Current practices and perspectives on the integration of contrast agents in MRI-guided radiation therapy clinical practice: A worldwide survey
Bibliographic record
Abstract
Aims: The introduction of on-line magnetic resonance image-guided radiotherapy (MRIgRT) has led to an improvement in the therapeutic workflow of radiotherapy treatments thanks to the better visualization of therapy volumes assured by the higher soft tissue contrast. Magnetic Resonance contrast agents (MRCA) could improve the target delineation in on-line MRIgRT planning as well as reduce inter-observer variability and enable innovative treatment optimization protocols. The aim of this survey is to investigate the utilization of MRCA among centres that clinically implemented on-line MRIgRT technology. Methods: In September 2021, we conducted an online survey consisting of a sixteen-question questionnaire that was distributed to the all the hospitals around the world equipped with MR Linacs. The questionnaire was developed by two Italian 0.35 T and 1.5 T MR-Linac centres and was validated by four other collaborating centres, using a Delphi consensus methodology. Results: The survey was distributed to 52 centres and 43 centres completed it (82.7%). Among these centres, 23 institutions (53.5%) used the 0.35T MR-Linac system, while the remaining 20 (46.5%) used the 1.5T MR-Linac system.According to results obtained, 25 (58%) of the centres implemented the use of MRCA for on-line MRIgRT. Gadoxetate (Eovist®; Primovist®) was reported to be the most used MRCA (80%) and liver the most common site of application (58%). Over 70% of responders agreed/strongly agreed to the need for international guidelines. Conclusions: The use of MRCA in clinical practice presents several pitfalls and future research will be necessary to understand the actual advantage derived from the use of MRCA in clinical practice, their toxicity profiles and better define the need of formulating guidelines for standardising the use of MRCA in MRIgRT workflow.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".