Commissioning of a Commercial Secondary Dose Check Software and Clinical Implementation for the Magnetic Resonance-guided Linear Accelerator Adaptive Workflow
Bibliographic record
Abstract
Purpose: The purpose of this study was to report the commissioning the secondary dose calculation software ThinkQA (TQA) for an magnetic resonance-guided linear accelerator (MR-linac). Methods: The Medical Physics Practice Guideline 5.a. (MPPG5a) tests, and dose in inhomogeneities, beam profiles, and depth dose curves were calculated and compared between Monaco and TQA. Five intensity modulated radiotherapy (IMRT) plans (anal, abdominal, head and neck, prostate, and lung), based on TG-244 guidelines were evaluated varying the gamma criteria. Furthermore, the initial and adapted plans for the first session for 17 patients in different anatomical regions were calculated in TQA using different gamma criteria. For five patients, six measurements were made at different fractions using ArcCheck and compared with TQA. Results: The majority of tests met the tolerances defined in the MPPG5a with the exception of dose profiles (>10%), and large multileaf collimator-shaped fields with extensive blocking (>2%). For the IMRT plans, tight criteria such as 2%/2 mm may not be suitable for all scenarios. Thus, we adopt a reasonable 3%/2 mm without compromising the quality of the plan that included significant high-to-low-density interfaces. It is observed that, the values obtained for clinical cases are in the range from 94.6% to 99.8% (TQA), 97.0% to 99.6% (ArcCheck), except in a prostate case with 87.8% (TQA) and 99.3% (ArcCheck). Conclusion: We commissioned TQA as a secondary dose calculation for MR-linac and we introduced it clinically for adaptive treatment workflow using 3%/2 mm with 95% as tolerance limit and 90% as action limit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".