Monte Carlo calculation of dose distribution in early stage NSCLC patients planned for accelerated hypofractionated radiation therapy in the NCIC-BR25 protocol
Bibliographic record
Abstract
The dosimetric consequences of plans optimized using a commercial treatment planning system (TPS) for hypofractionated radiation therapy are evaluated by re-calculating with Monte Carlo (MC). Planning guidelines were in strict accordance with the Canadian BR25 protocol which is similar to the RTOG 0236 and 0618 protocols in patient eligibility and total dose, but has a different hypofractionation schedule (60 Gy in 15 fractions versus 60 Gy in 3 fractions). A common requirement of the BR25 and RTOG protocols is that the dose must be calculated by the TPS without tissue heterogeneity (TH) corrections. Our results show that optimizing plans using the pencil beam algorithm with no TH corrections does not ensure that the BR25 planning constraint of 99% of the PTV receiving at least 95% of the prescription dose would be achieved as revealed by MC simulations. This is due to poor modelling of backscatter and lateral electronic equilibrium by the TPS. MC simulations showed that as little as 75% of the PTV was actually covered by the 95% isodose line. The under-dosage of the PTV was even more pronounced if plans were optimized with the TH correction applied. In the most extreme case, only 23% of the PTV was covered by the 95% isodose.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".