Improving the modelling of a multi-leaf collimator with tilted leaf sides used in radiotherapy
Bibliographic record
Abstract
Background and purpose: Multi-leaf collimators (MLCs) with tilted leaf sides have a complex transmission behaviour that is not easily matched by radiotherapy treatment planning systems (TPSs). We sought to develop an MLC model that can accurately match test fields and clinically relevant plans at different centres. Materials and methods: Two new MLC models were developed and evaluated within a research version of a commercial TPS. Prototype I used adjusted-constant transmissions and Prototype II used variable transmissions at the tongue-and-groove and leaf-tip regions. Three different centres evaluated these prototypes for a tilted MLC and compared them with their initial MLC model using test fields and patient-specific quality-assurance measurements of clinically relevant plans. For the latter, gamma passing rates (GPR) at 2 %/2mm were recorded. Results: For the prototypes the same set of MLC parameters could be used at all centres, with only a slight adjustment of the offset parameter. For centres A and C, average GPR were >95 % and within 0.5 % GPR difference between the standard, and prototype models. In center B, prototypes I and II improved the agreement in clinically relevant plans, with an increase in GPR of 2.3 % ± 0.8 % and 3.0 ± 0.8 %, respectively. Conclusions: The prototype MLC models were either similar or superior to the initial MLC model, and simpler to configure because fewer trade-offs were required. Prototype I performed comparably to the more sophisticated Prototype II and its configuration can be easily standardized, which can be useful to reduce variability and improve safety in clinical practice.
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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".