Practice-based training strategy for therapist-driven prostate MR-Linac adaptive radiotherapy
Bibliographic record
Abstract
Purpose: To develop a practice-based training strategy to transition from radiation oncologist to therapist-driven prostate MR-Linac adaptive radiotherapy. Methods and materials: In phase 1, 7 therapists independently contoured the prostate and organs-at-risk on T2-weighted MR images from 11 previously treated MR-Linac prostate patients. Contours were evaluated quantitatively (i.e. Dice similarity coefficient [DSC] calculated against oncologist generated online contours) and qualitatively (i.e. oncologist using a 5-point Likert scale; a score ≥ 4 was deemed a pass, a 90% pass rate was required to proceed to the next phase). Phase 2 consisted of supervised online workflow with therapists required no intervention from the oncologist on 10 total cases to advance. Phase 3 involved unsupervised therapist-driven workflow, with offline support from oncologists prior to the next fraction. Results: In phase 1, the mean DSC was 0.92 (range 0.85-0.97), and mean Likert score was 3.7 for the prostate. Five therapists did not attain a pass rate (3-5 cases with prostate contour score < 4), underwent follow-up one-on-one review, and performed contours on a further training set (n = 5). Each participant completed a median of 12 (range 10-13) cases in phase 2; of 82 cases, minor direction were required from the oncologist on 5 regarding target contouring. Radiation oncologists reviewed 179 treatment fractions in phase 3, and deemed 5 cases acceptable but with suggestions for next fraction; all other cases were accepted without suggestions. Conclusion: A training stepwise program was developed and successfully implemented to enable a therapist-driven workflow for online prostate MR-Linac adaptive radiotherapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".