Poster — Thur Eve — 33: A comprehensive analysis of the effect of respiratory motion on the delivery of IMRT to advanced stage non‐small cell lung cancer
Bibliographic record
Abstract
The goal of this work was to quantify the interplay effect of various IMRT delivery techniques in the treatment of Stage III non-small cell lung cancer. Five patients with significant tumour motion were retrospectively planned on the average 4D-CT dataset with eight different IMRT techniques: three Tomotherapy techniques with different beam-widths, two step-and-shoot (SS-IMRT) with different complexity, one sliding-window (SW-IMRT), and two VMAT techniques (RapidArc and SmartArc). Each plan was calculated on a delivery verification phantom that was mounted on a programmable respiratory motion platform and delivered under the following motion conditions: 1) Static; 2) sinusoidal with 4 different amplitudes; 3) Real Patient Breathing. A standard 3%/3mm gamma analysis compared the sum of all 30 fractions to their corresponding 60Gy/30fx plan. One-way ANOVA was conducted for respiratory motion amplitude and IMRT modality, separately. There were no significant differences amongst the modalities at any amplitude level. However, for individual modalities, there were significant differences amongst different amplitudes except for Tomo-2.5cm (p=0.260). Post-hoc Tukey tests determined that detectable significant differences amongst any motion level, including real-patient breathing, were observed when compared to the 20mm amplitude for all modalities except Tomo-2.5cm and SmartArc. SW-IMRT showed significant differences at 15mm when compared to both static (p=0.033) and 5mm (p=0.008). All methods except for RapidArc averaged out to clinically acceptable gamma pass rates up to 15mm. In conclusion, for motion levels above 15mm, the interplay effect can be clinically unacceptable. However, the interplay effect at these motion levels does not appear to be modality dependent.
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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 | 0.001 |
| 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) | 0.003 | 0.000 |
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".