SU‐E‐T‐495: Monte Carlo Simulations of Relative DNA Damage From KV CBCT Radiation
Bibliographic record
Abstract
Purpose: Daily imaging protocols using kilovoltage cone beam CT (kV CBCT) have the potential to accumulate non‐trivial doses (up to several percent of the prescription dose) to the imaged volume which could have implications for planning decisions or the induction of secondary cancers. When CBCT dose is added to therapeutic dose, it may be necessary to account for differences in the relative biological effectiveness (RBE). We conducted a Monte Carlo study to investigate the relative number and complexity of DNA double strand breaks (DSBs) that Result from kV CBCT radiation compared to Co‐60 as a reference for general external beam radiation. Methods: A method was developed for coupling the radiation transport code (PENELOPE) with a Monte Carlo Damage Simulation (MCDS) code that predicts relative numbers and complexity of DSBs. The PENELOPE simulations generated electron spectra relevant for the MCDS input consisting of (i) electrons generated outside of a cell nucleus that drift in and (ii) electrons created within the nuclear volume, both without the continued slowing within the nucleus itself that is simulated implicitly by the MCDS code. Various configurations were considered (surface/depth, bowtie filter thickness, kV energy, and degrees of oxygenation). Results: The RBE for DSB induction in the kV CBCT sources ranged from 1.14 to 1.19 with increased complexity compared to that of Co‐60 for the majority of conditions; however, values as high as 1.45 were calculated in extreme circumstances. The RBE increased for anoxic conditions, at the phantom surface, for lower energies and for less filtration. Conclusions:Our results suggest kV CBCT sources produce more DNA damage than therapeutic radiation for a given absorbed dose and the relative difference can depend on several factors. This has potential implications for calculating the probability for inducing secondary cancers through the use of image guidance radiation.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".