SU‐GG‐J‐10: Investigation of a Novel Algorithm for True 4D VMAT Planning and Delivery
Bibliographic record
Abstract
Purpose: To quantify the ability of our 4D VMAT planning algorithm to generate deliverable plans over a range of target motions. Method and Materials: Our 4D VMAT planning algorithm is an extension of the 3D algorithm by Otto (Med Phys 35 2008, 310–317) and fully incorporates target and organ motion during optimization. Delivery of each MLC aperture is synchronized to a specific phase of the target motion. The magnitude of motion between each phase is 2.5 or 5 mm. Using a phantom consisting of a cylindrical target nested within a half‐ring avoidance structure, treatment plans for a range of uniform target motions (0.5 – 4 cm) and periods (2.5 – 5.5 s) were generated. Dose prescription was 60 Gy. DVHs from the 4D VMAT plans were compared against the 3D VMAT DVH as well as 3D motion degraded DVHs. Results: 4D VMAT plans were similar in quality or superior to the 3D plan. For motion ranges of 1.5 cm and 4 cm, the volume of the avoidance structure receiving more than 20 Gy was decreased by 10.4% and 28.6% respectively while the target volume receiving greater than 58 Gy increased by 6.5% and 16.5%. Total treatment time ranged from 141 – 181 minutes to deliver the full 60 Gy prescription or 4.7 – 6.1 minutes for a 2 Gy fraction assuming maximum dose rate is 600 MU/min. Motion of 1 cm can cause noticeable degradation of the 3D DVH. Conclusion: Our 4D VMAT planning algorithm can create plans equal to or superior to 3D plans. Future work will investigate whether these benefits can be extended to actual 4D clinical patient data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".