A review of vascular disrupting agents as a concomitant anti-tumour modality with radiation
Bibliographic record
Abstract
Abstract Background Tumour vasculature plays an important role in the development, maintenance and sustainability of a tumour. Endothelial cells which are recruited into the tumour stroma facilitate the formation of essential blood vessels that deliver nutrients and oxygen to tumour cells. A growing body of research is showing that there are synergistic anti-tumour effects when anti-vascular agents are combined with radiation. More recent reports have described favourable radiation response as a function of vascular targeting and blood vessel breakdown, primarily through interactions of radiation with vascular endothelial cells. Vascular disrupting agents are being utilised in several forms that include molecular targeting, biophysical assault and biological interference. Purpose In the present review, we examine current advances in anti-vascular agents to enhance tumour response when combined with radiation therapy. Methods A comprehensive literature search was conducted on the US National Library of Medicine, National Institutes of Health (PubMed) using the following search keywords: vascular disrupting agents, radiation sensitisation, anti-angiogenic therapy, anti-vascular therapy, radiation therapy. Conclusion Current research suggests the applicability of vascular disrupting agents as an effective radiation sensitisation agent. Pre-clinical and clinical trials have been well developed to form the theoretical framework to apply this powerful modality to the treatment of cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".