A comparison of two commercial treatment‐planning systems for IMRT
Bibliographic record
Abstract
This study compared the clinical functionality of BrainSCAN (BrainLAB) and Helios (Eclipse, Varian) for intensity-modulated radiation therapy (IMRT) treatment planning with the aim of identifying practical and technical issues. The study considered implementation and commissioning, dose optimization, and plan assessment. Both systems were commissioned for the same 6 MV photon beam equipped with a high-resolution multileaf collimator (Varian Millennium 120 leaf). The software was applied to three test plans having identical imaging and contour data. Analysis considered 3D axial dose distributions, dose-volume histograms, and monitor unit calculations. Each system requires somewhat different input data to characterize the beam prior to use, so the same data cannot be used for commissioning. In addition, whereas measured beam data was entered directly into Helios with minimal data processing, the BrainSCAN system required configured beam data to be sent to BrainLAB before clinical use. One key difference with respect to system commissioning was that BrainSCAN required high resolution data, which necessitated the use of detectors with small active volumes. This difference was found to impact on the ability of the systems to accurately calculate dose for highly modulated fields, with BrainSCAN being more successful than Helios. In terms of functionality, the BrainSCAN system uses a dynamically penalized likelihood inverse planning algorithm and calculates four plans at once with various relative weighting of the planning target and organ-at-risk volumes. Helios uses a gradient algorithm that allows the user to make changes to some of the input parameters during optimization. An analysis of the dosimetry output shows that, although the systems are different in many respects, they are each capable of producing substantially equivalent dose plans in terms of target coverage and normal tissue sparing.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".