Feasibility of Therapist‐Driven <scp>MR</scp> ‐Guided Adaptive Radiotherapy for Oligometastatic Disease: Geometric Accuracy and Dosimetric Impact
Bibliographic record
Abstract
INTRODUCTION: MRI-guided adaptive radiation therapy (ART) is the resource-intensive process of daily treatment plan modification. This study aims to demonstrate the feasibility of radiation therapist (RT)-led MRI-guided ART for oligometastatic disease (OMD) by comparing geometric accuracy and dosimetric differences between RT and radiation oncologist (RO) re-contouring. METHODS: Five RTs and five ROs retrospectively re-contoured gross target volumes (GTVs) and organs-at-risk (OARs) for eight OMD cases. RT and RO contours were compared against consensus RO Simultaneous Truth and Performance Level Estimation (RO-STAPLE) contours using the Dice similarity coefficient (DICE), mean distance to agreement (MDA), planning target volume (PTV) D95 and OAR D0.5cc using the Wilcoxon signed-rank test. Moreover, an RO qualitatively scored all contours using a 5-point Likert scale. RESULTS: We found very good geometric accuracy with average (±standard deviation) GTV DICE of 0.82 ± 0.06 for RTs and 0.85 ± 0.09 for ROs and MDA of 0.88 ± 0.03 mm for RT and 0.75 ± 0.05 mm for ROs relative to the RO-STAPLE. Qualitative GTV Likert scores were excellent, 4.8/5 for RTs and 4.7/5 for ROs. Mean percent difference in PTV D95 compared to RO-STAPLE was small but significantly higher for RTs (0.5% ± 1.5%) compared with ROs (-0.7% ± 1.9%, p < 0.05). Mean relative change in OAR D0.5cc results was small with -1% ± 6% for RTs and -1% ± 12% for ROs. CONCLUSIONS: Here we provide the first report of geometric and dosimetric contouring uncertainty for MR-guided online ART for OMD. Our results show that RT re-contouring maintains similar performance for eligible targets and OARs compared with RO contours, establishing the initial feasibility of an RT-led 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".