SU‐FF‐T‐34: Inversely Planned Catheter Positions for High Dose Rate Brachytherapy of the Prostate
Bibliographic record
Abstract
Purpose: To determine the dosimetric impact of the number of catheters used in prostate HDR brachytherapy and to evaluate the possibility of reducing the number currently used clinically. Method and Materials: Our in‐house inverse planning optimizes the catheter positions based on clinical objectives such as prostate dose coverage, dose homogeneity and urethra protection. This optimization displaces straight catheters on a 5 mm grid searching for the best pattern. The number of catheters is fixed, only their positions and the dwell times are optimized. For one prostate, 8 implants were generated with different number of catheters (5, 6, 8, 10, 12, 14, 16 and 18). Results: Implants with more than 10 catheters shows equivalent dosimetric indices with good dose coverage (V100>95%) and low dose delivered to the urethra (V120<5%). With less than 10 catheters the implant dosimetry deteriorates rapidly. The protection of the urethra from high dose (V150=0) is independent of the number of catheters. This demonstrates that decent dose coverage can be achieved with a reduced number of catheters without affecting the urethra protection. The optimization tends to produce implants with peripherally loaded catheters and manages to produce adequate dose coverage with just a few catheters in the middle part of the prostate close to the urethra. Conclusion: This study encourages the diminution of the number of catheters implanted around the urethra, improving the urethra protection and facilitating the implant procedure. This is only true when using an inverse planning to optimize the dwell times. Centers that are performing the treatment planning without an inverse planning need these central catheters to produce adequate dose coverage. This demonstrates that inverse planning brings the possibility to safely reduce the number of catheters currently used in prostate HDR brachytherapy. This research was supported by NCI Canada with funds from the CCS.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".