AAPM WGDCAB Report 372: A joint AAPM, ESTRO, ABG, and ABS report on commissioning of model‐based dose calculation algorithms in brachytherapy
Bibliographic record
Abstract
Abstract The introduction of model‐based dose calculation algorithms (MBDCAs) in brachytherapy provides an opportunity for a more accurate dose calculation and opens the possibility for novel, innovative treatment modalities. The joint AAPM, ESTRO, and ABG Task Group 186 (TG‐186) report provided guidance to early adopters. However, the commissioning aspect of these algorithms was described only in general terms with no quantitative goals. This report, from the Working Group on Model‐Based Dose Calculation Algorithms in Brachytherapy, introduced a field‐tested approach to MBDCA commissioning. It is based on a set of well‐characterized test cases for which reference Monte Carlo (MC) and vendor‐specific MBDCA dose distributions are available in a Digital Imaging and Communications in Medicine—Radiotherapy (DICOM‐RT) format to the clinical users. The key elements of the TG‐186 commissioning workflow are now described in detail, and quantitative goals are provided. This approach leverages the well‐known Brachytherapy Source Registry jointly managed by the AAPM and the Imaging and Radiation Oncology Core (IROC) Houston Quality Assurance Center (with associated links at ESTRO) to provide open access to test cases as well as step‐by‐step user guides. While the current report is limited to the two most widely commercially available MBDCAs and only for 192 Ir‐based afterloading brachytherapy at this time, this report establishes a general framework that can easily be extended to other brachytherapy MBDCAs and brachytherapy sources. The AAPM, ESTRO, ABG, and ABS recommend that clinical medical physicists implement the workflow presented in this report to validate both the basic and the advanced dose calculation features of their commercial MBDCAs. Recommendations are also given to vendors to integrate advanced analysis tools into their brachytherapy treatment planning system to facilitate extensive dose comparisons. The use of the test cases for research and educational purposes is further encouraged.
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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 | 0.000 | 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) | 0.000 | 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".