Sci—Sat AM: Brachy — 01: Evaluation of dose‐volume metrics for microbeam radiation therapy
Bibliographic record
Abstract
Microbeam radiation therapy (MRT) is an experimental technique delivering an array of high dose synchrotron X-ray microbeams. Development of metrics to predict the biological efficacy of MRT dose distributions is needed to guide further MRT research and for potential translation to human trials. The most commonly used metric is the peak-to-valley-dose ratio (PVDR) relating the dose at the microbeam center to that between two microbeams. We investigate three additional metrics that characterize dose distributions from a more volumetric perspective - the peak-to-mean-valley-dose ratio (PMVDR), mean dose, and percentage volume below a threshold. The metrics are evaluated for Monte Carlo simulations of dose distributions in three cubic head phantoms (2, 4 and 8 cm side lengths) for microbeam widths of 25, 50, and 75 μm and centre-to-centre spacings of 100, 200 and 400 μm. The ratio of the PMVDR to the PVDR varied from 0.24 to 0.80 for the different configurations, indicating a difference in the predicted geometric dependence of outcome for these two metrics. The mean dose was 102, 79, and 42 % of the mean skin dose for the 2, 8, and 16 cm head phantoms, respectively. The percentage volume below a 10% dose threshold was highly dependent on geometry, with ranges for the different collimation configurations of 2 - 87% and 33 - 96% for the 2 and 16 cm heads, respectively. Different dose-volume metrics exhibit different dependencies on MRT geometry parameters, suggesting that reliance on PVDR as a predictor of therapeutic outcome may be insufficient.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".