TU-G-BRB-04: A Robust-CVaR Optimization Approach to Left-Sided Breast IMRT
Bibliographic record
Abstract
PURPOSE: To test the feasibility of a cardiac sparing IMRT planning approach for patients with left-sided breast cancer using a robust optimization model. METHODS: A robust optimization model was developed for breast IMRT. The concept of conditional-value-at-risk (CVaR) was used in the robust framework to guarantee that the clinical dose volume criteria for targets and organs at risk hold under uncertainty in the patient's breathing pattern. Clinical treatment methods for breast cancer (inhale breath-hold with active breathing control (ABC) or free breathing) were simulated via optimization models. A 4DCT patient dataset with target and organs at risk on each breathing phase was used to simulate a clinical case with a total of 20% increase in lung volume from exhale to inhale over 5 phases. The results of the proposed robust model were compared with those of the current clinical models. RESULTS: Compared to the conventional IMRT method for breast cancer (with free breathing), the proposed robust-CVaR model resulted in a 14.6% reduction in mean heart dose without compromising the target coverage and dose homogeneity. The clinical dose-volume limits for the heart as well as the clinical target volume were met in robust results. The robust method resulted in 23.9% improvement in the maximum dose to 25cc of the heart volume. The robust results showed very low variability among the quality of planning and realized treatments. CONCLUSIONS: Using CVaR limits in a robust optimization framework can help improve the quality of IMRT treatments. The robust-CVaR can generate a high quality treatment plans, but is delivered during free breathing and does not require patient compliance with an external device. The quality of robust treatment remains the same under irregular breathing. Explicitly including metrics for lung and bigger motion amplitudes in the robust optimization method may further improve the results.
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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 | 0.000 |
| 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".