IC‐P‐026: Defining an optimal approach for evaluating regional metabolism with 18F‐FDG‐PET imaging: Anatomical versus probabilistic volumes of interest
Bibliographic record
Abstract
[18 F]FDG-PET is a recognized tool to identify abnormal patterns of brain metabolism (CMRglc) accompanying neurodegenerative diseases. Although pathological changes in AD affect first the mesial temporal lobes, the presence of abnormal CMRglc at that level at different disease stages remains controversial, possibly because of technical issues. We analyzed studies from subjects with normal cognition (NCo), with MCI and with AD, using: 1) anatomically or 2) probabilistically defined volumes of interest (VOIs), to determine how to better identify CMRglc abnormalities. Subjects were categorized following standard criteria. FDG studies were spatially normalized to a PET template and intensity normalized to the pons. Four anatomically defined VOIs obtained from the template (cingulate gyrus = CG, precuneus = pCu, infraparietal cortex = IPC, hippocampus = HIP) were analyzed. Three probabilistic VOIs (posterior cingulate = PCC, IPC, HIP) were created from t-statistical FDG-PET comparisons between AD and NCI cases, using voxels with a t value of at least 50% (and t value >3) of the peak t-value in the VOI. Average normalized FDG counts (NFC) were extracted. AD and MCI NFC obtained with the 2 approaches were compared to NCo NFC mean values. Cutoff points sensitivity and specificity, post-test likelihood ratios and p-values were assessed. We studied 49 AD, 19 MCI, 9 NCo (no significant demographic difference found between groups). As compared to NCo, ANOVA of anatomical VOIs showed bilaterally reduced CG, pCu and IPC NFC in AD (P<0.001); and bilaterally reduced CG and pCu NFC in MCI (P<0.001). Probabilistic VOIs showed bilaterally reduced PCC, IPC and HIP NFC in AD (P<0.01); and bilaterally reduced PCC and IPC and left HIP NFC in MCI (P<0.001). When comparing patients to NCo, the analysis of HIP VOIs yielded no statistically significant results with the anatomically defined VOIs, but significance was reached with the probabilistically defined VOIs, except in the right HIP VOI of MCI individuals.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".