Predictive Factors and Clinical Impact of Radioactive Seed Migration After Prostate Brachytherapy: A Retrospective Study
Bibliographic record
Abstract
Radioactive seed migration after low-dose-rate brachytherapy (LDR-BT) for prostate cancer is a known phenomenon; however, its clinical impact remains unclear. We retrospectively analyzed 611 patients treated with LDR-BT using loose iodine-125 seeds. Post-treatment imaging was used to assess seed migration. Treatment efficacy was evaluated using post-plan dosimetry (V100 and D90) and biochemical recurrence-free survival (bRFS). Seed migration was observed in 150 patients (24.5%) within 1-3 months post-treatment, involving a total of 210 seeds. Migration sites included lungs, vasculature, and seminal vesicles. Hematogenous migration was significantly associated with higher seed counts. Seminal vesicle migration was linked to increased needle usage and absence of neoadjuvant hormone therapy. No significant differences were observed in V100, D90, or bRFS between patients with or without seed migration. However, migration of ≥3 seeds correlated with significantly lower V100 and with a trend toward decreased bRFS. Limited seed migration appears to have minimal clinical impact. However, ≥3 migrated seeds may reduce dosimetric quality and affect treatment efficacy. Risk factors include larger prostate volume as well as higher seed and needle counts. Improved planning and using linked seeds may reduce migration and improve outcomes in LDR-BT for prostate cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".