A multi-criteria optimization approach for HDR prostate brachytherapy: II. Benchmark against clinical plans
Bibliographic record
Abstract
Abstract The current iterative approach to inverse planning of high dose rate treatment planning can be time consuming. The purpose of this two-part study is to streamline the planning process while maintaining plan quality. In this second part, a multi-criteria optimization (MCO) planning algorithm is proposed and benchmarked against a standard planning algorithm. With a set of previously established regression models, a patient-specific valid solution space on the Pareto surface was predicted based on the anchor plans results. Alternative plans generated alongside the partial Pareto front were presented to the planner, and one plan was selected as the MCO plan. The dosimetric parameters results as well as the planning time were compared between the MCO plans and the physician-approved standard plans for 236 prostate cases. Results show that the urethra is better spared with MCO planning than with standard planning (a lower mean urethral D 10 value of 2.25%). The overall MCO plan quality also outperforms the standard plan quality, since MCO planning is able to increase the frequency of clinically acceptable plans meeting all of RTOG criteria simultaneously without any human intervention (from 83.05% to 97.46%). Finally, the average MCO planning time is without any interventions of treatment planners. The presented MCO planning algorithm constitutes a robust and automated way to improve treatment quality in brachytherapy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".