MMCTP: a radiotherapy research environment for Monte Carlo and patient-specific treatment planning
Bibliographic record
Abstract
Radiotherapy research lacks a flexible computational research environment for Monte Carlo (MC) and patient-specific treatment planning. The purpose of this study was to develop a flexible software package on low-cost hardware with the aim of integrating new patient-specific treatment planning with MC dose calculations suitable for large-scale prospective and retrospective treatment planning studies. We designed the software package 'McGill Monte Carlo treatment planning' (MMCTP) for the research development of MC and patient-specific treatment planning. The MMCTP design consists of a graphical user interface (GUI), which runs on a simple workstation connected through standard secure-shell protocol to a cluster for lengthy MC calculations. Treatment planning information (e.g., images, structures, beam geometry properties and dose distributions) is converted into a convenient MMCTP local file storage format designated, the McGill RT format. MMCTP features include (a) DICOM_RT, RTOG and CADPlan CART format imports; (b) 2D and 3D visualization views for images, structure contours, and dose distributions; (c) contouring tools; (d) DVH analysis, and dose matrix comparison tools; (e) external beam editing; (f) MC transport calculation from beam source to patient geometry for photon and electron beams. The MC input files, which are prepared from the beam geometry properties and patient information (e.g., images and structure contours), are uploaded and run on a cluster using shell commands controlled from the MMCTP GUI. The visualization, dose matrix operation and DVH tools offer extensive options for plan analysis and comparison between MC plans and plans imported from commercial treatment planning systems. 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. The impact of this tool lies in the fact that it allows for systematic, platform-independent, large-scale MC treatment planning for different treatment sites. Patient recalculations were performed to validate the software and ensure proper functionality.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.036 |
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".