SU‐EE‐A2‐05: Direct Aperture Optimization for On‐Line Adaptive Radiation Therapy
Bibliographic record
Abstract
Purpose: To investigate a novel technique for on‐line adaptive radiation therapy (ART) that uses direct aperture optimization (DAO) Methods and Materials: A model simulating the geometry of a prostate case was created. The prostate, rectum and bladder are represented by an ellipsoid, cylinder and sphere, respectively (the dimensions and positions of these structures are based on patient image data). This configuration represented the “original geometry” and was used to create the original IMRT treatment plan. The plan was created using an in‐house DAO system with seven beams (40, 80, 110, 250, 280, 310, 355 gantry angles) and six apertures per beam. Four different “deformed geometries” were created by systematically deforming the original geometry to various degrees (0.25, 0.50, 0.75 and 1.00 cm maximum deformations of rectum and prostate). For each deformed geometry, a new treatment plan was created by modifying (adapting) the original treatment plan using DAO. The quality of the resulting plans, together with the optimization time efficiency of the plan adaptation, was used to assess the suitability of DAO for on‐line ART. The effects of altering different DAO parameters were investigated by varying the maximum leaf step size, maximum aperture weight change and optimization cooling schedule. Results: The plans created by adapting the original treatment plan met the imposed dose constraints for all four deformed geometries. Adapting the original treatment plan was much faster than performing a completely new re‐optimization. Furthermore, by appropriately limiting selective DAO parameters the convergence to an acceptable plan was significantly accelerated. The optimal choice of DAO parameter limits was correlated to the degree of geometry deformation. Conclusion: This study demonstrated that DAO is highly suitable for online ART. The treatment plan adaptation was efficient and the resulting plans met the imposed dose constraints.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".