Evaluation of dose enhancement with gold nanoparticles in kilovoltage radiotherapy using the new EGS geometry library in Monte Carlo simulation
Bibliographic record
Abstract
Purpose This study compared the dose enhancement predicted in kilovoltage gold nanoparticle-enhanced radiotherapy using the newly developed EGS lattice and the typical gold-water mixture method in Monte Carlo simulation. This new method considered the gold nanoparticle-added volume consisting of solid nanoparticles instead of a gold-water mixture. In addition, this particle method is more realistic in simulation. Methods A heterogeneous phantom containing bone and water was irradiated by the 105 and 220 kVp x-ray beams. Gold nanoparticles were added to the tumour volume with concentration varying from 3–40 mg/mL in the phantom. The dose enhancement ratio (DER), defined as the ratio of dose at the tumour with and without adding gold nanoparticles, was calculated by the gold-water mixture and particle method using Monte Carlo simulation for comparison. Results It is found that the DER was 1.44–4.71 (105 kVp) and 1.27–2.43 (220 kVp) for the gold nanoparticle concentration range of 3–40 mg/mL, when they were calculated by the gold-water mixture method. The DER was slightly larger and equal to 1.47–4.84 (105 kVp) and 1.29–2.5 (220 kVp) for the same concentration range, when the particle method was used. Moreover, the DER predicted by both methods increased with an increase of nanoparticle concentration, and a decrease of x-ray beam energy. Conclusion The deviation of DER determined by the particle and gold-water mixture method was insignificant when considering the uncertainty in the calculation of DER (2%) in the nanoparticle concentration range of 3–40 mg/mL. It is therefore concluded that the gold-water mixture method could predict the dose enhancement as accurate as the newly developed particle method.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".