An End-to-end Quality Assurance Procedure for Ethos Online Adaptive Radiotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose: Online adaptive radiation therapy (OART) poses unique challenges for quality assurance (QA), requiring innovative methodologies beyond traditional techniques. This study introduced an end-to-end (E2E) QA test for the Ethos OART system. Materials and Methods: Initial treatment plans were developed using deformed computed tomography (CT) images of standard phantoms. During treatment sessions, adaptive plans were created and delivered using undistorted physical QA phantoms equipped with measuring detectors. Our approach was demonstrated using standard QA phantoms - OCTAVIUS-four-dimensional (PTW, Freiburg, Germany), ArcCHECK (Sun Nuclear Corp., FL, USA), and the RUBY (PTW, Freiburg, Germany) - to evaluate the accuracy of contouring, synthetic CT (sCT), and dosimetry of adaptive plans in the Ethos OART system. Results: Our findings demonstrated the superior performance of the Ethos OART system, with a gamma pass rate exceeding 96% (2% local/2 mm) and point dose deviations below 0.5%. The Dice coefficients for body contours between the sCT and reference CT were above 0.9, and the sCT accuracy was confirmed by mean absolute errors of <27 Hounsfield unit. Conclusion: This approach establishes a straightforward E2E test to assess the workflow accuracies essential for preclinical validation/monthly QA of OART systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it