Vascular Strategies for Enhancing Tumour Response to Radiation Therapy
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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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".