Radiation combined with ultrasound and microbubbles: A potential novel strategy for cancer treatment
Bibliographic record
Abstract
Cancer is one of the leading causes of death worldwide. Several emerging technologies are helping to battle cancer. Cancer therapies have been effective at killing cancer cells, but a large portion of patients still die to this disease every year. As such, more aggressive treatments of primary cancers are employed and have been shown to be capable of saving a greater number of lives. Recent research advances the field of cancer therapy by employing the use of physical methods to alter tumor biology. It uses microbubbles to enhance radiation effect by damaging tumor vasculature followed by tumor cell death. The technique can specifically target tumor volumes by conforming ultrasound fields capable of microbubbles stimulation and localizing it to avoid vascular damage in surrounding tissues. Thus, this new application of ultrasound-stimulated microbubbles (USMB) can be utilized as a novel approach to cancer therapy by inducing vascular disruption resulting in tumor cell death. Using USMB alongside radiation has showed to augment the anti-vascular effect of radiation, resulting in enhanced tumor response. Recent work with nanobubbles has shown vascular permeation into intracellular space, extending the use of this new treatment method to potentially further improve the therapeutic effect of the ultrasound-based therapy. The significant enhancement of localized tumor cell kill means that radiation-based treatments can be made more potent with lower doses of radiation. This technique can manifest a greater impact on radiation oncology practice by increasing treatment effectiveness significantly while reducing normal tissue toxicity. This review article summarizes the past and recent advances in USMB enhancement of radiation treatments. The review mainly focuses on preclinical findings but also highlights some clinical findings that use USMB as a therapeutic modality in cancer therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".