Impact of Reference and Target Region Selection on Amyloid PET SUV Ratios in the Phase 1b PRIME Study of Aducanumab
Bibliographic record
Abstract
SUV ratios (SUVRs) are commonly used to quantify tracer uptake in amyloid-β PET. Here, we explore the impact of target and reference region-of-interest (ROI) selection on SUVR effect sizes using interventional data from the ongoing phase 1b PRIME study (NCT01677572) of aducanumab (BIIB037) in patients with prodromal or mild Alzheimer disease. Methods: The florbetapir PET SUVR was calculated at baseline (screening) and at weeks 26 and 54 for patients randomized to receive placebo and each of 4 aducanumab doses (1, 3, 6, and 10 mg/kg) using the whole cerebellum, cerebellar gray matter, cerebellar white matter, pons, and subcortical white matter as reference regions. In addition to the prespecified composite cortex target ROI, individual cerebral cortical ROIs were assessed as targets. Results: Of the reference regions used, subcortical white matter, cerebellar white matter, and the pons, alone or in combination, generated the largest effect sizes. The use of the anterior cingulate cortex as a target ROI resulted in larger effect sizes than the use of the composite cortex. SUVR calculations were not affected by correction for brain volume changes over time. Conclusion: Dose- and time-dependent reductions in the amyloid PET SUVR were consistently observed with aducanumab only in cortical regions prone to amyloid plaque deposition, regardless of the reference region used. These data support the hypothesis that florbetapir SUVR responses associated with aducanumab treatment are a result of specific dose- and time-dependent reductions in the amyloid burden in patients with Alzheimer disease.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".