Impact of range uncertainties for intensity‐modulated mixed electron‐photon radiation therapy
Bibliographic record
Abstract
BACKGROUND: In the context of mixed electron-photon radiation therapy (MBRT), while the necessity of robust optimization to setup uncertainties is well-established, range uncertainties have yet to be investigated. PURPOSE: This study provides the first assessment of the impact of range uncertainties on MBRT plans. METHODS: The percent depth dose of 2 electron beams (6 MeV and 20 MeV) and 1 photon beam (6 MV) are calculated by Monte Carlo using EGSnrc in slab phantoms. Range uncertainties are simulated by generating two copies of the phantom with each voxel's mass density upscaled or downscaled by 3.5%. Two clinical plans for a leg sarcoma case and a post-mastectomy breast case were replanned with MBRT with 2 optimization methods: once without robust optimization and once with robust to both setup and range uncertainties. RESULTS: Dose discrepancies between the percent depth doses of density-scaled phantoms and the nominal phantom were found to be much larger for electron beams than photons with maximum differences of 6.9% (6 MeV) and 5.5% (20 MeV) versus 1.6% (6 MV) of the maximum dose. In both clinical cases, the region of largest dose discrepancy between the range and nominal scenarios was found to be along the electron's beam path, starting immediately downstream from the target and within a few cm. Even without robust optimization, dose-volume histograms (DVHs) of all relevant structures were not meaningfully degraded under range scenarios. In the breast plan, the ipsilateral lung's V20Gy increased by 1.9% under the worst range scenario. No substantial change in the DVHs of range scenarios were observed between the robustly and non-robustly optimized plan. CONCLUSION: In the two investigated cases, robustness to range uncertainties was not improved in robustly optimized versus non-robustly optimized plans. A larger study comprising more patients and treatment sites remains to be performed to adequately assess the necessity of robust optimization of MBRT plans to range uncertainties.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".