TH‐AB‐BRA‐02: Generation of 3D Dosimetric Reference Datasets for Commissioning and Validation of 192Ir Brachytherapy Model‐Based Dose Calculation Software
Bibliographic record
Abstract
Purpose: Previously, generic HDR 192Ir source and shielded applicator were designed by WG‐ MBDCA, the AAPM‐ESTRO‐ABG Working Group on model‐based dose calculation algorithms. TPS vendors have incorporated both into their TPS. A charge of WG‐MBDCA is to develop a limited number of well‐defined test cases and perform MBDCA dose calculations and comparisons. Here, we describe the test cases proposed, the 3D dosimetric data generation process and the 3D gamma analysis performed. Methods: A voxelized phantom of 512×512×512 voxels (HU=‐1024,Air), with each voxel being 1×1×1 mm3, containing a concentric cubic contour structure (HU=0,Water) of dimension 201×201×201 voxels, was created in the DICOM‐ RT format. Three configurations were tested: i) source located at the center with surrounding structure assigned to water to mimic TG‐43 conditions, ii) source displaced +7 cm along the × axis, iii) source located at the center with the shielded applicator in place. Advanced Collapsed‐cone Engine (ACE, Elekta) and AcurosBV (Varian) were benchmarked. Reference datasets were independently produced by different groups using BrachyDose (1010 photons), GEANT4(4×1010), Penelope (2×1010), MCNP5(2 x1010), MCNP6(1011), and ALGEBRA(4×1010) Monte Carlo (MC) dose calculation platforms. Results: For the three configurations, MC agreed within 1% for the volume bounded by the 1% isodose surface; MCNP6 data sets were designated as reference (statistical error < 0.2% across the phantom). A 2%/1mm 3D γ‐ index criteria was used, within the volume bounded by 5% isodose surface, to account for both dose difference and source placement errors in TPS. Voxel pass rates were 95.2%, 99.5% and 90% for ACE and 98%, 98.4% and 95.9% for AcurosBV, respectively. Conclusion: The evaluated test cases are suitable for MBDCA QA. DICOM‐RT files are being integrated into the joint AAPM/IROC Houston registry to support end‐user TPS software commissioning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".