Sci-Sat AM: Stereo - 04: Evaluation of VMAT interplay effect for lung SABR using TrueBeam 10XFFF beam
Bibliographic record
Abstract
During a VMAT treatment delivery, the interplay effect between the moving target and varying machine parameters result in dose distributions that are different from those initially planned. In this work, we investigate this effect for lung SABR by using 4D dose calculation derived from the Varian TrueBeam trajectory log file. The impact of treatment start phase is also evaluated. A QUASAR™ respiratory motion phantom was scanned with motion amplitudes of 0.4, 1, 2 and 3 cm with a 4 second period. MIP and the average dataset were generated from the 4DCT. A static CT was also acquired with the tumor in its centre position. Plans were optimized with 10X FFF beam until PTV and fictitious critical structures met the dose constraints. Ten temporally interleaved plans were constructed with the temporal machine parameter information from the trajectory log file. Ten plans were calculated with isocentre shifts to simulate respiratory motion and then summed. For each motion amplitude, three separate sum plans were created with various phase shifts (no phase shift, maximum inhalation and maximum exhalation) to assess the impact of treatment start phase. For all the phase shifts investigated, the DVH for PTV demonstrated good dose coverage. However, a careful review of slice by slice plan comparison indicates dose “holes” are observed within PTV. The PTV dose difference between various treatment start phases can be as high as 19%. This assumes all treatment fractions have identical treatment start phase. Our future work includes evaluation of interplay effect for various breathing periods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".