SU‐E‐T‐653: Predicting Objective Function Weights for IMRT Prostate Treatment Planning Using Patient Anatomy
Bibliographic record
Abstract
Purpose: To develop a prediction model for objective function weights for intensity‐modulated radiation therapy (IMRT) prostate treatment planning with multiple objectives using geometry information from patient anatomy. Methods: A previously developed inverse optimization method (IOM) was used to reverse‐engineer optimal objective function weights (inverse weights) from an observed treatment plan. We developed a regression model to predict the weights for IMRT prostate treatment planning using patient anatomy from 25 patients. The ratio of the overlap volumes of the rectum and bladder with the planning target volume expanded by 1cm was used to predict the bladder and rectum weights. The femoral head weights were included in the model as a small fixed weight (1%). The model was validated using leave‐one‐out cross‐validation. We evaluated the model by comparing the treatment plans generated through inverse planning using the inverse weights from IOM and the predicted weights from the regression model. Results: On average, V54Gy for the bladder was 36.1% using the inverse weights and 36.6% using the predicted weights. V70Gy for the bladder was 23.2% (inverse) and 23.5% (predicted). For the rectum, V54Gy was 34.6% (inverse) and 33.9% (predicted), and V70Gy was 22.6% (inverse) and 22.3% (predicted). For each criterion, the difference between the inverse and predicted metrics was not statistically significant. All treatment plans from the predicted weights satisfied the clinical criteria. Conclusion: Our results show that objective function weights are well‐predicted by the regression model. This approach may support the genesis of personalized weights in IMRT treatment planning. This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Ontario Graduate Scholarship (OGS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".