Poster - Thurs Eve-07: The dosimetric consequences of MLC position inaccuracy in IMRT
Bibliographic record
Abstract
The Multileaf Collimator (MLC), the most widely used means of intensity modulating beams for IMRT, is subject to random and systematic errors in leaf positions that may compromise the treatment quality. This work is a simulation study of the effect of random and systematic errors in leaf position on delivered dose distributions. The dosimetric effects of random errors of up to 2 mm and systematic errors (±1mm in 2 banks, ±0.5mm in 2 banks and 2mm in 1 bank of leaves) were analysed for a typical head and neck IMRT plan and a typical prostate IMRT plan. Dose Volume Histograms and Equivalent Uniform Doses (EUD) of the target volumes, bladder and rectum for the prostate plan and brainstem, larynx, parotids and spinal cord for the head and neck plan were calculated with and without MLC positioning errors and compared. The results show that if we adopt a 2% change in EUD of the target and 2 Gy for the OARs as acceptable levels of uncertainty in dose due to MLC effects only, then random errors of up to 2mm may be tolerated but systematic errors in leaf position will need to be limited to 0.5mm. Our study provides guidance, based on a surrogate of clinical outcome, for the development of quality control 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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".