [18F] Florbetapir PET Brain Scan Interpretation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Fluorine-18 florbetapir is a recently developed β-amyloid plaque positron-emission tomography imaging agent with high sensitivity, specificity, and accuracy in the detection of moderate-to-frequent cerebral cortical β-amyloid plaque. However, the FDA has expressed concerns about the consistency of interpretation of [(18)F] florbetapir PET brain scans. We hypothesized that incorporating automated cerebral-to-whole-cerebellar standardized uptake value ratios into [(18)F] florbetapir PET brain scan interpretation would reduce this interreader variability. MATERIALS AND METHODS: This randomized, blinded-reader study used previously acquired [(18)F] florbetapir scans from 30 anonymized patients who were enrolled in the Alzheimer's Disease Neuroimaging Initiative 2. In 4 separate, blinded-reading sessions, 5 readers classified 30 cases as positive or negative for significant β-amyloid deposition either qualitatively alone or qualitatively with additional adjunct software that determined standardized uptake value ratios. A κ coefficient was used to calculate interreader agreement with and without the use of standardized uptake value ratios. RESULTS: There was complete interreader agreement on 20/30 cases of [(18)F] florbetapir PET brain scans by using qualitative interpretation and on 27/30 scans interpreted with the adjunct use of standardized uptake value ratios. The κ coefficient for the studies read with standardized uptake value ratios (0.92) was significantly higher compared with the qualitatively read studies (0.69, P = .006). CONCLUSIONS: Use of standardized uptake value ratios improves interreader agreement in the interpretation of [(18)F] florbetapir images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".