Poster — Thur Eve — 31: Dosimetric Effect of Respiratory Motion on RapidArc Lung SBRT Treatment Delivered by TrueBeam Linear Accelerator
Bibliographic record
Abstract
Volumetric modulated arc therapy (VMAT) allows fast delivery of stereotactic radiotherapy. However, the discrepancies between the calculated and delivered dose distributions due to respiratory motion and dynamic multileaf collimators (MLCs) interplay are not avoidable. The purpose of this study is to investigate RapidArc lung SBRT treatment delivered by the flattening filter‐free (FFF) beam and flattened beam with Varian TrueBeam machine. CIRS Dynamic Thorax Phantom with in‐house made lung tumor insertion was CT scanned both in free breathing and 4DCT. 4DCT was used to determine the internal target volume. The free breathing CT scan was used for treatment planning. A 5 mm margin was given to ITV to generate a planning target volume. Varian Eclipse treatment planning was used to generate RapidArc plans based on the 6 MV flattened beam and 6MV FFF beam. The prescription dose was 48 Gy in 4 fractions. At least 95% of PTV was covered by the prescribed dose. The RapidArc plans with 6 MV flattened beam and 6MV FFF beam were delivered with Varian TrueBeam machine. The dosimetric measurements were performed with Gafchromic XR‐RV3 film, which was placed in the lung tumor insertion. The interplay between the dynamic MLC‐based delivery of VMAT and the respiratory motion of the tumor degraded target coverage and created undesired hot or cold dose spots inside the lung tumor. Lung SBRT RapidArc treatments delivered by the FFF beam of TrueBeam linear accelerator is superior to the flattened beam. Further investigation will be performed by Monte Carlo simulation.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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".