The Montreal split ring applicator: Towards highly adaptive gynecological brachytherapy using 3D-printed biocompatible patient-specific interstitial caps
Bibliographic record
Abstract
Purpose: The addition of interstitial (IS) needles to intra-cavitary (IC) brachytherapy applicators is associated with improved outcomes in locally advanced cervical cancers involving parametrial tumor extensions. The purpose of this work was to validate a clinical workflow involving 3D-printed caps for a commercial IC split ring applicator that enable using IS needle trajectories tailored to each treatment. Material and methods: A dedicated software module was developed in this work allowing users to design patient-specific IS caps without knowledge of computer-aided design (CAD) software. This software module was integrated to 3D Brachy, a commercial software developed by Adaptiiv Medical Technologies Inc. For validation of the workflow, CAD models of ground truth caps with five IS needle trajectories were designed with Fusion 360™, 3D-printed, assembled with a split ring applicator, and CT-scanned with radio-opaque markers. 3D Brachy was then applied to generate a replica based on trajectories reconstructed from the radio-opaque markers. A comparison between ground truth and replicated IS needle trajectories was done using intersection points with planes at the level of the cervix (z = 0 cm) and a representative needle depth (z = 3 cm). Results: Prototypes of interstitial caps 3D-printed in both BioMed Amber and BioMed Clear SLA resins were tested to be functional both pre- and post-sterilization for IS needles with obliquity angles ≤ 45°. Distance-to-agreement at z = 0 cm and 3 cm as well as deviations in pitch and yaw angles of the five IS needle trajectories were found to have mean values of 3.3 ±2.1 mm, 7.3 ±2.0 mm, 2.9° ±2.3°, and 7.0° ±7.0°, respectively. Conclusions: The clinical workflow for image-guided adaptive cervical cancer brachytherapy using the Montreal split ring applicator was validated.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".