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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".