Pharmacokinetic Analysis of Dynamic [18F]FAZA PET Imaging in Pancreatic Cancer Patient
Bibliographic record
Abstract
Abstract Purpose This study assessed the pharmacokinetics of the hypoxia PET tracer, [18F]fluoroazomycin arabinoside ([18F]FAZA), in pancreatic cancer (PCa) patients and determined the optimal kinetic parameters to distinguish cancerous from normal pancreatic tissue. Method Twenty patients with pancreatic ductal adenocarcinoma underwent dynamic [ 18 F]FAZA scans. The tissue time activity curve (TAC) was analyzed using graphical methods to determine reversibility of tracer binding and with standard compartment (S2TC) model and flow modified two tissue compartment (F2TC) model, developed to incorporate transit time of tracer through the blood vessel, to estimate the kinetic parameters. The optimal parameter set to distinguish hypoxic tumors from normal tissues was determined using logistic regression. Results Both graphical and kinetic model analysis indicated that tracer was reversibly bound. According to the Akaike Information Criteria, the F2TC model fitted the tumor TAC better than the S2TC model. Total distribution volume, V T , estimated by the F2TC model for both tumor and normal pancreatic tissue was not significant but that estimated by the S2TC model was significantly different from Logan graphical analysis. The extravascular distribution volume ( DV ) and tracer dissociation rate constant ( k 4 ) can classify hypoxic PCa from normal tissue with sensitivity of 95% and negative predictive value of 89% (P<0.01). Conclusions Kinetic analysis of dynamic [ 18 F]FAZA PET can distinguish PCa from normal tissue with high sensitivity. The reversibility of [ 18 F]FAZA binding in hypoxic cells could be due to glutathionylation of the nitroreductase reduced products and their subsequent efflux from same cells via the ATP mediated multidrug resistant protein (MRP-1) efflux pump.
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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) | 0.000 | 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 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".