Sci—Sat AM: Brachy — 09: Permanent seed re‐implantation using image guidance for composite dose planning
Bibliographic record
Abstract
Treatment outcome has been associated with dosimetric implant quality assessed in a postplan. Uncertainties during the implant and in the period before postplan evaluation lead to dosimetric deviations from the original treatment plan. The observed range of postplan results can include patients with dosimetry below recommended levels. Further treatment options may be considered to boost the delivered dose and a second seed implant is one way to accomplish this. This work describes the development of a procedure to plan and deliver a second seed implant and an evaluation of the dosimetric improvement. A patient with a post-plan D90 of 124 Gy was offered a second seed implant 21 weeks after the initial implant. A total dose of 163 Gy was prescribed due to radiobiological considerations for the time between implants. A volume study was performed 2 weeks before the implant and variable angle images of the seeds from the first implant were obtained to reconstruct the original seed coordinates. The second implant was planned considering the total composite physical dose distribution by forward planning additional seeds onto the original implant coordinates. The additional seeds were manually added to a new plan on the volume study images at the planned coordinates and delivered as a typical preplanned implant. Post-plan evaluation four weeks after the second implant showed a D90 of 177 Gy and a V100 of 92.7%. These results demonstrate the effective use of a second seed implant with image guided composite planning to improve dosimetric implant quality.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".