SU‐E‐T‐231: Cross‐Validation of 3D Gamma Comparison Tools
Bibliographic record
Abstract
Purpose: Moving the computational analysis for 3D gel dosimetry into the 3D Slicer (www.slicer.org) environment has made gel dosimetry more clinically accessible. To ensure accuracy, we cross‐validate the 3D gamma comparison module in 3D Slicer with an independently developed algorithm using simulated and measured dose distributions. Methods: Two reference dose distributions were generated using the Varian Eclipse treatment planning system. The first distribution consisted of a four‐field box irradiation delivered to a plastic water phantom and the second, a VMAT plan delivered to a gel dosimeter phantom. The first reference distribution was modified within Eclipse to create an evaluated dose distribution by spatially shifting one field by 3mm, increasing the monitor units of the second field, applying a dynamic wedge for the third field, and leaving the fourth field unchanged. The VMAT plan was delivered to a gel dosimeter and the evaluated dose in the gel was calculated from optical CT measurements. Results from the gamma comparison tool built into the SlicerRT toolbox were compared to results from our in‐house gamma algorithm implemented in Matlab (via MatlabBridge in 3D Slicer). The effects of noise, resolution and the exchange of reference and evaluated designations on the gamma comparison were also examined. Results: Perfect agreement was found between the gamma results obtained using the SlicerRT tool and our Matlab implementation for both the four‐field box and gel datasets. The behaviour of the SlicerRT comparison with respect to changes in noise, resolution and the role of the reference and evaluated dose distributions was consistent with previous findings. Conclusion: Two independently developed gamma comparison tools have been cross‐validated and found to be identical. As we transition our gel dosimetry analysis from Matlab to 3D Slicer, this validation serves as an important test towards ensuring the consistency of dose comparisons using the 3D Slicer environment.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".