Optimization of photon beam energies in gold nanoparticle enhanced arc radiation therapy using Monte Carlo methods
Bibliographic record
Abstract
Abstract As a recent area of development in radiation therapy, gold nanoparticle (GNP) enhanced radiation therapy has shown potential to increase tumour dose while maintaining acceptable levels of healthy tissue toxicity. In this study, the effect of varying photon beam energy in GNP enhanced arc radiation therapy (GEART) is quantified through the introduction of a dose scoring metric, and GEART is compared to a conventional radiotherapy treatment. The PENELOPE Monte Carlo code was used to model several simple phantoms consisting of a spherical tumour containing GNPs (concentration: 15 mg Au g −1 tumour, 0.8 mg Au g −1 normal tissue) in a cylinder of tissue. Several monoenergetic photon beams, with energies ranging from 20 keV to 6 MeV, as well as 100, 200, and 300 kVp spectral beams, were used to irradiate the tumour in a 360° arc treatment. A dose metric was then used to compare tumour and tissue doses from GEART treatments to a similar treatment from a 6 MV spectrum. This was also performed on a simulated brain tumour using patient computed tomography data. GEART treatments showed potential over the 6 MV treatment for many of the simulated geometries, delivering up to 88% higher mean dose to the tumour for a constant tissue dose, with the effect greatest near a source energy of 50 keV. This effect is also seen with the inclusion of bone in a brain treatment, with a 14% increase in mean tumour dose over 6 MV, while still maintaining acceptable levels of dose to the bone and brain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".