TH‐E‐224C‐04: MMCTP, a Radiotherapy Research Environment for Monte Carlo and Patient‐Specific Treatment Planning
Bibliographic record
Abstract
Purpose: To develop a flexible software package, on low cost hardware with the aim of integrating new patient specific treatment planning with Monte Carlo dose calculation suitable for large‐scale prospective and retrospective treatment planning studies. Programming Philosophy: The McGill Monte Carlo Treatment Planning system (MMCTP) is designed as a software environment for the research development of patient specific treatment planning. The design includes a workstation GUI for treatment planning tools, and anonymous access to standard low cost hardware for MC dose calculation. Results: Before using MMCTP, treatment plans are converted into the so‐called McGill RT format. This new file structure was designed for saving patient plans on the workstation. The current MMCTP features are: (a) DICOM and RTOG imports; (b) transverse/sagittal/coronal slice viewing for contours, CT scans, dose distributions; (c) contouring tools; (d) colour‐wash and isodose line display; (e) DVH analysis, and dose matrix comparison tools; (f) external beam editing; (g) thumbnail CT navigation tool; (h) EGS/Beam calculation and XVMC patient transport for photon and electron beams. MMCTP uses a two‐step process to generate MC dose distributions. The MC module controls egs/Beam and XVMC calculations. Input files, prepared from the beam geometry, are uploaded and run on the cluster using shell commands. Upon completion of XVMC, the GUI downloads individual dose files. Conclusion: The MMCTP GUI provides a flexible research platform for the development of patient specific MC treatment planning for photon and electron external beam radiation therapy. MMCTP uses an internal storage format that is flexible in that it allows for multi‐instance multi‐modality image information useful in the planning process. The visualization, dose matrix operation and DVH tools offer extensive possibility for plan analysis and comparison to plans imported from commercial treatment planning systems through well‐documented image storage protocols such as DICOM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.034 |
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".