SU-D-108-01: An Efficient and Robust Algorithm for Catheter Optimization in High Dose Rate Brachytherapy
Bibliographic record
Abstract
Purpose: We present a simple, fast and robust method to optimize both the number and position of catheters in interstitial high dose rate (HDR) brachytherapy, using a modified version of the Centroidal Voronoi Tessellations algorithm. Methods: 8 HDR clinical cases were chosen randomly for both prostate and breast to test our method. The dose distributions were obtained using a research version of IPSA. Clinically relevant dosimetric parameters were computed to evaluate our method and test the robustness. For the prostate, plans generated from our method were compared to the clinical cases with 17 catheters. The efficiency of the algorithm was also tested with breast cases. The robustness of the method to implantation error was evaluated using 100 iterations and an error of 1, 2, 3, or 5 mm to each catheter of the plan. Results: A better or equal prostate V100 was obtained with as few as 12 catheters when compared with the clinical case. Plans with 9 or less catheters would not be clinically acceptable. Plans with 17 catheters were better than the clinical plans with the same number of catheters. The computation time to obtain 10 complete treatment plans ranging from 9 to 18 catheters, with the corresponding dosimetric indices, was 90 s. For the breast, on average, the RTOG recommendations would be satisfied with 12 catheters. Plans with 9 or less catheters would not be clinically acceptable in terms of V100, DHI and D90. Implantation errors up to 3 mm were acceptable. Conclusion: We have devised a simple, fast, robust 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. Funding support: CIHR and NSERC
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".