SU‐E‐T‐667: Radiosensitization Due to Gold Nanoparticles: A Monte Carlo Cellular Dosimetry Investigation of An Expansive Parameter Space
Bibliographic record
Abstract
Purpose: To investigate dose enhancement to cellular compartments following gold nanoparticle (GNP) uptake in tissue, varying cell and tissue morphology, intra and extracellular GNP distribution, and source energy using Monte Carlo (MC) simulations. Methods: Models of single and multiple cells are developed for normal and cancerous tissues; cells (outer radii 5–10 µm) are modeled as concentric spheres comprising the nucleus (radii 2.5–7.5 µm) and cytoplasm. GNP distributions modeled include homogeneous distributions throughout the cytoplasm, variable numbers of GNP‐containing endosomes within the cytoplasm, or distributed in a spherical shell about the nucleus. Gold concentrations range from 1 to 30 mg/g. Dose to nucleus and to cytoplasm for simulations including GNPs are compared to simulations without GNPs to compute Nuclear and Cytoplasm Dose Enhancement Factors (NDEF, CDEF). Photon source energies are between 20 keV and 1.25 MeV. Results: DEFs are highly sensitive to GNP intracellular distribution; for a 2.5 µm radius nucleus irradiated by a 30 keV source, NDEF varies from 1.2 for a single endosome containing all GNPs to 8.2 for GNPs distributed about the nucleus (7 mg/g). DEFs vary with cell dimensions and source energy: NDEFs vary from 2.5 (90 keV) to 8.2 (30 keV) for a 2.5 µm radius nucleus and from 1.1 (90 keV) to 1.3 (30 keV) for a 7.5 µm radius nucleus, both with GNPs in a spherical shell about the nucleus (7 mg/g). NDEF and CDEF are generally different within a single cell. For multicell models, the presence of gold within intervening tissues between source and target perturbs the fluence reaching cellular targets, resulting in DEF inhomogeneities within a population of irradiated cells. Conclusion: DEFs vary by an order of magnitude for different cell models, GNP distributions, and source energies, demonstrating the importance of detailed modelling for advancing GNP development for radiotherapy. Funding provided by the Natural Sciences and Engineering Council of Canada (NSERC), and the Canada Research Chairs Program (CRC)
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".