Clinical utility of radioactive seed localization in nonpalpable breast cancer: A retrospective single institutional cohort study
Bibliographic record
Abstract
BACKGROUND: With advances in mammographic screening techniques, it has become easier to detect nonpalpable breast lesions at an early stage. Pre-surgical localization of lesions by radioactive seed localization (RSL) has several benefits over conventional wire localization (WL) in guiding breast conserving surgery. In this study, we compared WL and RSL, focusing on the relationship between the techniques and in-breast recurrence or margin positivity. METHODS: This study included 1083 patients with nonpalpable breast lesions who underwent breast conserving surgery between 2010 and 2015. The patients were classified into WL and RSL groups. RESULTS: Margin positivity and in-breast recurrence rates did not differ significantly between the WL and RSL groups (P = 0.368 and P = 0.167, respectively). Multivariate analysis showed that tumor grade (OR: 5.016; 95% CI: 1.53-23.059) was significantly associated with margin positivity in patients undergoing RSL. Tumor size was significantly associated with in-breast recurrence in both the WL group (OR: 2.299; 95% CI: 1.561-3.411) and RSL group (OR: 2.998; 95% CI: 1.128-8.043). CONCLUSION: As the method of tumor localization did not influence margin positivity or in-breast recurrence, either WL or RSL appear to be appropriate for breast conserving surgery. Given the advantages of RSL, including the ability to perform this technique days to weeks before surgery, we propose that high-volume breast centers consider adopting this localization method.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".