Applying the column generation method to the intensity modulated high dose rate brachytherapy inverse planning problem
Bibliographic record
Abstract
Abstract Objective. Intensity modulated high dose rate brachytherapy (IMBT) is a rapidly developing application of brachytherapy where anisotropic dose distributions can be produced at each source dwell position. This technique is made possible by placing rotating metallic shields inside brachytherapy needles or catheters. By dynamically directing the radiation towards the tumours and away from the healthy tissues, a more conformal dose distribution can be obtained. The resulting treatment planning involves optimizing dwell position and shield angle (DPSA). The aim of this study was to investigate the column generation method for IMBT treatment plan optimization. Approach. A column generation optimization algorithm was developed to optimize the dwell times and shield angles. A retrospective study was performed on 10 prostate cases using RapidBrachyMCTPS. At every iteration, the plan was optimized with the chosen DPSA which would best improve the cost function that was added to the plan. The optimization process was stopped when the remaining DPSAs would not add value to the plan to limit the plan complexity. Main results. The average number of DPSAs and voxels were 2270 and 7997, respectively. The column generation approach yielded near-optimal treatment plans by using only 11% of available DPSAs on average in ten prostate cases. The coverage and organs at risk constraints passed in all ten cases. Significance. The column generation method produced high-quality deliverable prostate IMBT plans. The treatment plan quality reached a plateau, where adding more DPSAs had a minimal effect on dose volume histogram parameters. The iterative nature of the column generation method allows early termination of the treatment plan creation process as soon as the dosimetric indices from dose volume histogram satisfy the clinical requirements or if their values stabilize.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".