First Evaluation of Temporal and Spatial Fractionation in Proton Minibeam Radiation Therapy of Glioma-Bearing Rats
Bibliographic record
Abstract
(1) Background: Proton minibeam radiation therapy (pMBRT) is a new radiotherapy technique using spatially modulated narrow proton beams. pMBRT results in a significantly reduced local tissue toxicity while maintaining or even increasing the tumor control efficacy as compared to conventional radiotherapy in small animal experiments. In all the experiments performed up to date in tumor bearing animals, the dose was delivered in one single fraction. This is the first assessment on the impact of a temporal fractionation scheme on the response of glioma-bearing animals to pMBRT. (2) Methods: glioma-bearing rats were irradiated with pMBRT using a crossfire geometry. The response of the irradiated animals in one and two fractions was compared. An additional group of animals was also treated with conventional broad beam irradiations. (3) Results: pMBRT delivered in two fractions at the biological equivalent dose corresponding to one fraction resulted in the highest median survival time, with 80% long-term survivors free of tumors. No increase in local toxicity was noted in this group with respect to the other pMBRT irradiated groups. Conventional broad beam irradiations resulted in the most severe local toxicity. (4) Conclusion: Temporal fractionation increases the therapeutic index in pMBRT and could ease the path towards clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".