A method for converting dose-to-medium to dose-to-tissue in Monte Carlo studies of gold nanoparticle-enhanced radiotherapy
Bibliographic record
Abstract
Gold nanoparticles (GNPs) have shown potential in recent years as a means of therapeutic dose enhancement in radiation therapy. However, a major challenge in moving towards clinical implementation is the exact characterisation of the dose enhancement they provide. Monte Carlo studies attempt to explore this property, but they often face computational limitations when examining macroscopic scenarios. In this study, a method of converting dose from macroscopic simulations, where the medium is defined as a mixture containing both gold and tissue components, to a mean dose-to-tissue on a microscopic scale was established. Monte Carlo simulations were run for both explicitly-modeled GNPs in tissue and a homogeneous mixture of tissue and gold. A dose ratio was obtained for the conversion of dose scored in a mixture medium to dose-to-tissue in each case. Dose ratios varied from 0.69 to 1.04 for photon sources and 0.97 to 1.03 for electron sources. The dose ratio is highly dependent on the source energy as well as GNP diameter and concentration, though this effect is less pronounced for electron sources. By appropriately weighting the monoenergetic dose ratios obtained, the dose ratio for any arbitrary spectrum can be determined. This allows complex scenarios to be modeled accurately without explicitly simulating each individual GNP.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".