Evaluation of Dosimetric Effect of Bone Scatter on Nanoparticle-Enhanced Orthovoltage Radiotherapy: A Monte Carlo Phantom Study
Bibliographic record
Abstract
In nanoparticle (NP)-enhanced orthovoltage radiotherapy, bone scatter affected dose enhancement at the skin lesion in areas such as the forehead, chest wall, and knee. Since each of these treatment sites have a bone, such as the frontal bone, rib, or patella, underneath the skin lesion and this bone is not considered in dose delivery calculations, uncertainty arises in the evaluation of dose enhancement with the addition of NPs in radiotherapy. To investigate the impact of neglecting the effect of bone scatter, Monte Carlo simulations based on heterogeneous phantoms were carried out to determine and compare the dose enhancement ratio (DER), when a bone was and was not present underneath the skin lesion. For skin lesions with added NPs, Monte Carlo simulations were used to calculate the DER values using different elemental NPs (gold, platinum, silver, iodine, as well as iron oxide), in varying NP concentrations (3−40 mg/mL), at two different photon beam energies (105 and 220 kVp). It was found that DER values at the skin lesion increased with the presence of bone when there was a higher atomic number of NPs, a higher NP concentration, and a lower photon beam energy. When comparing DER values with and without bone, using the same NP elements, NP concentration, and beam energy, differences were found in the range 0.04−3.55%, and a higher difference was found when the NP concentration increased. By considering the uncertainty in the DER calculation, the effect of bone scatter became significant to the dose enhancement (>2%) when the NP concentration was higher than 18 mg/mL. This resulted in an underestimation of dose enhancement at the skin lesion, when the bone underneath the tumour was neglected during orthovoltage radiotherapy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".