SU‐GG‐T‐315: McGill Monte Carlo Research Platform (MMCTP) for Dose Comparison Studies
Bibliographic record
Abstract
Purpose: To demonstrate recent improvements in the functionality of MMCTP, a radiotherapy research platform GUI, with integrated Monte Carlo (MC) dose calculation submission and dose analysis tools. MMCTP allows for quick MC dose calculations from standard radiotherapy formats. Clinical and MC dose distributions are analyzed under the same platform, which eliminates inherent planning system tool dependent effects. History: MMCTP has been internally distributed within the last year. External distribution, which has so far been limited to close contacts, will be open to the public. DICOM‐RT plans, which include: images, structures, dose distributions and beam information, have been successfully imported from TPS such as: Eclipse, Corvus and Tomotherapy. MC calculations use the images structures, and beam information to generate a beam and patient model. The GUI is capable of handling the following beam parameters: dynamic wedge, static/dynamic MLC, and static wedge. MC dose distributions are normalized to absolute dose with the beam monitor units and directly compared to the imported clinical dose distributions. The GUI includes a set of built in comparison tools: isodose lines and colour‐wash displays in transverse/sagittal/coronal view, DVH calculations, dose difference maps and dose profile graphs. Validation has been extensive and includes profile comparisons between MC and measured data. Results: Patient recalculations include a set of: conformal lung and breast patients, gated high dose lung patients, IMRT head and neck patients. Recalculations times vary depending on the number and complexity of beams, size and resolution of patient model. Heterogeneous recalculations reveal differences between planned and delivered dose distributions. Conclusion: MMCTP is a flexible research platform for the development of patient specific MC treatment planning for photon and electron external beam radiation therapy. The visualization, dose analysis tools offer extensive possibility for plan analysis and comparison to plans imported from commercial TPS through well‐documented protocols such as DICOM‐RT.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.023 |
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".