MO‐F‐144‐03: Real‐Time Guidance and Planning in Breast High Dose Rate Brachytherapy Using 3D Ultrasound
Bibliographic record
Abstract
Purpose: In breast interstitial high dose rate (HDR) brachytherapy, the number and positions of the catheters are usually chosen manually using a preimplant CT scan. In this work, we present an innovative approach for real‐time 3D ultrasound (3DUS) planning in breast HDR brachytherapy. Methods: The end‐to‐end procedure was realized with a computer controlled robotic 3DUS system for breast brachytherapy so that both planning and guidance are possible. The scanner is mounted on the Kuske template (Nucletron, Veneendal, The Netherlands) so that the scan direction is longitudinal to the catheter axis. Software modules were developed for the acquisition and reconstruction of ultrasound images. A semi‐automatic segmentation algorithm and a needle reconstruction algorithm were integrated into the software to segment the planning target volume and reconstruct the catheter. A Centroidal Voronoi Tessellations (CVT) algorithm, in conjunction with IPSA, was used for catheter number and position optimization. The procedure was tested with 9 catheters in an agarose‐based phantom with a hypo‐echoic mass. Results: The 3DUS acquisition time is less than 7s and the catheter optimization algorithm can obtain 10 complete treatments plans, with the corresponding dosimetric indices, in 90s. The catheter optimization algorithm was shown to reduce the number of catheters to 12 without reducing the quality of the treatment. Breast PTV V100 and dose homogeneity index (DHI) calculated after the procedure were compared to the value obtained from the optimal plan calculated with the CVT algorithm. Both dosimetric indexes were still within RTOG guidelines after the procedure. However, the PTV V100 is smaller after the procedure due to the template‐based implantation. Isodoses are comparable. Conclusion: We have devised a simple, fast and efficient method for real‐time 3DUS guidance of breast HDR brachytherapy. This approach could be coupled with personalized 3D‐printed or robotic template to further optimize this brachytherapy procedure. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".