Delivery validation of an automated modulated electron radiotherapy plan
Bibliographic record
Abstract
PURPOSE: Modulated electron radiation therapy (MERT) represents an active area of interest that offers the potential to improve healthy tissue sparing in treatment of certain cancer cases. Challenges remain however in accurate beamlet dose calculation, plan optimization, collimation method, and delivery accuracy. In this work, the authors investigate the accuracy and efficiency of an end-to-end MERT plan and automated delivery method. METHODS: Treatment planning was initiated on a previously treated whole breast irradiation case including an electron boost. All dose calculations were performed using Monte Carlo methods and beam weights were determined using a research-based treatment planning system capable of inverse optimization. The plan was delivered to radiochromic film placed in a water equivalent phantom for verification, using an automated motorized tertiary collimator. RESULTS: The automated delivery, which covered four electron energies, 196 subfields, and 6183 total MU was completed in 25.8 min, including 6.2 min of beam-on time. The remainder of the delivery time was spent on collimator leaf motion and the automated interfacing with the accelerator in service mode. Comparison of the planned and delivered film dose gave 3%/3mm gamma pass rates of 62.1%, 99.8%, 97.8%, 98.3%, and 98.7% for the 9, 12, 16, and 20 MeV, and combined energy deliveries, respectively. Delivery was also performed with a MapCHECK device and resulted in 3%/3 mm gamma pass rates of 88.8%, 86.1%, 89.4%, and 94.8% for the 9, 12, 16, and 20 MeV energies, respectively. CONCLUSIONS: Results of the authors' study showed that an accurate delivery utilizing an add-on tertiary electron collimator is possible using Monte Carlo calculated plans and inverse optimization, which brings MERT closer to becoming a viable option for physicians in treating superficial malignancies.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".