Quantifying Reoxygenation in Pancreatic Cancer During Stereotactic Body Radiotherapy
Bibliographic record
Abstract
Hypoxia, the state of low oxygenation that often arises in solid tumours due to their high metabolism and irregular vasculature, is a major contributor to the resistance of tumours to radiation therapy (RT) and other treatments. Conventional RT extends treatment over several weeks or more, and nominally allows time for oxygen levels to increase ("reoxygenation") as cancer cells are killed by RT, mitigating the impact of hypoxia. Recent advances in RT have led to an increase in the use stereotactic body radiotherapy (SBRT), which delivers high doses in five or fewer fractions. For cancers such as pancreatic adenocarcinoma for which hypoxia varies significantly between patients, SBRT might not be optimal, depending on the extent to which reoxygenation occurs during its short duration. We used fluoro-5-deoxy-α-D-arabinofuranosyl)-2-nitroimidazole positron-emission tomography (FAZA-PET) imaging to quantify hypoxia before and after 5-fraction SBRT delivered to patient-derived pancreatic cancer xenografts orthotopically implanted in mice. An imaging technique using only the pre-treatment FAZA-PET scan and repeat dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans throughout treatment was able to predict the change in hypoxia. Our results support the further testing of this technique for imaging of reoxygenation in the clinic.
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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 | 0.000 |
| 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".