SU‐E‐T‐498: Implementation of Clinical Monte Carlo Dose Calculation for CyberKnife On a Web‐Based Treatment Planning System WebTPS
Bibliographic record
Abstract
Purpose: The scope of this study is to implement an accurate Cyberknife model on a web‐based tool (WebTPS), which uses the EGSnrc Monte Carlo dose calculation engine. WebTPS will be mostly used as a reference to evaluate clinical treatment plans in highly heterogeneous phantoms. Methods: The WebTPS dose calculation module is linked to the user code DOSxyznrc. WebTPS automatically converts CyberKnife clinical plans to DOSxyznrc input files. Phantoms are created using a tissue segmentation method from HU‐ED calibrated curves and materials are assigned based on CT data and contours performed by radiation oncologists. Parallel computation is run on a high‐performance cluster (Compute Canada) to achieve reasonable simulation time. The CyberKnife model is built on the BEAMnrc system using manufacturer's specifications. Simulated and experimental data are compared to estimate the optimal electron beam parameters. The beam energy estimation is based on percent depth dose (PDD) data comparison, while the spot size is validated using output factor (OF) and off‐axis ratio (OAR) data. An egs_chamber model of a PTW60012 diode is used to simulate OF experimental measurements for different collimator sizes. Results: A preliminary linac model optimization yields a 0.5% agreement between experimental and simulation PDD data; a 0.5% or 1 mm agreement for OAR data and a 2% agreement for OF data. Full treatment plan simulations are achieved with the CyberKnife model using patient heterogeneous phantoms. Uncertainties under 1% are achieved for less than 2 hours of CPU time. Conclusion: This work aims to develop a suitable model for reference plan dose calculation. WebTPS will be used in several clinical and research applications where the CyberKnife embedded ray‐tracing algorithm show significant limitations. Further improvements are yet to be achieved to match experimental data to a level of 1%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".