Poster - Thurs Eve-36: Use of multileaf collimator as a replacement of physical missing tissue compensator
Bibliographic record
Abstract
Missing tissue compensators are used to improve dose uniformity for some patients undergoing radiation therapy. Currently, our practice is to machine compensators out of lead alloy plate. Replacing this physical filter with a segmented multileaf collimator (MLC) delivery sequence is beneficial in terms of work flow and delivery efficiency. The purpose of this work is to compare the dose uniformity achieved by fields that are either (A) conventionally compensated, compensated by segmenting the physical compensator thickness map into either (B) step-and-shoot or (C) dynamic MLC delivery sequences using an in-house sequencer, (D) compensated using Pinnacle sequencer, or (E) compensated using IMRT optimization. A computer program was developed to construct both step-and-shoot and dynamic MLC sequence files from mechanical thickness maps of our current compensators. In addition, the Pinnacle sequencer and IMRT optimization were used to generate step-and-shoot MLC sequences. Planar doses were measured for each at the isocenter depth with an ion chamber array to compare the five methods. A comparison of the relative dose distribution shows that the compensation achieved by method (E) is in close agreement with that achieved using method (A), that is, dose uniformity within 4%. Method (D) resulted in the shortest delivery time and achieved dose uniformity to within 5%. Methods (B) and (C) need additional refinement to be of practical use. The results support the feasibility of replacing physical compensators with MLC delivery sequences. Compensation by MLC segments provides more flexibility and efficiency in design and delivery than by physical compensators while maintaining or improving the uniformity of dose to the plane of compensation.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".