SU‐E‐T‐401: The Development of Intensity and Energy Modulated Electron Radiotherapy; An Alternative to Photon Volumetric Modulated Arc Therapy
Bibliographic record
Abstract
Purpose: This work explains the development of an advanced treatment planning system for the generation of intensity and energy modulated electron radiotherapy (MERT) plans. The quality of MERT plans for the treatment of tumour bed boost in breast cancer is compared to direct electrons (DE) and volumetric modulated photon arc therapy (MAT). Method and Materials: The MERT treatment planning and delivery system at McGill University consists of MMCTP, an inverse optimization toolkit and the few leaf electron collimator. The stepwise planning process consists of: 1) generating a series of field openings, 2) Monte Carlo dose calculation for each field, 3) planning constraints, 4) iterative direct‐aperture optimization. For evaluation purposes, fourteen patients with breast cancer treated by lumpectomy and requiring post‐operative whole breast radiotherapy with tumour bed boost were planned using conventional DE, MAT and MERT. The planning goal was to deliver 10 Gy to at least 95% of the target volume. Dosimetry parameters for all techniques were compared. Results: Target coverage and homogeneity was best for MERT (D98=9.77 Gy, D2=11.03 Gy) followed by MAT (D98=9.56 Gy, D2=11.07 Gy) and DE (D98=9.81 Gy, D2=11.52 Gy). Relative to the DE plans, the MERT plans predicted a reduction of 35% in mean breast dose (p<0.05), 54% in mean lung dose (p<0.05) and 46% in mean body dose (p<0.05). Relative to the MAT plans, the MERT plans predicted a reduction of 24%, 36% and 39% in mean breast dose, heart dose and body dose respectively (p<0.05). Conclusions: MERT was a considerable improvement in dosimetry over DE. In some cases, there was a dosimetric advantage in using MERT over MAT for increased target conformity and low‐dose sparing of healthy tissue. Based on the favorable comparisons shown in this work it is reasonable to suggest that MERT could play a more significant role in breast radiotherapy.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".