Sci—Sat AM: Brachy — 11: Improving treatment planning for I‐125 lung brachytherapy using Monte Carlo methods
Bibliographic record
Abstract
125I brachytherapy used in conjunction with sublobar resection to treat stage I non‐small cell lung cancer has been reported to improve disease‐free and overall survival rates compared with resection alone. Treatments are planned intra‐operatively using seed spacing nomograms or tables to achieve a prescription dose defined 5 mm above the implant plane. Dose distributions for patients treated with this technique at the Mayo Clinic Rochester were reanalyzed using a Monte Carlo (MC) calculation; significant differences were observed between the standard TG‐43 dose calculations and the actual dose delivered as determined by MC. This work investigates differences between TG‐43 calculated prescription doses and those calculated in more accurate models. Monte Carlo calculations are performed using the EGSnrc user‐code BrachyDose with a number of lung tissue phantom models including patient CT‐derived phantoms. Seed spacing nomograms using these models are recalculated by determining the dose to the prescription point using the activities per seed required to produce a prescription dose of 100 Gy with the TG‐43 point source formalism. Models using nominal density lung or CT‐derived density lung tissue result in a significant increase in dose to the prescription point (up to approximately 25%) compared to TG‐43 calculated doses. The differences observed suggest that patients routinely receive significantly higher doses than planned using TG‐43 derived nomograms. Additionally, deviation from TG‐43 increases as seed spacing increases. Media heterogeneities significantly affect dose distributions and prescription doses for 125I lung brachytherapy, underlining the importance of using model‐based dose calculation algorithms to plan and analyze these treatments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".