SU‐FF‐T‐79: IMRT with MLC Rotation — Dose Volume Benefits of High Precision Treatment Planning
Bibliographic record
Abstract
Purpose: To evaluate a new method of treatment planning that incorporates collimator rotation into MLC based fluence segmentation. Method and Materials: A set of treatment plans were generated using a new technique for fluence segmentation where the collimator is rotated in between each MLC sub‐field. It was previously shown that this technique has improved spatial resolution, increased flexibility in generating aperture shapes and decreased systematic error due to interleaf effects. Unfortunately these studies were limited to single fields. In this study we evaluate the ability of the rotating collimator technique to generate conformal dose distributions by comparing to conventional IMRT plans. Plans for a simple geometric phantom as well as a prostate and nasopharynx carcinoma recurrence were generated. One set of plans was generated with a conventional step‐and‐shoot delivery method (no MLC rotation) while a second set was generated with the collimator rotation segmentation method. Results: 3D dose comparisons as well as DVH analyses show that target coverage is equivalent or better with collimator rotation. Healthy tissue sparing is improved with collimator rotation, although more so for the smaller, more complex, nasopharynx target. It is also shown that increasing radiation efficiency (decreasing monitor units) will increase healthy tissue sparing. A decrease in the number of monitor units is seen with collimator rotation for both clinical cases. Conclusion: By incorporating MLC rotation into IMRT planning it is possible to improve target conformity and healthy tissue sparing. These benefits are more significant for smaller targets of higher complexity. The number of monitor units will also decrease, providing less transmission and total body scatter dose. Conflict of Interest: This work was supported in part by Varian Medical Systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".