Monte Carlo based modulated electron beam treatment planning using a few-leaf electron collimator—feasibility study
Bibliographic record
Abstract
Energy modulated electron beam therapy with conventional clinical accelerators has lagged behind photon IMRT despite its potential to achieve highly conformal dose distributions in superficial targets. One of the reasons for this is the absence of an automated collimating device that allows for the flexible delivery of a series of variable field openings. Electron-specific multileaf collimators attached to the bottom of the applicator require the use of a large number of motors and suffer from being relatively bulky and impractical for head and neck sites. In this work, we investigate the treatment planning aspects of a proposed 'few-leaf' electron collimator (FLEC) that consists of four motor-driven trimmer bars at the end of the applicator. The device is designed to serve as an accessory to standard equipment and allows for the shaping of any irregular field by combination of rectangular fieldlets. Using a Monte Carlo model of the FLEC, dose distributions are optimized using a simulated annealing (SA) inverse planning algorithm based on a limited number of Monte Carlo pre-generated, realistic phantom-specific dose kernels and user-specified dose-volume constraints. Using a phantom setup with an artificial target enclosed by organs at risk (OAR) as well as using a realistic patient case, we demonstrate that highly conformal distributions can be generated. Estimates of delivery times are made and show that a full treatment fraction can be kept to 15 min or less.
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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 | 0.002 |
| 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 | 0.000 |
| 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".