SU-E-T-607: Determining Critical Objectives and Importance Factors for Prostate IMRT Treatment Planning
Bibliographic record
Abstract
PURPOSE: To determine the optimization objectives that are most critical in prostate IMRT treatment planning, and to demonstrate that clinically acceptable treatments can be obtained using fewer optimization objectives with intelligently chosen importance factors. METHODS: We develop a novel optimization method that uses a historical prostate IMRT treatment as an input to quantify importance factors for a given set of objectives in the treatment planning problem. An initial treatment planning formulation with many candidate objectives is formulated. Then, given a historical treatment, importance factors for the objectives are determined via inverse optimization. We analyzed the results over several patients and identified the most critical optimization objectives in prostate IMRT treatment planning. We then designed a new treatment planning formulation with only the critical objectives, determined the importance factors via inverse optimization, and then compared the dose distribution to that of the original planning problem. The method was applied to a homogeneous cohort of 12 patients from Princess Margaret Hospital. RESULTS: A treatment plan generated using 18 objectives was replicated using only six objectives and inversely-optimized importance factors. For the bladder and rectum, a combination of the objective that minimizes the mean dose and the objective that penalizes dose above 50 Gy was determined to be most critical, while objectives that minimize the maximum dose were found to be critical for the femoral heads. The bladder and rectum objectives carried more than 95% of the importance factor over all objectives. CONCLUSIONS: By identifying critical objectives, our method has the potential to significantly enhance the computational efficiency of a treatment planning problem. A simplified treatment planning formulation with importance factors that are determined via inverse optimization reduces the need for an iterative, trial-and-error process in treatment planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".