SU‐E‐T‐494: Variation of Mucosal Dose in Head‐And‐Neck Radiotherapy: A Phantom Study Using Monte Carlo Simulation
Bibliographic record
Abstract
Purpose: This phantom study investigated variations of mucosal dose on photon beam energy, beam angle, multi‐beam configuration and mucosal thickness, when using small photon fields in head‐and‐neck radiotherapy. Methods: Cylindrical mucosa phantoms with bone and air heterogeneities were created with mucosal thickness (normal tissue) equal to 1, 2 and 3 mm. For dosimetric comparison, corresponding homogeneous phantom with all heterogeneities replaced by normal tissue was also used. These phantoms were irradiated by photon beams with field size = 1 × 1 cm2. Beam energies of 6 and 18 MV were used with beam angles varied to 0°, 90° and 180°. Moreover, multi‐beam configurations of 2, 4 and 8 beams were used, and doses along the central‐beam axis in the mucosal tissue were calculated using Monte Carlo simulations (EGSnrc code). Results: For beam angle equal to 0°, the mucosal surface doses decreased slightly with an increase of the mucosal thickness (1–3 mm), while the surface dose of the 6 MV photon beam was decreased more significantly than the 18 MV. For beam angle equal to 180°, variation of muscosal surface dose with its thickness was found insignificant. For different multi‐beam configurations, it was found that the variation of mucosal dose on its thickness became insignificant when the number of photon beams around the mucosa was increased. In addition, the change of mucosal dose due to the bone and air heterogeneities depended on the photon beam energy, beam angle and muscosal thickness. Conclusions: It is concluded that mucosal dose depends on variations of beam energy, beam angle, multi‐beam configuration and mucosal thickness for small photon fields. The dosimetric information in this study should be considered in studying the mucosal complications in head‐and‐neck IMRT, so that an optimized treatment strategy to minimize mucosal complications can be developed. This study is supported by the Dean’s Fund Grant in the University of Toronto.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".