SU‐E‐T‐30: A Factor for Converting Dose to a Gold Nanoparticle Mixture to a Biologically‐Relevant Dose
Bibliographic record
Abstract
Purpose: Monte Carlo studies of gold nanoparticle (GNP) dose enhancement on macroscopic scales in radiotherapy have modeled GNPs in tissue as a homogeneous mixture of gold and tissue. Using an explicit model of GNPs randomly positioned in a small volume (1 µm3) of tissue, this study aims to quantify the dose to the biologically relevant component of a goldtissue mixture, enabling a conversion from macroscopically‐scored dose. Methods: Using the PENELOPE Monte Carlo code with the penEasy package, we modeled a 1 µm3 volume containing either a tissue‐gold mixture or GNPs suspended in ICRU 4‐component tissue at various gold concentrations (0, 5, 10, and 15 mg Au/g tissue) and GNP diameters (20, 30, 40, 50 nm). The volume was irradiated with monoenergetic photon and electron beams, ranging from 110 eV to 6 MeV. Interaction forcing was utilized to increase simulation efficiency. Energy deposition was scored in the tissue for each case and was converted to dose. For each scenario, we calculated a conversion factor, the ratio of dose‐to‐tissue to dose‐to‐mixture as a function of energy. Results: The conversion factor was plotted as a function of energy for both photons and electrons. For electrons, the conversion factor was relatively unaffected by any of the parameters, including energy, ranging between 0.98–1.02. For photons, the factor was very energy dependent, with a range of 0.49–1.02. The factor was lowest for 10–100 keV photons. The conversion factor generally decreased with increasing GNP concentration and increasing GNP size. Conclusion: With a large variation in the conversion factor with incident energy, dose deposition is dependent on the spectrum incident on a volume. By scoring the energy spectrum in a given volume, one can provide a scenario‐specific conversion factor, allowing fast, detailed Monte Carlo simulations without the need for explicit GNP‐definition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".