Analysis of Seed Loss and Pulmonary Seed Migration in Patients Treated With Virtual Needle Guidance and Robotic Seed Delivery
Bibliographic record
Abstract
PURPOSE AND BACKGROUND: To determine whether automated seed delivery system and real-time intraoperative (IO) virtual needle guidance reduce seed loss and pulmonary seed migration. PATIENTS AND METHODS: We analyzed 279 patients with low and intermediate risk prostate cancer treated in our institution with radioactive iodine (I-125) permanent seed implants. Loose seeds were exclusively used. To account for lost seeds, pelvic fluoroscopic imaging from 3 different angles was done 30 days after the implant. Posteroanterior and lateral chest x-rays were done when seed loss was confirmed. Patients were compared using the χ(2) test and Fisher exact test. RESULTS: At least 1 seed was lost in 31.5% of patients with a migration rate of 1.02%; 9.3% of patients had at least 1 seed in the lung with a migration rate of 0.22%. The population was divided into 3 groups according to the order in which they were treated. Seed loss (P=0.02) and pulmonary seed embolization (P=0.008) were significantly lower in the second hundred than in the first hundred patients. No difference was noted between groups 1 and 3 (patient, 201-279). Peri- or extracapsular seed placement was not correlated to seed loss (P=0.780 and P=0.092, respectively). No serious complications from seed migration were reported. Seed loss did not influence dosimetry parameters (V100, V150, and D90). CONCLUSION: Our pulmonary seed migration and total seed loss rates are comparable to the ones reported in the literature. Virtual needle guidance and automated seed delivery system are in our hand as accurate as the manual technique.
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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 | 0.003 |
| 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".