Skin dose enhancement from the application of skin-care creams using FF and FFF photon beams in radiotherapy: A Monte Carlo phantom evaluation
Bibliographic record
Abstract
Skin-care cream is commonly applied to relieve skin redness in radiotherapy. However, using cream on the patient under the photon field would increase the skin dose in delivery. The aim of this study is to evaluate the dependences of skin dose enhancement on different beam and cream variables using Monte Carlo simulation. Using a solid water phantom with water-equivalent bolus, PMMA layer and cream layer, we irradiated it by 6 MV photon beams. Skin doses were calculated by varying the beam quality (flattening-filter (FF) and flattening-filter-free (FFF)), beam angle (0°–80°), skin-care cream type (water-based and silicon-based) and cream thickness (0–3 mm) using the EGSnrc Monte Carlo code. The densities of the water- and silicon-based cream were 0.92 and 1.14 g/cm<sup>3</sup>. The dose enhancement factor (DEF) defined as the ratio of the skin dose with skin-care cream to the skin dose without cream was calculated. It is found that the FF photon beam had higher DEF value than the FFF. For the water-based cream of 3 mm, the DEF for the FF beam was about 22.1% higher than that of the FFF. While for the silicon-based cream with the same thickness, the DEF was 24.2% higher. DEF value also increased with the beam angle. The DEF values were from 1.4 to 2.52 (water-based cream) and 1.42 to 2.68 (silicon-based cream) when the beam angle was increased from 20° to 80° using the 6 MV FF beams. Similarly, for the 6 MV FFF beams, the DEF values increased from 1.29 to 2.07 and 1.30 to 2.18 for the water- and silicon-based cream. These simulation results showed that the skin dose enhancement increased with an increase of beam angle, cream thickness, cream density, and the irradiation of FFF photon beams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".