SU‐G‐TeP3‐13: The Role of Nanoscale Energy Deposition in the Development of Gold Nanoparticle‐Enhanced Radiotherapy
Bibliographic record
Abstract
Purpose: Gold nanoparticles (GNPs) can enhance radiotherapy effects. The high photoelectric cross section of gold relative to tissue, particularly at lower energies, leads to localized dose enhancement. However in a clinical context, photon energies must also be sufficient to reach a target volume at a given depth. These properties must be balanced to optimize such a therapy. Given that nanoscale energy deposition patterns around GNPs play a role in determining biological outcomes, in this work we seek to establish their role in this optimization process. Methods: The PENELOPE Monte Carlo code was used to generate spherical dose deposition kernels in 1000 nm diameter spheres around 50 nm diameter GNPs in response to monoenergetic photons incident on the GNP. Induced “lesions” were estimated by either a local effect model (LEM) or a mean dose model (MDM). The ratio of these estimates was examined for a range of photon energies (10 keV to 2 MeV), for three sets of linear‐quadratic parameters. Results: The models produce distinct differences in expected lesion values, the lower the alpha‐beta ratio, the greater the difference. The ratio of expected lesion values remained constant within 5% for energies of 40 keV and above across all parameter sets and rose to a difference of 35% for lower energies only for the lowest alpha‐beta ratio. Conclusion: Consistent with other work, these calculations suggest nanoscale energy deposition patterns matter in predicting biological response to GNP‐enhanced radiotherapy. However the ratio of expected lesions between the different models is largely independent of energy, indicating that GNP‐enhanced radiotherapy scenarios can be optimized in photon energy without consideration of the nanoscale patterns. Special attention may be warranted for energies of 20 keV or below and low alpha‐beta ratios.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".