Daily adaptive radiotherapy for patients with prostate cancer using a high field MR-linac: Initial clinical experiences and assessment of delivered doses compared to a C-arm linac
Bibliographic record
Abstract
INTRODUCTION: MR-guided adapted radiotherapy (MRgART) using a high field MR-linac has recently become available. We report the estimated delivered fractional dose of the first five prostate cancer patients treated at our centre using MRgART and compare this to C-Arm linac daily Image Guided Radiotherapy (IGRT). METHODS: Patients were treated using adapted treatment plans shaped to their daily anatomy. The treatments were recalculated on an MR image acquired immediately prior to treatment delivery in order to estimate the delivered fractional dose. C-arm linac non-adapted VMAT treatment plans were recalculated on the same MR images to estimate the fractional dose that would have been delivered using conventional radiotherapy techniques using a daily IGRT protocol. RESULTS: 95% and 93% of mandatory target coverage objectives and organ at risk dose constraints were achieved by MRgART and C-arm linac delivered dose estimates, respectively. Both delivery techniques were estimated to have achieved 98% of mandatory Organ At Risk (OAR) dose constraints whereas for the target clinical goals, 86% and 80% were achieved by MRgART and C-arm linac delivered dose estimates. CONCLUSIONS: Prostate MRgART can be delivered using the a high field MR-linac. Radiotherapy performed on a C-arm linac offers a good solution for prostate cancer patients who present with favourable anatomy at the time of reference imaging and demonstrate stable anatomy throughout the course of their treatment. For patients with critical OARs abutting target volumes on their reference image we have demonstrated the potential for a target dose coverage improvement for MRgART compared to C-arm linac treatment.
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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 | 0.000 | 0.000 |
| 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".