Comparison of PRIMO Monte Carlo code and Eclipse treatment planning system in calculation of dosimetric parameters in brain cancer radiotherapy
Bibliographic record
Abstract
Background: It is important to evaluate the dose calculated by treatment planning systems (TPSs) and dose distribution in tumor and organs at risk (OARs). The aim of this study is to compare dose calculated by the PRIMO Monte Carlo code and Eclipse TPS in radiotherapy of brain cancer patients. Materials and methods: PRIMO simulation code was used to simulate a Varian Clinac 600C linac. The simulations were validated for the linac by comparison of the simulation and measured results. In the case of brain cancer patients, the dosimetric parameters obtained by the PRIMO code were compared with those calculated by Eclipse TPS. Gamma function analysis with 3%, 3 mm criteria was utilized to compare the dose distributions. The evaluations were based on the dosimetric parameters for the planning target volume (PTV) and OAR including D min , D mean, and D max , homogeneity index (HI), and conformity index (CI). Results: The gamma function analysis showed a 98% agreement between the results obtained by the PRIMO code and measurement for the percent depth dose (PDD) and dose profiles. The corresponding value in comparing the dosimetric parameters from PRIMO code and Eclipse TPS for the brain patients was 94%, on average. The results of the PRIMO simulation were in good agreement with the measured data and Eclipse TPS calculations. Conclusions: Based on the results of this study, the PRIMO code can be utilized to simulate a medical linac with good accuracy and to evaluate the accuracy of treatment plans for patients with brain cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".