New developments in hypoxia-directed patient selection and stratification in radiotherapy
Bibliographic record
Abstract
PURPOSE: Tumor hypoxia is a negative prognostic factor that causes radiotherapy resistance. Decades of clinical trials employing various hypoxia intervention strategies have had limited impact on daily clinical practise. This is largely due to modest benefits of hypoxia modification in unselected patient populations, combined with higher toxicity and increases in cost and time. However, numerous studies have employed post-hoc analysis to demonstrate a benefit of hypoxia intervention in patients with the most hypoxic tumors, and these benefits are of sufficient magnitude to warrant further pursuit. For the first time, we have recently seen the emergence of interventional trials with patient selection or stratification based on tumor hypoxia biomarkers. The purpose of this mini-review is to present the design and results from these recent trials, and highlight their impact in propelling this field forward. CONCLUSIONS: Recent trials employing patient selection based on hypoxia biomarkers have investigated the effects of dose (distribution) modifications, and drug-induced tumor reoxygenation or radiosensitization. Encouraging results from some approaches have laid the foundation for larger follow-up studies that have the potential to change clinical practice. These clinical trials set an important precedent for future trial design and help guide the path for the future of hypoxia-directed radiotherapy.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".