Monte Carlo Models for Eye Plaque Brachytherapy Treatment of Iris Melanoma
Bibliographic record
Abstract
Eye plaque brachytherapy is an effective treatment for eye cancer that uses radioactive seeds (that emit low-energy photons) in an applicator. The purpose of this work is to model eye plaques used to treat iris melanoma and evaluate radiation dose (energy deposited per unit mass) using Monte Carlo simulations. The simulations are performed using the egs_brachy code using 125I (OncoSeed 6711) and 103Pd (TheraSeed 200) brachytherapy seeds. The plaques span 180°, 270°and 360° arcs and are modelled at the center of a 30 cm3 water volume. Dose was scored in a 2.55 cm3 sub-volume made up of 0.5mm3 voxels for simulations with 1010 histories. Voxelized three-dimensional (3D) dose distributions for each plaque are calculated and compared to published BrachyDose data (Thomson et al., 2010). egs_brachy calculated doses are compared to BrachyDose considering absolute dose in gray, dose as a percentage of prescription dose, and dose relative to the TG43 approach currently used in hospitals (Rivard et al., 2004). Statistical analyses show agreement within a 95% confidence interval with published data at 6 points of interest in the eye (cornea, sclera, lens, eye center, macula, and optic disk). Along the plaque central axis, egs_brachy and BrachyDose results agree within statistical uncertainties. In conclusion, eye plaques used for treatment of iris melanoma were successfully modelled and benchmarked. These models will be distributed (open source) with the egs_brachy code on GitHub, enabling state-of-the-art dose calculations in hospitals that will improve patient treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".