SU-E-T-592: A Novel 3D CT-Based Treatment Planning for Palladium 103 Breast Seed Implant Brachytherapy
Bibliographic record
Abstract
Purpose: To present a novel 3D CT-based treatment planning method for permanent breast seed implant (PBSI) with Palladium-103. Methods: On a pilot study for adjuvant partial breast irradiation between 2003 and 2005 at the BC Cancer Agency, Vancouver Island, Canada, we established a unique method for planning CT-based multi-catheter HDR interstitial brachytherapy using Varian BrachyVision. Based on our experience from that study we transferred the concept to LDR Palladium-103 PBSI. Patients are CT scanned with two radio-opaque grids placed on the medial and lateral aspect of the breast and other reference skin markers are added. The grids assist in determining the entrance and exit point of a reference needle. The implant is then guided by landmark measurements and the reference needle. Results: We found that the BrachyVision system, developed by Varian for planning of LDR seed implants, was unsuitable for needle-based preplanning. When we used the afterloader planning tools, we were able to adapt its parameters to represent manual needle loadings. The total dose was converted to dose rate because of the lack of flexibility for permanent implant prescription. The BrachyVision afterloader interstitial planning tools were suitable for producing an optimal plan for PBSI with Palladium- 103 in a relatively short time. Images in the oblique plane were generated in BrachyVision and exported to VariSeed planning system for validation. An image manipulation software was used to overlay images for BrachyVision and VariSeed for comparison. The dose distribution variance between the two systems was found to be within 3%. Conclusions: The BrachyVison afterloader planning concept can be used for PBSI planning. An optimal plan can be obtained in a relatively short time with minimal planner intervention. The skin grid guidance may achieve high accuracy of seed placements without the aid of a real time ultrasound.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".