Tolerances on MLC leaf position accuracy for IMRT delivery with a dynamic MLC
Bibliographic record
Abstract
The objective determination of performance standards for radiation therapy equipment requires, ideally, establishing the quantitative relationship between performance deviations and clinical outcome or some acceptable surrogate. In this simulation study the authors analyzed the dosimetric impact of random (leaf by leaf) and systematic (entire leaf bank) errors in the position of the MLC leaves on seven clinical prostate and seven clinical head and neck IMRT plans delivered using a dynamic MLC. In-house software was developed to incorporate normally distributed errors of up to +/- 2 mm in individual leaf position or systematic errors (+/- 1 and +/- 0.5 mm in all leaves of both leaf banks or +1 mm in one bank only) into the 14 plans, thus simulating treatment delivery using a suboptimally performing MLC. The dosimetric consequences of suboptimal MLC performance were quantified using the equivalent uniform doses (EUDs) of the clinical target volumes and important organs at risk (OARs). The deviation of the EUDs of the selected structures as the performance of the MLC deteriorated was used as the objective surrogate of clinical outcome. Random errors of 2 mm resulted in negligible changes for all structures of interest in both sites. In contrast, systematic errors can lead to potentially significant dosimetric changes that may compromise clinical outcome. If a 2% change in EUD of the target and 2 Gy for the OARs were adopted as acceptable levels of deviation in dose due to MLC effects alone, then systematic errors in leaf position will need to be limited to 0.3 mm. This study provides guidance, based on a dosimetric surrogate of clinical outcome, for the development of one component, leaf position accuracy of performance standards for multileaf collimators.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".