Poster — Thur Eve — 70: Quantification of tumour dose enhancement at kilo‐voltage energies due to the presence of gold nanoparticles during radiation therapy: EGSnrcMP Monte Carlo study
Bibliographic record
Abstract
One of the greatest challenges in radiation therapy is the ability to deliver a lethal dose of radiation to a tumour while sparing the surrounding normal tissues. In theory, the dose delivered to a tumour during photon-based radiation therapy can be enhanced by loading high atomic number (Z) materials into the tumour, which results in greater photoelectric absorption and hence increased photoelectron fluence within the tumour than in surrounding tissues. The EGSnrcMP Monte Carlo code, together with DOSXYZnrc, a three-dimensional voxel dose calculation module has been used to study the macroscopic dose enhancement factor (MDEF) in a tumour infused with gold nanoparticles at the kilo-voltage energies. We observed that gold nanoparticles infused in a tumour irradiated with kilo-voltage energies has the potential to enhance the tumour dose by a factor ranging from 0.25 to about 5 depending on the mean energy of the beam and the concentration of gold nanoparticles in the tumour. The increase in dose can be attributed to the significant increase in the photoelectron fluence within the tumour loaded with gold particles during the irradiation. Future studies will involve the characterization of the MDEF at megavoltage energies.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".