Sci—Thur AM: YIS ‐ 04: Gold Nanoparticle Enhanced Arc Radiotherapy: A Monte Carlo Feasibility Study
Bibliographic record
Abstract
Introduction: The use of gold nanoparticles (GNPs) in radiotherapy has shown promise for therapeutic enhancement. In this study, we explore the feasibility of enhancing radiotherapy with GNPs in an arc‐therapy context. We use Monte Carlo simulations to quantify the macroscopic dose‐enhancement ratio (DER) and tumour to normal tissue ratio (TNTR) as functions of photon energy over various tumour and body geometries. Methods: GNP‐enhanced arc radiotherapy (GEART) was simulated using the PENELOPE Monte Carlo code and penEasy main program. We simulated 360° arc‐therapy with monoenergetic photon energies 50 – 1000 keV and several clinical spectra used to treat a spherical tumour containing uniformly distributed GNPs in a cylindrical tissue phantom. Various geometries were used to simulate different tumour sizes and depths. Voxel dose was used to calculate DERs and TNTRs. Inhomogeneity effects were examined through skull dose in brain tumour treatment simulations. Results: Below 100 keV, DERs greater than 2.0 were observed. Compared to 6 MV, tumour dose at low energies was more conformai, with lower normal tissue dose and higher TNTRs. Both the DER and TNTR increased with increasing cylinder radius and decreasing tumour radius. The inclusion of bone showed excellent tumour conformality at low energies, though with an increase in skull dose (40% of tumour dose with 100 keV compared to 25% with 6 MV). Conclusions: Even in the presence of inhomogeneities, our results show promise for the treatment of deep‐seated tumours with low‐energy GEART, with greater tumour dose conformality and lower normal tissue dose than 6 MV.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".