RBE of kV CBCT radiation determined by Monte Carlo DNA damage simulations
Bibliographic record
Abstract
Due to the higher LET of kilovoltage (kV) radiation, there is potential for an increase in relative biological effectiveness (RBE) of absorbed doses of radiation from kV cone beam computed tomography (CBCT) sources in reference to megavoltage or Co-60 doses. This work develops a method for accurately coupling a Monte Carlo (MC) radiation transport code (PENELOPE) with the damage simulation (MCDS) to predict relative numbers of DNA double strand breaks (DSBs). The MCDS accounts for slowing down of electrons and delta ray production within the cell nucleus; however, determining the spectrum of electrons incident on the cell nucleus from photons interacting in a larger region of tissue is not trivial. PENELOPE simulations were conducted with a novel tally algorithm invoked where electrons incident on a detection material were tracked and both the incident energy and the final deposited dose were recorded. The DSB yield predicted by a set of MCDS runs of monoenergetic electrons was then looked up in a table and weighted by the specific energy of the incident electron. Our results indicate that the RBE for DSB induction is 1.1 for diagnostic x-rays with energies from 80 to 125 kVp. We found no significant change in RBE with depth or filtration. The predicted absolute DSB yields are about three times lower for cells irradiated under anoxic conditions than the yield in cells irradiated under normoxic (5%) or fully aerobic (100%) conditions. However, oxygen concentration has a negligible (± 0.02) effect on the RBE of kV CBCT x-rays.
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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 | 0.003 |
| 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 | 0.001 | 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".