A Semiautomated Method for Quantification of F 18 Florbetapir PET Images
Bibliographic record
Abstract
UNLABELLED: PET amyloid imaging is increasingly used in research trials related to Alzheimer disease and has potential as a quantitative biomarker. This study had 3 objectives: first, to describe a semiautomated quantitative method that does not require subject-specific MR imaging scans for estimating F 18 Florbetapir plaque binding using 10-min PET images; second, to evaluate the method's accuracy for identifying positive and negative scans; and third, to correlate derived standardized uptake value ratios to neuropathologic measures of amyloid. METHODS: The F 18 Florbetapir PET images are initially converted to Montreal Neurologic Institute brain atlas space using an internally developed PET target F 18 Florbetapir template. Subsequently, a single mean cortical standardized uptake value ratio (mcSUVr) is calculated from the mean standardized uptake value of 6 cortical regions normalized to a reference region. Four reference regions were explored: whole cerebellum, cerebellar gray matter, pons, and centrum semiovale. The performance of the resultant mcSUVrs were evaluated in 74 young cognitively normal subjects (age < 50 y) with a negligible likelihood of amyloid β pathology, and in 59 deceased subjects with autopsy-based amyloid β neuritic plaque measure who underwent F 18 Florbetapir PET imaging before death. RESULTS: Significant correlations were obtained between mcSUVrs and 3 different pathologic measures of amyloid deposition at autopsy using all 4 reference regions, with the whole-cerebellum mcSUVr correlating most strongly across pathologic measures (r = 0.71-0.75, P < 0.0001). Using the whole-cerebellum mcSUVr and a threshold mcSUVr of less than 1.10, 100% of young cognitively normal subjects were correctly classified as amyloid-negative (mcSUVr range, 0.87-1.08). Similarly, 20 of 20 autopsy-negative subjects showed mcSUVrs of 1.10 or less, whereas 38 of 39 pathology-verified amyloid-positive subjects had mcSUVrs of more than 1.10. CONCLUSION: This semiautomated F 18 Florbetapir PET quantification method yielded mcSUVrs that significantly correlated with measures of amyloid pathology at autopsy. The method also effectively discriminated autopsy-identified amyloid-positive and -negative cases using a whole-cerebellum mcSUVr threshold of 1.10.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".