Monte Carlo simulation of a novel medical linac concept for highly conformal x-ray FLASH cancer radiotherapy
Bibliographic record
Abstract
A growing body of pre-clinical research has demonstrated the potential of ultra-high dose-rate (UHDR) radiotherapy to reduce normal tissue toxicity while maintaining tumor control. However, owing to a wide range of technical difficulties, no existing x-ray systems are capable of highly conformal UHDR radiotherapy to humans. In this work, we designed and simulated a novel x-ray UHDR system representing a next-generation solution for rapid and highly conformal treatment delivery. This system comprises 16 stationary beamlines employing a new class of highly efficient linear accelerator to generate 12 MeV electron beams electromagnetically steered onto a bremsstrahlung target, a collimator with channels to create a divergent array of x-ray beamlets, and a translating patient couch. The system design was tuned to maximize the dose rate while minimizing beam penumbra and cross-channel leakage. A simulation framework was developed to facilitate iteration of machine parameters during optimization. A treatment plan for a case of locally advanced lung cancer was generated using an in-house optimizer to assess the capabilities of the x-ray UHDR system. Individual beamlets of 10, 15, and 20-mm in size can produce isocenter dose rates of 17.3, 18.7, and 19.7-Gy/mAs and cross-channel leakage of 2.3, 1.9, and [Formula: see text], respectively. The novel UHDR 10-Gy/fraction plan exhibited comparable or improved conformity, homogeneity, and mean dose to organs at risk compared to the clinically used plan and it was delivered in 500 ms with more than [Formula: see text] of target volume receiving local dose rates higher than 40Gy/s.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".