Poster — Thur Eve — 01: Dynamic Aperture Optimization in MERT Using Direct Aperture Optimization
Bibliographic record
Abstract
The advantage of modulated electron radiation therapy (MERT) comes from the defined electron range and sharp fall‐off offered with electron beams, combined with complex inverse planning techniques to conform dose to the target and reduce dose to organs at risk (OAR) beyond the target. Recent studies have evaluated the feasibility of various electron collimator devices for shallow tumor treatments. Despite the promising MERT studies, a MERT system comparable to IMRT is not commercailly available. In this work we investigate a dynamic aperture optimization process, which dynamically optimizes the aperture shapes and weights using direct aperture optimization (DAO) and is applicable to the McGill MERT delivery process using the Few‐Leaf‐Electron‐Collimator (FLEC). This study presents the optimization code (DADAO), and a plan comparison to commercially available photon beam optimization algorithms using a basic target and organ at risk geometry. A FLEC‐DADAO plan was benchmarked to plans generated from TomoTherapy and Varian Eclipse IMRT and RapidArc in order to establish a baseline level of confidence. Results were analyzed using dose volume histograms (DVH) and isodose plots. The DADAO plan was comparable to the clinical plans, competitive in its ability to reduce the dose to OAR but weaker in its ability to highly conform the dose to the target objectives. The DADAO code for MERT demonstrates potential to optimize electron planning and reduce the low‐dose irradiated volume including the low dose to the OAR at a slight cost in target coverage as compared to photon techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".