Poster — Thur Eve — 30: 4D VMAT dose calculation methodology to investigate the interplay effect: experimental validation using TrueBeam Developer Mode and Gafchromic film
Bibliographic record
Abstract
The interplay effect between the tumor motion and the radiation beam modulation during a VMAT treatment delivery alters the delivered dose distribution from the planned one. This work present and validate a method to accurately calculate the dose distribution in 4D taking into account the tumor motion, the field modulation and the treatment starting phase. A QUASAR™ respiratory motion phantom was 4D scanned with motion amplitude of 3 cm and with a 3 second period. A static scan was also acquired with the lung insert and the tumor contained in it centered. A VMAT plan with a 6XFFF beam was created on the averaged CT and delivered on a Varian TrueBeam and the trajectory log file was saved. From the trajectory log file 10 VMAT plans (one for each breathing phase) and a developer mode XML file were created. For the 10 VMAT plans, the tumor motion was modeled by moving the isocentre on the static scan, the plans were re‐calculated and summed in the treatment planning system. In the developer mode, the tumor motion was simulated by moving the couch dynamically during the treatment. Gafchromic films were placed in the QUASAR phantom static and irradiated using the developer mode. Different treatment starting phase were investigated (no phase shift, maximum inhalation and maximum exhalation). Calculated and measured isodose lines and profiles are in very good agreement. For each starting phase, the dose distribution exhibit significant differences but are accurately calculated with the methodology presented in this work.
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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 | 0.001 | 0.000 |
| 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".