Poster — Thur Eve — 17: In‐phantom and Fluence‐based Measurements for Quality Assurance of Volumetric‐driven Adaptation of Arc Therapy
Bibliographic record
Abstract
During volumetric modulated arc therapy (VMAT) of head and neck cancer, some patients lose weight which may result in anatomical deviations from the initial plan. If these deviations are substantial a new treatment plan can be designed for the remainder of treatment (i.e., adaptive planning). Since the adaptive treatment process is resource intensive, one possible approach to streamlining the quality assurance (QA) process is to use the electronic portal imaging device (EPID) to measure the integrated fluence for the adapted plans instead of the currently‐used ArcCHECK device (Sun Nuclear). Although ArcCHECK is recognized as the clinical standard for patient‐specific VMAT plan QA, it has limited length (20 cm) for most head and neck field apertures and has coarser detector spacing than the EPID (10 mm vs. 0.39 mm). In this work we compared measurement of the integrated fluence using the EPID with corresponding measurements from the ArcCHECK device. In the past year nine patients required an adapted plan. Each of the plans (the original and adapted) is composed of two arcs. Routine clinical QA was performed using the ArcCHECK device, and the same plans were delivered to the EPID (individual arcs) in integrated mode. The dose difference between the initial plan and adapted plan was compared for ArcCHECK and EPID. In most cases, it was found that the EPID is more sensitive in detecting plan differences. Therefore, we conclude that EPID provides a viable alternative for QA of the adapted head and neck plans and should be further explored.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".