Sci-Thur AM: Planning - 08: Validation of a commercial Monte Carlo code used for stereotactic radiosurgery and stereotactic body radiation therapy
Bibliographic record
Abstract
Our project consisted of validating the BrainLab iPlan Monte Carlo algorithm, used in conjunction with the stereotactic radiosurgery (SRS) mode of the Varian Novalis TX linear accelerator, for clinical use. Our approach was to "benchmark" the iPlan algorithm by comparing dose distributions with those obtained using a BEAMnrc model of the Novalis SRS mode. The BEAMnrc model was obtained by modifying an existing accelerator model to include the SRS flattening filter and source characteristics of the Novalis TX, and by reprogramming a component module to model the high definition 120-leaf multi-leaf collimator. The free parameters of interleaf air gap and leaf density were adjusted by matching to interleaf leakage profiles measured with EBT2 film. The BEAMnrc model was used to perform comparisons of depth dose curves and planar distributions for fields in homogeneous and heterogeneous slab phantoms between both MC codes and film. The source parameters of electron beam energy, size and angular spread were determined to be 6.6 MeV, 0.7 mm and 0.8 mm (cross and in-plane), and 1.27°, respectively. Comparisons between iPlan and EGSnrc MC codes show agreement within 2% for PDD curves, and a high pass rate (>98%) on gamma analysis (3%/3mm) for planar distributions, when the scored quantity is dose to medium. Discrepancies between both MC codes and film measurements were seen near bone inhomogeneities, where the film trend agrees somewhat with iPlan MC reporting dose-to-water. Further work is being performed to understand these differences and how film is used to measure dose near bone.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".