Poster — Thur Eve — 55: GRRCC‐Monte Carlo Treatment Planning Research Environment (MCTPRE)
Bibliographic record
Abstract
The implementation of Monte Carlo dose calculation algorithms in clinical radiotherapy treatment planning systems has been anticipated for many years however, its introduction into routine clinical practice has been delayed by the extent of calculation time required. With the advent of faster computers, Monte Carlo (MC) algorithms will become a standard calculation algorithm in clinical treatment planning systems. The purpose of this work was to develop a Monte Carlo based radiotherapy treatment planning research environment (MCTPRE) that uses patient‐specific computed tomography (CT) dataset. We have developed a windows based graphical user interface (GUI) that makes it very easy to import patient specific plans from a treatment planning system in either DICOM or RTOG format to a MC treatment planning research environment and can also be used to simulate simple patient specific treatment plans. The MCTPRE uses the BEAMnrcMP Monte Carlo code, which is based on the underlying ESGnrcMP particle transport code to simulate the Varian Clinac 21EX accelerator treatment head for 6 and 15MV photons and 6–20 MeV electron beams. The DOSXYZnrc Monte Carlo code is use for patient specific phantom dose calculations. We compared dose distributions from calculations done with a TPS and with the Monte Carlo code. The GUI incorporating the Monte Carlo code is a very useful research tool for benchmarking treatment planning systems (TPS), testing technique development, and dose verification. Future development will include a direct interface to a TPS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.323 | 0.136 |
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".