Small animal radiotherapy research platforms
Bibliographic record
Abstract
Advances in conformal radiation therapy and advancements in pre-clinical radiotherapy research have recently stimulated the development of precise micro-irradiators for small animals such as mice and rats. These devices are often kilovolt x-ray radiation sources combined with high-resolution CT imaging equipment for image guidance, as the latter allows precise and accurate beam positioning. This is similar to modern human radiotherapy practice. These devices are considered a major step forward compared to the current standard of animal experimentation in cancer radiobiology research. The availability of this novel equipment enables a wide variety of pre-clinical experiments on the synergy of radiation with other therapies, complex radiation schemes, sub-target boost studies, hypofractionated radiotherapy, contrast-enhanced radiotherapy and studies of relative biological effectiveness, to name just a few examples. In this review we discuss the required irradiation and imaging capabilities of small animal radiation research platforms. We describe the need for improved small animal radiotherapy research and highlight pioneering efforts, some of which led recently to commercially available prototypes. From this, it will be clear that much further development is still needed, on both the irradiation side and imaging side. We discuss at length the need for improved treatment planning tools for small animal platforms, and the current lack of a standard therein. Finally, we mention some recent experimental work using the early animal radiation research platforms, and the potential they offer for advancing radiobiology research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".