Kinetics of the amino acid uptake tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) in human brain
Bibliographic record
Abstract
The large neutral amino acid [ 18 F]fluoroethoxy- L -tyrosine ([ 18 F]FET) is a popular tracer for detection and staging of intracranial tumors by positron emission tomography (PET). While its high tumoral uptake reflects over-expression of the L-type amino acid transporter (LAT1), there is little knowledge about the kinetics of [ 18 F]FET uptake in healthy brain tissue, owing to the limited PET data in healthy volunteers, and to the requirement of an arterial input function for compartmental analysis. To address this, we used long axial field-of-view (LAFOV) dynamic 40-min recordings of 28 post-operative patients with intracranial tumors to undertake parametric brain mapping relative to an image-derived arterial input function (IDIF) obtained from the aorta. We averaged the individual parametric maps to obtain estimates of the physiological uptake relatively unaffected by individual residual lesions and resections, and tested simplified single-frame methods for quantitation. The analyses yielded estimates of regional unidirectional blood–brain clearance K 1 (0.00825–0.0244 ml g −1 min −1 ), net blood–brain clearance K in (0.00448–0.00913 ml g −1 min −1 ), and equilibrium distribution volume V T (0.126–0.495 ml g −1 ), where the lowest values depict white matter, and the highest values cerebellum. In our test of a simplified quantitation of [ 18 F]FET uptake from single frame recordings, i.e., Gjedde–Patlak multilinear graphic analyses of K 1 at five min post-injection and K in at 40 min post injection, results were in good agreement with the analyses from the dynamic recordings (< 10% error). Compartmental analysis results for [ 18 F]FET uptake in extra-tumoral human brain regions are in accord with the few prior reports, mainly obtained in experimental animals, and support the use of single frame quantitation. Present findings in relatively healthy brain should inform the interpretation of pathological [ 18 F]FET uptake in tumors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".