Dosimetric impact of physics libraries for electronic brachytherapy Monte Carlo studies
Bibliographic record
Abstract
Abstract Background Low‐energy x‐ray beams used in electronic brachytherapy (eBT) present significant dosimetric challenges due to their high depth‐dose gradients, the dependence of detector response on materials, and the lack of standardized dose‐to‐water references. These challenges have driven the need for Monte Carlo (MC) simulations to ensure accurate dosimetry. However, discrepancies in the physics models used by different MC systems have raised concerns about their dosimetric consistency, particularly in modeling bremsstrahlung interactions. Purpose To assess the dosimetric impact of using different physics approaches in three state‐of‐the‐art MC systems for eBT, focusing on the disagreements observed when different MC methods are used to evaluate bremsstrahlung interactions. Methods The MC studies of the Axxent S700, the Esteya, and the INTRABEAM eBT systems were performed using two EGSnrc applications (egs_brachy and egs_kerma), TOPAS, and PENELOPE‐2018 (PEN18). The fluence spectra and depth doses were compared for simplified x‐ray tube models, which maintain the target mode (transmission or reflection), the target material and thickness, and the surface applicators’ source‐to‐surface distance. An extra simulation was made to evaluate the utility of the simplified models as proxies in predicting the most important characteristics of an accurate applicator's simulation (detailed model of INTRABEAM's 30 mm surface applicator). The EGSnrc applications and PEN18 utilized their default bremsstrahlung angular emission approaches. TOPAS used two physics lists: g4em‐livermore (TOPASliv) and g4em‐penelope (TOPASpen). Results The most significant differences between MC codes were observed for the transmission target mode. The bremsstrahlung component of the fluence spectra differed by about 15% on average, comparing PEN18, EGSnrc applications, and TOPASliv, with PEN18's fluences consistently lower. EGSnrc and PEN18 agreed within 3% for their characteristic spectrum components. However, PEN18's characteristic lines overreached TOPASliv’s by 40%. Those spectral characteristics generated depth dose differences, where PEN18, on average, scored 9% lower than EGSnrc and TOPASliv. Considering TOPASpen in the transmission mode, PEN18's fluence spectrum presented a lower bremsstrahlung (5%) but a higher characteristic component (10%); these spectral differences compensated, generating depth dose differences within 1% average. In the reflection target mode, EGSnrc and PEN18 agreed within 4% for the bremsstrahlung and characteristic components of the fluence spectra. With TOPASpen in the reflection mode, PEN18 presents 12% lower fluences in the bremsstrahlung component but 6% higher characteristic lines. This spectral behavior diminished the depth dose differences up to 3%. Conclusion This work found considerable disagreements between three state‐of‐the‐art MC systems commonly used in medical applications when simulating bremsstrahlung in eBT. The differences arose when the bremsstrahlung angular distribution and the atomic relaxation processes in the target became relevant. More theoretical and experimental studies are necessary to evaluate the impact of these differences on related calculations.
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.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".