A stochastic frontier analysis for enhanced treatment quality of high-dose-rate brachytherapy plans
Bibliographic record
Abstract
Abstract The purpose of the present study is to develop patient specific unbiased quality control (QC) models for high dose rate (HDR) brachytherapy plans. The proposed models are based on the stochastic frontier analysis formalism, a method of economic modeling. They act as a QC tool by predicting before the treatment planning process starts, the dosimetric coverage achievable for a HDR brachytherapy prostate plan. The geometric parameters considered in developing the models were: patient clinical target volume (CTV), organs at risk (OAR) volume, the bidirectional Hausdorff distance between CTV and OARs, and a fourth parameter measuring the catheters degree of non-parallelism within the target volume. Dosimetry parameters of interest are V 100 for the CTV, V 75 (bladder, rectum) and D 10 (urethra). Results show that the built models can provide valuable information on the personalization of the optimization process based on the patient geometric parameters. The impact on the quality plan due to the planner’s experience variability and judgment can be reduced by using those models, since the planner will attempt to achieve dosimetric parameters predicted by the models. Furthermore, the models provide information on the better trade-off between the target volume coverage and OARs sparing that can be achieved, regardless of the planner’s experience; the latter being achieved by moving each plan at least around their respective frontier for V 100 , V 75 and D 10 . The shortfall of the dosimetric parameters values computed by the treatment planning system (TPS) from those predicted by the models for a proportion of plans in the dataset reveals that optimized plans from a TPS, even clinically acceptable, are not necessarily the best that could be achieved. These represent 83% of plans in the training set for the target volume coverage (V 100 ), ∼50% for the bladder (V 75 ) and ∼72% for the urethra (D 10 ).
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".