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 [18F]fluoroethoxy-L-tyrosine ([18F]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 [18F]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 K1 (0.00825–0.0244 ml g−1 min−1), net blood–brain clearance Kin (0.00448–0.00913 ml g−1 min−1), and equilibrium distribution volume VT (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 [18F]FET uptake from single frame recordings, i.e., Gjedde–Patlak multilinear graphic analyses of K1 at five min post-injection and Kin at 40 min post injection, results were in good agreement with the analyses from the dynamic recordings (< 10% error). Compartmental analysis results for [18F]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 [18F]FET uptake in tumors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".