Dosimetry of interface region near closed air cavities for Co-60, 6 MV and 15 MV photon beams using Monte Carlo simulations
Bibliographic record
Abstract
Underdosing of treatment targets can occur in radiation therapy due to electronic disequilibrium around air-tissue interfaces when tumors are situated near natural air cavities. These effects have been shown to increase with the beam energy and decrease with the field size. Intensity modulated radiation therapy (IMRT) and tomotherapy techniques employ combinations of multiple small radiation beamlets of varying intensities to deliver highly conformal radiation therapy. The use of small beamlets in these techniques may therefore result in underdosing of treatment target in the air-tissue interfaces region surrounding an air cavity. This work was undertaken to investigate dose reductions near the air-water interfaces of 1x1x1 and 3x3x3 cm(3) air cavities, typically encountered in the treatment of head and neck cancer utilizing radiation therapy techniques such as IMRT and tomotherapy using small fields of Co-60, 6 MV and 15 MV photons. Additional investigations were performed for larger photon field sizes encompassing the entire air-cavity, such as encountered in conventional three dimensional conformal radiation therapy (3DCRT) techniques. The EGSnrc/DOSXYZnrc Monte Carlo code was used to calculate the dose reductions (in water) in air-water interface region for single, parallel opposed and four field irradiations with 2x2 cm(2) (beamlet), 10x2 cm(2) (fan beam), 5x5 and 7x7 cm(2) field sizes. The magnitude of dose reduction in water near air-water interface increases with photon energy; decreases with distance from the interface as well as decreases as the number of beams are increased. No dose reductions were observed for large field sizes encompassing the air cavities. The results demonstrate that Co-60 beams may provide significantly smaller interface dose reductions than 6 MV and 15 MV irradiations for small field irradiations such as used in IMRT and tomotherapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".