Apical dose versus volume dose of Ruthenium-106 brachytherapy for uveal melanoma
Bibliographic record
Abstract
Objective Ruthenium-106 brachytherapy is commonly used to treat uveal melanomas. Most centres prescribe a radiation dose to the tumour apex that is calculated with the tumour located in the centre of the plaque. Recent work suggests that D 99% —the minimum radiation dose delivered to 99% of tumour volume—may be a better predictor of tumour control than apex dose. Both dosing regimens may be affected by tumour and treatment variables differently. We explored the effect of differences in these variables on volume and apex dose using a 3-dimensional planning model. Methods The time required to deliver 100 Gy to the tumour apices of representative tumours ranging from 2- to 6-mm thickness with central plaque positioning was calculated in Plaque Simulator™. This treatment time was used for further calculations, including D 99% with central plaque placement, and apical and tumour volume doses when tumour and plaque characteristics were altered, including eccentric plaque placement, either away from (tilt) or along (offset) scleral surface, tumour shape, and plaque type. Results D 99% was always greater than the apex dose when plaques were placed centrally, and the difference increased with tumour thickness. Increasing degrees of tumour offset reduced apical dose and D 99% , with a greater effect on apical dose for thicker and D 99% for thinner tumours, respectively. Differences in tumour shape and plaque type had idiosyncratic effects on apical and volume dosing. Conclusion D 99% and apex dose are affected by tumour and treatment characteristics in different ways, highlighting the complexity of radiation delivery to uveal tumours.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".