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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".