Dose-based constraint generation for large-scale IMRT optimization
Bibliographic record
Abstract
Intensity-modulated radiation therapy (IMRT) is a commonly-used method for treating cancer. To develop a treatment plan, an optimization problem is formulated to find the optimal radiation intensities to ensure that the cancerous region receives the required prescribed radiation dose while limiting the excess radiation to the surrounding healthy organs. Due to the granularity of the discretization of the body into numerous three-dimensional pixels, the resulting optimization problem is often extremely large-scale and can include tens of thousands of constraints. This paper presents an exact dose-based constraint generation technique to solve large-scale linear problems in IMRT. We first use specific characteristics of the IMRT problem to cluster the voxels based on how they are influenced per unit intensity of each part of the radiation beams and then use these clusters in a specialized constraint generation algorithm. We demonstrate the applicability of the proposed approach using several retrospective patient data sets and discuss the computational efficiency and solution quality of the proposed approach for different cases of the algorithm. Our results show that the proposed method decreases the solution time by 75% to 98% for all patients, without affecting the treatment quality compared to the original full-scale IMRT problem.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".