SU-E-T-533: Surface Dosimetry in the Presence of a Bone in Skin Radiotherapy: A Monte Carlo Study Using KV Photon Beams
Bibliographic record
Abstract
Purpose: This study examined the variation of surface dose in the presence of a bone located under the surface of patient (e.g. sites of forehead, chest wall and kneecap) in skin radiotherapy. A 105 kVp photon beam was used, and simulated by Monte Carlo method using the EGSnrc code. Methods: Heterogeneous phantoms containing a water layer (thickness = 0.5−5 mm) over a bone with thickness equal to 1 cm were irradiated by a clinical 105 kVp photon beam (Gulmay D3225 x-ray machine). The field size was 5 cm diameter and the source-to-surface distance of the beam was 20 cm. Phase-space file of the photon beam was generated using the BEAMnrc code and verified by measurements. Surface doses for different phantom geometries were calculated using the DOSXYZnrc code. For comparison, all Monte Carlo simulations were repeated in a water phantom with the same dimension of the corresponding heterogeneous phantom. Results: With the presence of a bone under a water layer of 1 mm thickness, surface dose was found decreased 6.3% using the 105 kVp photon beam. This is due to the loss of backscatter from the bone in the beam irradiation. When the water thickness increased to 3 and 5 mm, the surface dose reduction decreased to 4.7% and 3.4%, respectively. This shows that the dosimetric impact due to the presence of bone decreased, when the bone was located further away from the phantom surface. Conclusion: Surface dose reduction was found in the presence of a bone under the skin tissue. Since such dose reduction is not considered in the dose calculation based on the absolute dose calibration using a homogeneous water phantom, an overestimation of dose occurs when it is prescribed. This results in a dosimetric uncertainty with a variation of tissue thickness in skin radiotherapy.
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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 | 0.000 | 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".