Biological consequences of MLC calibration errors in IMRT delivery and QA
Bibliographic record
Abstract
PURPOSE: The purpose of this work is threefold: (1) to explore biological consequences of the multileaf collimator (MLC) calibration errors in intensity modulated radiotherapy (IMRT) of prostate and head and neck cancers, (2) to determine levels of planning target volume (PTV) and normal tissue under- or overdose flagged with clinically used QA action limits, and (3) to provide biologically based input for MLC QA and IMRT QA action limits. METHODS: Ten consecutive prostate IMRT cases and ten consecutive head and neck IMRT cases were used. Systematic MLC offsets (i.e., calibration error) were introduced for each control point of the plan separately for X1 and X2 leaf banks. Offsets were from - 2 to 2 mm with a 0.5 mm increment. The modified files were imported into the planning system for forward dose recalculation. The original plan served as the reference. The generalized equivalent uniform dose (gEUD) was used as the biological index for the targets, rectum, parotid glands, brainstem, and spinal cord. Each plan was recalculated on a CT scan of a 27 cm diameter cylindrical phantom with a contoured 0.6 cc ion chamber. Dose to ion chamber and 3D gamma analysis were compared to the reference plan. QA pass criteria: (1) at least 95% of voxels with a dose cutoff of 50% of maximum dose have to pass at 3 mm/3% and (2) dose to chamber within 2% of the reference dose. RESULTS: For prostate cases, differences in PTV and rectum gEUD greater than 2% were identified. However, a larger proportion of plans leading to greater than 2% difference in prostate PTV gEUD passed the ion chamber QA but not 3D gamma QA. A similar trend was found for the rectum gEUD. For head and neck IMRT, the QA pass criteria flagged plans leading to greater than 4% differences in PTV gEUD and greater than 5% differences in the maximum dose to brainstem. If pass criteria were relaxed to 90% for gamma and 3% for ion chamber QA, plans leading to a 5% difference in PTV gEUD and a 5%-8% difference in brainstem maximum dose would likely pass IMRT QA. A larger proportion of head and neck plans with greater than 2% PTV gEUD difference passed 3D gamma QA compared to ion chamber QA. CONCLUSIONS: For low modulation plans, there is a better chance to catch MLC calibration errors with 3D gamma QA rather than ion chamber QA. Conversely, for high modulation plans, there is a better chance to catch MLC calibration errors with ion chamber QA rather than with 3D gamma QA. Ion chamber and 3D gamma analysis IMRT QA can detect greater than 2% change in gEUD for PTVs and critical structures for low modulation treatment plans. For high modulation treatment plans, ion chamber and 3D gamma analysis can detect greater than 2% change in gEUD for PTVs and a 5% change in critical structure gEUD since either QA methods passes the QA criteria. For gEUD changes less than those listed above, either QA method has the same proportion of passing rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".