Simultaneous implementation of unrelated tumour sites on the MR Linac: A review of the commissioning process from a radiographer perspective and lessons learned
Bibliographic record
Abstract
INTRODUCTION: This work reports on a systematic approach to select MRI sequences, quantify inter-observer image registration variation and determine patient positioning for the clinical implementation of MR-guided adaptive radiotherapy (MRgRT) in patients with oropharyngeal (H&N) and lung cancer. METHODS: A total of 30 participants (N=10 H&N and N=10 lung cancer patients and N=10 healthy participants) were scanned on the Elekta Unity Magnetic Resonance Linear Accelerator (MRL). Participant experience questionnaires were used to determine the most appropriate positioning device for lung treatments and tolerability of H&N immobilization devices within the confined MR Linac environment. Visual guided assessments (VGAs) completed by three observers (one oncologist and two radiographers) were used to determine the most suitable tissue weighting (using vendor-provided 3D T1w and T2w sequences) for online image registration. Offline MRI to CT and MRI to MRI rigid registrations were undertaken by nine radiographers using bony and soft tissue matching. Single-factor ANOVA and paired t-tests were utilized to determine the interobserver variation. RESULTS: Based on oncologist and patient feedback, lung cancer patients would be treated in a vac-bag with their arms by their sides, while H&N cancer patients would be immobilized using a 5-point fixation device and 5-point personalized thermoplastic shell. There was no clear preference for T1w or T2w images in the H&N cohort. However, observers preferred T2w sequences for tumour and organ at risk (OAR) visualization in the lung images. When a bony match was conducted, single-factor ANOVA tests showed no statistically significant differences between all H&N image registration types (p=0.09). For the soft-tissue registrations, T1w-CT and T1w-T1w registrations showed a statistically significant (p=0.01) reduction in inter-observer variability over T2w-CT registrations. Paired t-tests showed no statistically significant differences for bony or soft tissue matches using T1w or T2w sequences to the planning CT in the lung cohorts (p=0.63 and p=0.52, respectively). CONCLUSION: We describe the systematic approach to the selection of strategies for imaging, immobilization, and online image registration we used for H&N and lung cancer treatments on the MRL. This has facilitated the selection of the most appropriate adaptive MRgRT strategies for treating these sites at our institution.
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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 | 0.037 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".