Generation and comparison of 3D dosimetric reference datasets for COMS eye plaque brachytherapy using model‐based dose calculations
Bibliographic record
Abstract
PURPOSE: A joint Working Group of the American Association of Physicists in Medicine (AAPM), the European Society for Radiotherapy and Oncology (ESTRO), and the Australasian Brachytherapy Group (ABG) was created to aid in the transition from the AAPM TG-43 dose calculation formalism, the current standard, to model-based dose calculations. This work establishes the first test cases for low-energy photon-emitting brachytherapy using model-based dose calculation algorithms (MBDCAs). ACQUISITION AND VALIDATION METHODS: I brachytherapy seed in water, 13 seeds (2) individually and (3) in combination in water, (4) the full Collaborative Ocular Melanoma Study (COMS) 16 mm eye plaque in water, and (5) the full plaque in a realistic eye phantom. Calculations are done with four Monte Carlo (MC) codes and a research version of a commercial treatment planning system (TPS). For all test cases, local agreement of MC codes was within ∼2.5% and global agreement was ∼2% (4% for test case 5). MC agreement was within expected uncertainties. Local agreement of TPS with MC was within 5% for test case 1 and ∼20% for test cases 4 and 5, and global agreement was within 0.4% for test case 1 and 10% for test cases 4 and 5. DATA FORMAT AND USAGE NOTES: Dose distributions for each set of MC and TPS calculations are available online (https://doi.org/10.52519/00005) along with input files and all other information necessary to repeat the calculations. POTENTIAL APPLICATIONS: These data can be used to support commissioning of MBDCAs for low-energy brachytherapy as recommended by TGs 186 and 221 and AAPM Report 372. This work additionally lays out a sample framework for the development of test cases that can be extended to other applications beyond eye plaque brachytherapy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".