Sci—Sat AM: Brachy — 10: Adaptation of the CVT algorithm for catheter optimization in high dose rate brachytherapy
Bibliographic record
Abstract
INTRODUCTION: In interstitial high dose rate (HDR) brachytherapy, the number and positions of the catheter are usually fixed by the use of a template, without considering tumor size and shape. In this work, we present a simple and fast method to optimize both the number and position of catheters, using a modified version of the Centroidal Voronoi Tessellations (CVT) algorithm. METHODS: 8 prostate HDR clinical cases were chosen randomly to test our method. The treatment plan was obtained from a research version of IPSA. Clinically relevant dosimetric parameters were computed to evaluate our method and help optimizing the CVT algorithm parameters. Plans were generated with a specified number of catheters ranging from 9 to 18 and compared to the clinical cases with 17 catheters. RESULTS: The computation time to optimize the positions of a specific number of catheters was 1.5 s. The prostate V100 was better than the clinical case up to 12 catheters. Plans with 9 or less catheters would not be clinically acceptable in terms of prostate V100 and D90. High conformity is achieved whether the number of catheters used. The V75 of the bladder seems slightly higher, but not significant clinically. All other dosimetric indices are as good as the clinical plan. CONCLUSION: We have devised a simple, fast and efficient method to optimize the number and position of catheters in HDR brachytherapy. Ultimately, this catheter optimization algorithm could be coupled with a 3D ultrasound system to allow real-time guidance and planning for any interstitial brachytherapy sites.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".