The quality assurance of volumetric modulated arc therapy (<scp>VMAT</scp>) plans for early stage prostate cancer: a technical note
Bibliographic record
Abstract
Abstract As radiation therapy transitions from intensity modulated radiation therapy (IMRT) to volumetric modulated arc therapy (VMAT) it is important to consider the quality assurance (QA) of VMAT plans in light of what has previously been learned and developed in IMRT QA. This technical note assesses if IMRT based plan QA software, which has reduced the need in IMRT for phantom dose measurements on the linear accelerator, can be incorporated into VMAT QA processes. Twenty prostate cases were retrospectively planned using VMAT with one arc to deliver a prescription of 74 Gy in 37 fractions. A plan QA was performed using both IMSure (version 3.3), a software‐based IMRT QA program, and ArcCHECK (version 6.2.3.5713), a phantom‐based VMAT QA tool. Outcomes assessed included the time needed to perform the QA of both the IMSure and ArcCHECK QA methods, and agreement between planned dose and QA measured dose. On average per case, the ArcCHECK technique needed 31.5 min to perform the VMAT plan QA, while IMSure required 3.5 min to perform the same QA. All 20 cases passed dosimetric QA using ArcCHECK. However, using IMSure, three cases failed dosimetric QA using the departments existing IMRT QA criteria. This research has demonstrated that the IMRT QA software IMSure may be incorporated into the QA of VMAT plans, however the criteria to assess the dosimetry of the VMAT plans may need to be different to that for IMRT cases. The implication of this research for radiation therapists is to be critically aware of the differences between the plan QA requirements and methods for IMRT and those required for VMAT.
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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 | 0.005 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".