Voxel-based evaluation for [18F] Florbetaben brain β-amyloid positron emission tomography of healthy control, mild cognitive impairment, and Alzheimer’s disease
Bibliographic record
Abstract
Background: Positron emission tomography (PET) scans are commonly used to diagnose Alzheimer’s disease (AD) by detecting β-amyloid (Aβ) deposition in the cortex; however, brain amyloid plaque load (BAPL) scores based on the visual interpretation of experts are highly subjective. In the current retrospective study, voxel-based processing of [18F] Florbetaben ([18F] FBB) Aβ PET scans was used to compare images from patients with AD, patients with mild cognitive impairment (MCI), and healthy controls (HCs). The aim of our study was to highlight the gray matter voxels that were higher uptake than white matter and perform group comparison of the numbers of these voxels among AD, MCI and HC subjects. Methods: This was a cross-sectional study investigating Aβ PET of AD, MCI and HC subjects from the Global Alzheimer’s Association Information Network (GAAIN) database and from Taipei Veterans General Hospital (TVGH) between October 2019 and December 2021. The determination of diagnosis (AD, MCI and HC) from GAAIN database was referred from the notes of this database and that from TVGH was referred from the medical records. Aβ PET scans were processed using statistical parametric mapping software. This analysis identified gray matter voxels presenting [18F] FBB uptake intensity exceeding 98% of the maximal uptake intensity in white matter (i.e., positive gray matter voxels). Comparison of numbers of positive gray matter voxels among AD, MCI and HC subjects was performed by analysis of variance (ANOVA) (Kruskal-Wallis) test. Results: Whole brain observations revealed significant differences between AD patients, MCI patients, and elderly HC subjects in terms of the number of positive gray matter voxels (P=0.0281). In addition, more number of positive gray matter voxels were observed in AD patients than in elderly HC subjects (P=0.036). Most of the elderly HC subjects exhibited no positive gray matter voxels. Conclusions: Our preliminary analysis of [18F] FBB Aβ PET scans demonstrates proof-of-concept, suggesting that positive gray matter voxels could be used to differentiate among AD, MCI, and HC subjects.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".