Vascular Strategies for Enhancing Tumour Response to Radiation Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Radiation therapy is prescribed to more than 50% of patients diagnosed with cancer. Although mechanisms of interaction between radiation and tumour cells are well understood on a molecular level, much remains uncertain concerning the interaction of radiation with the tumour as a whole. Recent studies have demonstrated that single large doses of radiation (8-20 Gy) may primarily target tumour endothelial cells, leading to secondary tumour clonogenic cell death. These studies suggest that blood vessels play an important role in radiation response. As a result, various strategies have been proposed to effectively combine radiation with vascular targeting agents. While most proposed schemes focus on methods to disrupt tumour blood vessels, recent evidence supporting that some anti-angiogenic agents may "normalize" tumour blood vessels, in turn enhancing tumour oxygenation and radiosensitivity, indicates that there may be more efficient strategies. Furthermore, vascular targeting agents have recently been demonstrated to enhance radiation therapy by targeting endothelial cells. When combined with radiation, these agents are believed to cause even more localized vascular destruction followed by tumour clonogenic cell death. Taken together, it is now crucial to elucidate the role of tumour blood vessels in radiation therapy response, in order to make use of this knowledge in developing therapeutic strategies that target tumour vasculature above and beyond classic clonogenic tumour cell death. In this report, we review some major developments in understanding the importance of tumour blood vessels during radiation therapy. A discussion of current imaging modalities used for studying vascular response to treatments will also be presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it