Robust mixed electron–photon radiation therapy optimization
Bibliographic record
Abstract
Purpose Mixed beam electron–photon radiation therapy (MBRT) is an emerging technique that has the potential to reduce dose to normal tissue while improving target coverage for cancer sites with superficial tumors. Advances in optimization algorithms and robotic linear accelerators have made the creation and delivery of complex MBRT plans realistic without the need for special additional collimators, devices, or resetup of the patient. However, no study has been performed on the robustness of MBRT dose distributions to patient setup errors. Intensity‐modulated delivery of other charged particles such as protons have been shown to require robust planning techniques to maintain adequate target coverage under positioning errors. We therefore assess the sensitivity of MBRT treatment plans to positioning uncertainties when created under the traditional planning target volume (PTV)‐based planning paradigm and present a novel optimization model for the creation of robust MBRT plans. Methods The column generation method was applied to robust MBRT treatment planning by deriving the pricing problem for stochastic and “worst case” minimax optimization models, two common formulations of robustness. Robust treatment plans were created for two patient cases representative of the cancer sites which stand to benefit from MBRT: soft tissue sarcoma (STS) irradiation and chest wall irradiation with deep‐seated internal mammary, axillary, and supraclavicular nodes (CW‐N). For both patient cases, beamlet dose distributions for electrons and photons were generated for positioning shifts in six directions, in addition to a nominal unshifted scenario, for a total of seven sets of beamlets. Robust plans were created by specifying dose coverage constraints to the clinical target volume (CTV), as opposed to the PTV. Comparisons were performed against traditional PTV‐based plans created with a single set of unshifted beamlets. Results The dose distributions of traditional PTV‐based MBRT plans showed significant degradation in target coverage homogeneity when patient positioning errors were considered. For both cancer sites, cold spots below 95% and hot spots above 108% of the prescription dose appeared within the CTV when shifting the patient by 5 mm, corresponding to the margin added to the CTV to form the PTV. In contrast, CTV‐based robust plans created with the new optimization model maintained target coverage within the 95%–108% limits, for all positioning errors. Conclusion The quality of MBRT treatment plans created using a traditional PTV‐based optimization model was highly sensitive to patient positioning errors. For both patient cases, positioning errors resulted in perturbations to the nominal dose distributions which would have rendered PTV‐based plans clinically unacceptable. In contrast, CTV‐based robust plans were able to maintain adequate target coverage under all positioning error scenarios considered. We therefore conclude that to ensure the fidelity of the dose distribution delivered to the patient, robust optimization is critical when creating MBRT plans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".