Poster — Thur Eve — 53: Analysis of the distribution of dose delivery during respiratory‐gated step‐and‐shoot IMRT for lung cancer radiotherapy
Bibliographic record
Abstract
Respiratory motion is a large source of dosimetric error when treating lung cancer with Intensity Modulated Radiation Therapy (IMRT). The asynchronicity of the tumour motion and the multileaf collimator (MLC) used to modulate the radiation beam intensity, leads to the interplay effect. One method to account for this effect is respiratory gating. Treatment planning optimization for gated IMRT is performed on a subset average 4D-CT which includes the phases surrounding end exhalation. However, this assumes that the beam delivery will be evenly distributed amongst those phases. This study investigates the distribution of beam delivery during gated step-and-shoot IMRT (SS-IMRT) for both early and late stage non-small cell lung cancer (NSCLC). Four Stage I NSCLC patients, prescribed a dose of 54 Gy in 3 fractions, and five Stage III NSCLC patients, prescribed a dose of 60 Gy in 30 fractions, were retrospectively planned with high and low modulation beams-IMRT, and delivered using the QUASAR™ Programmable Respiratory Motion Platform with 15 mm and 20 mm peak-to-peak sinusoidal motion and real patient breathing motion. The percent monitor units delivered at each phase were compared. For Stage I patients, the monitor units delivered were evenly distributed over the gating window due to a high number of monitor units delivered per control point. For Stage III patients, as the complexity of SS-IMRT increases, there were more monitor units delivered in the initial gating phase. This dose discrepancy could potentially lead to geographic miss of the tumour and should be taken into account during treatment planning.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".