Monte Carlo calculated doses to treatment volumes and organs at risk for permanent implant lung brachytherapy
Bibliographic record
Abstract
Iodine-125 ((125)I) and Caesium-131 ((131)Cs) brachytherapy have been used with sublobar resection to treat stage I non-small cell lung cancer and other radionuclides, (169)Yb and (103)Pd, are considered for these treatments. This work investigates the dosimetry of permanent implant lung brachytherapy for a range of source energies and various implant sites in the lung. Monte Carlo calculated doses are calculated in a patient CT-derived computational phantom using the EGsnrc user-code BrachyDose. Calculations are performed for (103)Pd, (125)I, (131)Cs seeds and 50 and 100 keV point sources for 17 implant positions. Doses to treatment volumes, ipsilateral lung, aorta, and heart are determined and compared to those determined using the TG-43 approach. Considerable variation with source energy and differences between model-based and TG-43 doses are found for both treatment volumes and organs. Doses to the heart and aorta generally increase with increasing source energy. TG-43 underestimates the dose to the heart and aorta for all implants except those nearest to these organs where the dose is overestimated. Results suggest that model-based dose calculations are crucial for selecting prescription doses, comparing clinical endpoints, and studying radiobiological effects for permanent implant lung 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".