Toward automatic field selection and planning using Monte Carlo-based direct aperture optimization in modulated electron radiotherapy
Bibliographic record
Abstract
Modulated electron radiotherapy (MERT) has been proven to produce optimal plans for shallow tumors. This study investigates automated approaches to the field determination process in generating optimal MERT plans for few-leaf electron collimator (FLEC)-based MERT, by generating a large database of pre-calculated beamlets stored as phase-space files. Beamlets can be used in an overlapping feathered pattern to reduce the effect of abutting fields, which can contribute to dose inhomogeneities within the target. Beamlet dose calculation was performed by Monte Carlo (MC) simulations prior to direct aperture optimization (DAO). The second part of the study examines a preliminary clinical comparison between FLEC-based MERT and helical TomoTherapy. A MERT plan for spinal irradiation was not able to conform to the PTV dose constraints as closely as the TomoTherapy plan, although the TomoTherapy plan was taken as is, i.e. not Monte Carlo re-calculated. Despite the remaining gradients in the PTV, the MERT plan was superior in reducing the low-dose bath typical of TomoTherapy plans. In conclusion, the FLEC-based MERT planning techniques developed within the study produced promising MERT plans with minimal user input. The phase-space database reduces the MC calculation time and the feathered field pattern improves target homogeneity. With further investigations, FLEC-based MERT will find an important niche in clinical radiation therapy.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".