Delivered dose uncertainty analysis at the tumor apex for ocular brachytherapy
Bibliographic record
Abstract
PURPOSE: To estimate the total dosimetric uncertainty at the tumor apex for ocular brachytherapy treatments delivered using 16 mm Collaborative Ocular Melanoma Study (COMS) and Super9 plaques loaded with (125)I seeds in order to determine the size of the apex margin that would be required to ensure adequate dosimetric coverage of the tumor. METHODS: The total dosimetric uncertainty was assessed for three reference tumor heights: 3, 5, and 10 mm, using the Guide to the expression of Uncertainty in Measurement/National Institute of Standards and Technology approach. Uncertainties pertaining to seed construction, source strength, plaque assembly, treatment planning calculations, tumor height measurement, plaque placement, and plaque tilt for a simple dome-shaped tumor were investigated and quantified to estimate the total dosimetric uncertainty at the tumor apex. Uncertainties in seed construction were determined using EBT3 Gafchromic film measurements around single seeds, plaque assembly uncertainties were determined using high resolution microCT scanning of loaded plaques to measure seed positions in the plaques, and all other uncertainties were determined from the previously published studies and recommended values. All dose calculations were performed using plaque simulator v5.7.6 ophthalmic treatment planning system with the inclusion of plaque heterogeneity corrections. RESULTS: The total dosimetric uncertainties at 3, 5, and 10 mm tumor heights for the 16 mm COMS plaque were 17.3%, 16.1%, and 14.2%, respectively, and for the Super9 plaque were 18.2%, 14.4%, and 13.1%, respectively (all values with coverage factor k = 2). The apex margins at 3, 5, and 10 mm tumor heights required to adequately account for these uncertainties were 1.3, 1.3, and 1.4 mm, respectively, for the 16 mm COMS plaque, and 1.8, 1.4, and 1.2 mm, respectively, for the Super9 plaque. These uncertainties and associated margins are dependent on the dose gradient at the given prescription depth, thus resulting in the changing uncertainties and margins with depth. CONCLUSIONS: The margins determined in this work can be used as a guide for determining an appropriate apex margin for a given treatment, which can be chosen based on the tumor height. The required margin may need to be increased for more complex scenarios (mushroom shaped tumors, tumors close to the optic nerve, oblique muscle related tilt, etc.) than the simple dome-shaped tumor examined and should be chosen on a case-by-case basis. The sources of uncertainty contributing most significantly to the total dosimetric uncertainty are seed placement within the plaques, treatment planning calculations, tumor height measurement, and plaque tilt. This work presents an uncertainty-based, rational approach to estimating an appropriate apex margin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".