Dosimetric Investigation of Six Ru-106 Eye Plaques by EBT3 Radiochromic Films and Monte Carlo Simulation
Bibliographic record
Abstract
Background: Ophthalmic brachytherapy using radioactive plaques is an effective technique for the treatment of uveal melanoma. Ru-106 eye plaques are considered as interesting issue due to their steep gradient dose. The pre-planning evaluation of dosimetric parameters is essential for the treatment planning system. Objective: The current study aims at providing dose distributions of six Ru-106 eye plaques (CCA, CCB, CGD, CIB, COB and COD) using radiochromic EBT3 film, Geant4 Monte Carlo toolkit and the treatment planning software (Plaque Simulator). Material and Methods: In this experimental study, an in-house phantom was employed for depth dose measurements with EBT3 films. Also, Geant4.10.5 scoring mesh was implemented to obtain the 2D dose distribution of the plaques. The results were compared with Plaque Simulator software and the manufacturer's (BEBIG) data. The gamma index criterion (3%/3 mm) was used to evaluate dose distributions obtained by the film measurements and Geant4 simulation. Results: A good agreement was achieved between simulation and experimental results. Gamma index passing rate was 94.2%, 89.3%, 88.2%, 82.2%, 92.2% and 90.1% for CCA, CCB, CGD, CIB, COB and COD plaques, respectively. Absolute dose rate (mGy/min) obtained by EBT3 film at the depth of 2 mm was 79.4 mGy/min, 81.0 mGy/min, 78.6 mGy/min, 62.2 mGy/min, 75.2 mGy/min and 81.2 mGy/min for CCA, CCB, CGD, CIB, COB and COD plaques, respectively. Conclusion: The measured dose distributions and lateral dose profiles may be utilized in the treatment planning system to cover clinical volumes such as the clinical target volume and the gross tumor volume.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".