Evaluation of dose enhancement with gold nanoparticles in kilovoltage radiotherapy using the new EGS geometry library in Monte Carlo simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<abstract><sec> <title>Purpose</title> <p>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.</p> </sec><sec> <title>Methods</title> <p>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.</p> </sec><sec> <title>Results</title> <p>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.</p> </sec><sec> <title>Conclusion</title> <p>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.</p> </sec></abstract>
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it