Simulated daily plan adaptation for magnetic resonance-guided liver stereotactic body radiotherapy
Bibliographic record
Abstract
INTRODUCTION: Liver cancers are challenging to treat using image-guided radiotherapy (IGRT) due to motion and deformation of target volumes and organs at risk (OARs), as well as difficulties in visualising liver tumours using cone-beam computed tomography (CBCT) based IGRT. Liver cancer patients may thus benefit from magnetic resonance (MR)-guided daily adaptive re-planning. We evaluated the dosimetric impact of a daily plan adaptation strategy based on daily MR imaging versus CBCT-based IGRT. METHODS: Ten patients were studied who were treated with CBCT-guided five-fraction stereotactic body radiotherapy (SBRT) and underwent MR imaging before each fraction. Simulated reference plans were created on computer tomography (CT) images and adapted plans were created on the daily MR images. Two plan adaptation strategies were retrospectively simulated: (1) translational couch shifts to match liver, mimicking standard CBCT guidance and (2) daily plan adaptation based on reference plan clinical goals and daily target and OAR contours. Dose statistics were calculated for both strategies and compared. RESULTS: Couch shifts resulted in an average reduction in GTV D99% relative to reference plan values of 5.2 Gy (-12.5% of reference values). Daily plan adaptation reduced this to 0.8 Gy (-2.0%). For six patients who were OAR dose-limited on reference plans, couch shifts resulted in OAR dose violations in 28 out of 28 simulated fractions, respectively; no violations occurred using daily plan adaptation. No OAR dose violations occurred using either strategy for the four cases not OAR dose-limited at reference planning. CONCLUSIONS: MR-guided daily plan adaptation ensured OAR dose constraints were met at all simulated treatment fractions while CBCT-based IGRT resulted in a systematic over-dosing of OARs in patients whose doses were limited by OAR dose at the time of reference planning.
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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) | 0.001 | 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".