Poster — Wed Eve—38: Comparison of IMRT Plan Quality from Two Different Linear Accelerator
Bibliographic record
Abstract
The purpose of this work was to determine whether two different types of linear accelerators manufacturers with similar MLC leaf widths deliver equivalent IMRT dose distributions for head and neck radiotherapy patients. In the first study, a retrospective analysis of 197 head and neck IMRT patients delivered on Siemens Primus and Elekta Synergy machines was undertaken to test statistical differences in machine type, monitor units, maximum target dose, and number of target doses. Both machines have 1 cm MLC leaves but have different linac head geometries. Plan were created using the direct machine parameter optimization (DMPO) routine on Philips' Pinnacle treatment planning system (TPS) system, in step and shoot mode. A multi‐variate analysis was used to test for significance, Pearson's correlations, and coefficients of determination. In the second study, a replanning exercise was conducted where deliverable plans from a Siemens machine was re‐optimized with an Elekta machine and vice‐versa. In the first study, there was no evidence of any significant difference in the IMRT plans. Elekta machines delivered more MUs than the Siemens units but the difference was not significant ( ). In the second study, the presentation of the dose distributions, DVHs, and mean dose to target and normal tissue structures were equivalent. However, approximately 15% more monitor units were delivered when plans were planned or re‐planned on the Elekta machine. This work suggests that for plans of comparable quality, Elekta machines deliver more monitor units than Siemens machines, likely due to differences in the geometric properties of the machines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.008 | 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".