WE‐AB‐209‐11: Prostate Cancer Treatment Planning: Sensitivity and Representative Objective Function Weights
Bibliographic record
Abstract
Purpose: To develop an automated planning methodology that exploits patient sensitivity to objective function weights. Methods: Given a treatment plan, we first create an acceptable treatment region that encompasses a set of treatment plans with similar clinical performance (e.g., +/−1% at V70Gy). We use inverse optimization to map this region in criterion space to the weight space and find a corresponding region of acceptable weight vectors (W). The shape and size of W describes how sensitive a patient is to perturbations in objective function weights. To exploit the information encoded by these regions, we approximate W for each patient by a polyhedron and we cluster patients using a novel integer programming model with cluster sizes from k=1,2,…,10. Each cluster centroid is a representative objective function weight vector and we use these weight vectors to generate k treatment plans for each patient (AUTO plans). Using 315 prostate cancer plans, we determine the number of patients that would have received an improved treatment plan using our automated approach. Results: Clustering patients into five groups produced a global set of representative weights such that for 88% of patients there exists at least one AUTO plan that improves upon the clinical treatment plan in terms of organ‐at‐risk mean dose and clinical acceptability criteria satisfaction (i.e., V54Gy50% and V70Gy30%). The AUTO plans provided bladder or rectum mean dose improvement over the clinical treatment plans for 296 (94%) patients, bladder mean dose improvement for 185 (59%) patients, rectum mean dose improvement for 273 (87%) patients, and mean dose improvements for both bladder and rectum in 162 (51%) patients. The AUTO plans provided fewer violations for bladder/rectum V54Gy50% and slightly more for bladder/rectum V70Gy30% when compared to clinical plans. Conclusion: A method combining inverse optimization and clustering automatically produces prostate treatment plans for 88% of patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".