TH‐A‐116‐05: A Robust Direct Aperture Optimization Approach for Left‐Sided Breast IMRT
Bibliographic record
Abstract
Purpose: To test the feasibility of a robust direct aperture optimization (DAO) model for cardiac sparing IMRT planning of left‐sided breast cancer patients. Methods: We extended a previously developed robust optimization method to obtain a DAO approach for tangential breast IMRT that is robust against uncertainty in the patient's breathing pattern. The robust DAO model contains additional constraints on the segment shapes and weights in order to reduce the complexity of delivery and ensure that high weight segments cover the whole breast volume. A 4DCT dataset was used to test the model, and 100 simulated breathing patterns were generated. Results were compared with the previously developed robust fluence map optimization (FMO) model as well as the clinical treatment methods for breast cancer (inhale breath‐hold with active breathing control (ABC) and free breathing). Results: On average, the robust DAO approach reduced the mean heart dose by 15.1% compared to the conventional IMRT method for breast cancer (with free breathing) using an equal number of segments. The maximum dose to 10cc of the heart was reduced by 19.7%. Compared to the previous robust FMO method, the robust DAO method delivered less than 1% higher dose to the heart and 1% lower dose to the CTV on average. The dose‐volume criteria for the heart and CTV were met in all of the breathing scenarios. The robust DAO approach resulted in a 20.8% reduction in the number of cases that would have required ABC treatment in comparison with the clinical method with free breathing. Conclusions: The proposed DAO approach results in lower heart dose compared to the conventional method with free breathing and shows low sensitivity to breathing pattern uncertainty. The robust DAO method is able to derive simpler IMRT segments during free breathing and can potentially reduce the need for breath‐hold techniques. This research was supported in part by the Ontario Ministry of Economic Development and Innovation, the Canadian Breast Cancer Foundation ‐ Ontario Region, and the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO) funded by the Ontario Research Fund (ORF).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".