SU‐C‐BRC‐06: Adaptive and Robust IMRT Treatment Planning for Lung Cancer
Bibliographic record
Abstract
Purpose: To improve IMRT treatments for lung cancer patients who exhibit breathing motion uncertainty; to demonstrate that combining adaptive radiation therapy with robust optimization can lead to simultaneous improvements in tumor coverage and healthy tissue sparing over non‐adaptive robust optimization methods. Methods: A model of breathing motion uncertainty was derived from previous robust optimization research. We then developed two algorithms ‐ exponential smoothing and running average ‐ to up‐date this model from fraction to fraction, using a sequence of retrospective motion probability mass functions (PMFs) from real patients. A robust optimization problem was solved for each fraction with an updated uncertainty model to generate a sequence of treatments. Delivery of the entire treatment was simulated and dose was accumulated according to the sequence of PMFs. The breathing motion model was created and updated in MATLAB, while CPLEX was used to solve the corresponding robust optimization problem as a linear program. The adaptive robust treatment was then compared with a robust treatment in which the uncertainty set was not updated over the treatment course. Results: Our adaptive robust optimization method exhibited a simultaneous improvement over non‐adaptive robust methods in both tumor coverage and healthy tissue sparing. The adaptive robust treatments were insensitive to the choice of the initial uncertainty model, and also closely comparable to idealized treatments that would be obtained with perfect foresight — i.e., treatments created with a priori knowledge of the entire sequence of future patient PMFs. Conclusions: By combining adaptive radiation therapy and robust optimization, our method combats both the instantaneous breathing motion uncertainty realized in each fraction and changes in the uncertainty that occur from fraction to fraction. Our method demonstrates that it is possible escalate dose to the tumor from the level possible with current robust methods without sacrificing healthy tissue sparing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".