International Workshop on Current Topics in Monte Carlo Treatment Planning
Bibliographic record
Abstract
The use of Monte Carlo particle transport simulations in radiotherapy was pioneered in the early nineteen-seventies, but it was not until the eighties that they gained recognition as an essential research tool for radiation dosimetry, health physics and later on for radiation therapy treatment planning. Since the mid-nineties, there has been a boom in the number of workers using MC techniques in radiotherapy, and the quantity of papers published on the subject. Research and applications of MC techniques in radiotherapy span a very wide range from fundamental studies of cross sections and development of particle transport algorithms, to clinical evaluation of treatment plans for a variety of radiotherapy modalities. The International Workshop on Current Topics in Monte Carlo Treatment Planning took place at Montreal General Hospital, which is part of McGill University, halfway up Mount Royal on Montreal Island. It was held from 3-5 May, 2004, right after the freezing winter has lost its grip on Canada. About 120 workers attended the Workshop, representing 18 countries. Most of the pioneers in the field were present but also a large group of young scientists. In a very full programme, 41 long papers were presented (of which 12 were invited) and 20 posters were on display during the whole meeting. The topics covered included the latest developments in MC algorithms, statistical issues, source modelling and MC treatment planning for photon, electron and proton treatments. The final day was entirely devoted to clinical implementation issues. Monte Carlo radiotherapy treatment planning has only now made a slow entrée in the clinical environment, taking considerably longer than envisaged ten years ago. Of the twenty-five papers in this dedicated special issue, about a quarter deal with this topic, with probably many more studies to follow in the near future. If anything, we hope the Workshop served as an accelerator for more clinical evaluation of MC applications. The remainder of the papers in this issue demonstrate that there is still plenty of work to be undertaken on other topics such as source modelling, calculation speed, data analysis, and development of user-friendly applications. We acknowledge the financial support of the National Cancer Institute of Canada, the Institute of Cancer Research of the Canadian Institutes of Health Research, the Research Grants Office and the Post Graduate Student Society of McGill University, and the Institute of Physics Publishing (IOPP). A final word of thanks goes out to all of those who contributed to the successful Workshop: our local medical physics students and staff, the many colleagues who acted as guest associate editors for the reviewing process, the IOPP staff, and the authors who generated new and exciting work. We are also grateful for the endorsement of the Workshop by the International Atomic Energy Agency, the Canadian Organization of Medical Physicists and the American Association of Physicists in Medicine. [This line was added to the editorial on 23 March 2005.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.014 |
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".