Universal evaluation of MLC models in treatment planning systems based on a common set of dynamic tests
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility of characterising MLCs and MLC models implemented in TPSs using a common set of dynamic beams. MATERIALS AND METHODS: A set of tests containing synchronous (SG) and asynchronous sweeping gaps (aSG) was distributed among twenty-five participating centres. Doses were measured with a Farmer-type ion chamber and computed in TPSs, which provided a dosimetric characterisation of the leaf tip, tongue-and-groove, and MLC transmission of each MLC, as well as an assessment of the MLC model in each TPS. Five MLC types and four TPSs were evaluated, covering the most frequent combinations used in radiotherapy departments. RESULTS: Measured differences within each MLC type were minimal, while large differences were found between MLC models implemented in clinical TPSs. This resulted in some concerning discrepancies, especially for the HD120 and Agility MLCs, for which differences between measured and calculated doses for some MLC-TPS combinations exceeded 10%. These large differences were particularly evident for small gap sizes (5 and 10 mm), as well as for larger gaps in the presence of tongue-and-groove effects. A much better agreement was found for the Millennium120 and Halcyon MLCs, differences being within ± 5% and ± 2.5%, respectively. CONCLUSIONS: The feasibility of using a common set of tests to assess MLC models in TPSs was demonstrated. Measurements within MLC types were very similar, but TPS dose calculations showed large variations. Standardisation of the MLC configuration in TPSs is necessary. The proposed procedure can be readily applied in radiotherapy departments and can be a valuable tool in IMRT and credentialing audits.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".