Radiotherapeutic enhancement using ultrasound-stimulated microbubbles: a critical review
Bibliographic record
Abstract
PURPOSE: Ultrasound stimulated microbubbles (USMB) are proposed as radioenhancing agents. Acting mechanically, they are attractive because their effects can be localized to the tumor, limiting the potential for normal tissue toxicity. Extensive preclinical research in models of human cancers has demonstrated increased tumor control when USMB are combined with radiotherapy compared with radiation alone, which has led to recent Phase I trials. The leading theory on the radioenhancement mechanism of action (MOA) is that USMB act as vascular disrupting agents, but others are proposed. MATERIALS AND METHODS: Current literature was reviewed with a focus on the role of the tumor vasculature on radiotherapy response, the bioeffects of USMB on the vasculature, and studies of USMB as radioenhancers. Additionally, the possible interplay between USMB as vascular modulators, and radiation-induced anti-tumor immunity, is explored. RESULTS: Whilst most preclinical evidence compellingly describes the radioenhancement effect, only one study considers the immune cell infiltration post USMB plus radiotherapy, with non-significant findings. Clinical studies demonstrate the safety of USMB. As a monotherapy, USMB can alter tumor immune microenvironments and induce a variety of bioeffects on the vasculature, depending on the stimulatory acoustic parameters. Treatment parameters used to study the effects of USMB alone, and with radiotherapy, vary in the literature making direct comparisons difficult. CONCLUSIONS: Further work exploring USMB for radioenhancement is warranted. Elucidation of the MOA is required to support clinical translation, particularly with a view to optimize treatment parameters.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".