2-<sup>18</sup>F-Fluoroethanol Is a PET Reporter of Solid Tumor Perfusion
Bibliographic record
Abstract
Solid tumor perfusion is a proven variable of interest for predicting cancer aggression and response to therapy. Current methods for noninvasively imaging tumor perfusion with PET are limited by restricted accessibility and short half-lives of perfusion radiotracers. This study presents 2-18F-fluoroethanol (2-18F-FEtOH) as a perfusion reporter that can distinguish between tumors of varying perfusion levels and can be applied to screening drugs that modify tumor perfusion. Methods: Uptake of 2-18F-FEtOH in 4T1 and 67NR murine mammary carcinoma tumors grown in mice was measured using ex vivo radiography as well as static and dynamic PET imaging. 2-18F-FEtOH uptake was directly compared with the 14C-iodoantipyrine perfusion reporter, and the perfusion-modifying drugs nicotinamide, pentoxifylline, and hydralazine were used to manipulate tumor perfusion before 2-18F-FEtOH quantification. Results: Uptake of 2-18F-FEtOH in 4T1 and 67NR tumors was consistent with known perfusion differences within and between these tumors. 2-18F-FEtOH uptake corresponded well with 14C-iodoantipyrine and reflected the tumor perfusion-modifying effects of each drug. Conclusion: 2-18F-FEtOH is a novel 18F-based radiotracer for investigating tumor perfusion with PET imaging. Quantification of 2-18F-FEtOH uptake can be used to distinguish between tumors of varying perfusion and to screen the efficacy of blood flow–modifying drugs for use as adjuvants to existing cancer therapies.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".