International survey; current practice in On-line adaptive radiotherapy (ART) delivered using Magnetic Resonance Image (MRI) guidance
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The uptake of new technologies has varied internationally and there have often been barriers to implementation. On-line adaptive radiotherapy (ART) promises to improve patient outcome. This survey focuses on the implementation phase of delivering ART and professional roles and responsibilities currently involved in the workflow and changes which may be expected in the future. MATERIALS AND METHODS: A 38 question survey included aspects on current practice; professional responsibilities; benefits and barriers; and decision making and responsibilities. For the purposes of the questionnaire and paper, ART was considered where tumour and /or organs at risk were contoured and re-planning was performed on-line. The questionnaire was electronically distributed via radiotherapy networks. RESULTS: Nineteen international responses were received. Europe (n = 11), United States of America (n = 4); Canada (n = 2), Australia (n = 1) and Hong Kong (n = 1). The majority of centres started using ART in either 2018 (n = 7) or 2019 (n = 6). Four centres started treating with ART between 2015 and 2017, and the first was in 2014. Centres initially treated prostate and oligometastases patients, expanding to treat prostate, oligometastases, pancreas and rectum. The majority of centres were working in conventional roles, however moving towards radiographers taking more responsibility in contouring organs at risk (OAR), target and dosimetry. The three most important criteria chosen by medical doctors to determine if ART should be used were overall gross anatomy changes of target and OAR, target not covered by planning target volume (PTV) and OAR close to the high dose area. There was no clear consensus on the minimum improvement in dose to target or reduction in dose to OAR to warrant adaption. CONCLUSION: On-line ART has been implemented successfully internationally. Initial practice maintains conventional professional roles and responsibilities, however there is trend to changing roles for the future. There is little consensus regarding the triggers of adaption.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".