Evaluation of dose-volume metrics for microbeam radiation therapy dose distributions in head phantoms of various sizes using Monte Carlo simulations
Bibliographic record
Abstract
This work evaluates four dose-volume metrics applied to microbeam radiation therapy (MRT) using simulated dosimetric data as input. We seek to improve upon the most frequently used MRT metric, the peak-to-valley dose ratio (PVDR), by analyzing MRT dose distributions from a more volumetric perspective. Monte Carlo simulations were used to calculate dose distributions in three cubic head phantoms: a 2 cm mouse head, an 8 cm cat head and a 16 cm dog head. The dose distribution was calculated for a 4 × 4 mm² microbeam array in each phantom, as well as a 16 × 16 mm² array in the 8 cm cat head, and a 32 × 32 mm² array in the 16 cm dog head. Microbeam widths of 25, 50 and 75 µm and center-to-center spacings of 100, 200 and 400 µm were considered. The metrics calculated for each simulation were the conventional PVDR, the peak-to-mean valley dose ratio (PMVDR), the mean dose and the percentage volume below a threshold dose. The PVDR ranged between 3 and 230 for the 2 cm mouse phantom, and between 2 and 186 for the 16 cm dog phantom depending on geometry. The corresponding ranges for the PMVDR were much smaller, being 2-49 (mouse) and 2-46 (dog), and showed a slightly weaker dependence on phantom size and array size. The ratio of the PMVDR to the PVDR varied from 0.21 to 0.79 for the different collimation configurations, indicating a difference between the geometric dependence on outcome that would be predicted by these two metrics. For unidirectional irradiation, the mean lesion dose was 102%, 79% and 42% of the mean skin dose for the 2 cm mouse, 8 cm cat and 16 cm dog head phantoms, respectively. However, the mean lesion dose recovered to 83% of the mean skin dose in the 16 cm dog phantom in intersecting cross-firing regions. 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 cm mouse and 16 cm dog heads, respectively. The results of this study illustrate that different dose-volume metrics exhibit different functional dependences on MRT geometry parameters, and suggest that reliance on the 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".