Dosimetric evaluation of lung treatment plans produced by the Prowess Panther system using Monte Carlo simulation
Bibliographic record
Abstract
Abstract Purpose: This study evaluated the accuracy of lung dose calculation done by the fast photon effective (FPE) and the collapsed cone convolution (CCC) algorithms of the Prowess Panther treatment planning system (TPS) using Monte Carlo (MC) simulation. Materials and methods: A set of treatment plans of test cases including an acrylic phantom, the QUASAR multi-purpose body phantom, and one lung cancer patient, were created the system to assess the accuracy of the FPE and CCC algorithms. The DICOM-RT files of the plans were imported to the EGSnrc-based Monte Carlo simulation for dose calculations. The plans generated by the TPS and Monte Carlo simulation were compared using relative dose error comparison and 3D gamma index. The gamma index, using global methods, was implemented in PTW-VeriSoft with 3%/3 mm, 2%/2 mm criteria. Results: There was a good agreement between Monte Carlo-simulated and TPS-calculated doses for both the QUASAR multi-purpose body phantom and one lung cancer patient. However, discrepancies for the FPE algorithm were found to be 10% in the inhomogeneous medium such as the lung. Conclusions: The FPE algorithm may not accurately predict the dose distributions in and near the inhomogeneous structures. Monte Carlo simulation and CCC algorithm are more accurate than the FPE algorithm in calculating the dose in an inhomogeneous medium. The FPE and CCC algorithms must be validated before clinical implementation of the system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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 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".