Poster — Thur Eve — 63: Dosimetric impact of breathing motion in lung SBRT: Dual vs single volumetric modulated arc therapy
Bibliographic record
Abstract
Volumetric modulated arc therapy (VMAT) is a time efficient treatment delivery platform capable of producing highly conformal dose distributions with a single 360° arc. However, additional arcs can be used to further improve the conformal dose distribution. For these reasons, VMAT is often used for stereotactic body radiation therapy (SBRT) in which the treatment deliveries are hypofractionated. The dosimetric impact of tumour motion, especially in lung SBRT where tumour motion is most significant and treatments are hypofractionated, has always been a clinical concern. Through the use of 4-dimensional computed tomography (4D-CT), 4D dose distributions can be calculated that account for dosimetric errors due to motion and temporal variation in lung density that are not accounted for in clinical treatment plans. The purpose of this study was to quantify the dosimetric differences that arise due to tumour motion and variations in lung density between single and dual VMAT SBRT treatment plans. Six patients previously treated for stage I/II non-small-cell lung cancer with SBRT were included in this retrospective study. 3D and 4D dose distributions were calculated for both single and dual arc plans for each of the six patients. Dose-volume histogram metrics are reported for the target and critical structures. The results show significant differences (p ≤ 0.05) between the 3D and 4D dose distributions for the ratio of the prescription isodose volume to the primary target volume (PTV). This result was consistent for both single and dual arc VMAT plans.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".