Direct aperture optimization for FLEC‐based MERT and its application in mixed beam radiotherapy
Bibliographic record
Abstract
PURPOSE: Despite promising research in modulated electron radiotherapy (MERT), an applicator to produce modulated electron beams and associated treatment planning software is still not commercially available. This work investigated an optimization process in treatment planning for the McGill few leaf electron collimator (FLEC) MERT delivery device. In addition, the possibility of combining MERT with photon fields was examined to investigate mixed beam radiotherapy. METHODS: A FLEC direct aperture optimization (DAO) method, in which FLEC apertures and weights were iteratively optimized was created. The authors evaluated the performance of DAO against our previous technique for generating FLEC plans and with commercially available photon beam optimization algorithms using a basic target and organ at risk geometry. The authors applied the DAO technique on a sarcoma treatment to evaluate clinical parameters. Finally, the authors examined the merit of mixing the DAO generated FLEC electron fields with photon fields to improve the dosimetry of the sarcoma treatment. RESULTS: In relation to the alternative plans, the DAO generated sarcoma MERT plan was competitive in its ability to reduce the dose to OAR but weaker in its ability to highly conform the dose to the target volume. The addition of photon fields improved the quality of the MERT plan in terms of OAR sparing and target conformality. CONCLUSIONS: The DAO approach yielded deliverable FLEC-based MERT plans with a limited number of fields. The approach combined with photon optimization added flexibility, where the mutual benefits of each radiation type was used in unison to improve plan quality.
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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 | 0.001 |
| 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 | 0.000 | 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".