Determination of an Optimal Pharmacokinetic Model of <sup>18</sup>F-FET for Quantitative Applications in Rat Brain Tumors
Bibliographic record
Abstract
<i>O</i>-(2-<sup>18</sup>F-fluoroethyl)-l-tyrosine (<sup>18</sup>F-FET) is a radiolabeled artificial amino acid used in PET for tumor delineation and grading. The present study compares different kinetic models to determine which are more appropriate for <sup>18</sup>F-FET in rats. <b>Methods:</b> Rats were implanted with F98 glioblastoma cells in the right hemisphere and scanned 9–15 d later. PET data were acquired during 50 min after a 1-min bolus of <sup>18</sup>F-FET. Arterial blood samples were drawn for arterial input function determination. Two compartmental pharmacokinetic models were tested: the 2-tissue model and the 1-tissue model. Their performance at fitting concentration curves from regions of interest was evaluated using the Akaike information criterion, <i>F</i> test, and residual plots. Graphical models were assessed qualitatively. <b>Results:</b> Metrics indicated that the 2-tissue model was superior to the 1-tissue model for the current dataset. The 2-tissue model allowed adequate decoupling of <sup>18</sup>F-FET perfusion and internalization by cells in the different regions of interest. Of the 2 graphical models tested, the Patlak plot provided adequate results for the tumor and brain, whereas the Logan plot was appropriate for muscles. <b>Conclusion:</b> The 2-tissue-compartment model is appropriate to quantify the perfusion and internalization of <sup>18</sup>F-FET by cells in various tissues of the rat, whereas graphical models provide a global measure of uptake.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".