Sentinel Node Biopsy Alone or With Axillary Dissection in Breast Cancer Patients After Primary Chemotherapy: Long-Term Results of a Prospective Interventional Study
Bibliographic record
Abstract
OBJECTIVE: To ascertain, in cN0/1 breast cancer patients given primary chemotherapy followed by sentinel node biopsy (SNB), whether SNB alone is adequate axillary treatment if the sentinel nodes (SNs) are clear (pN0). SUMMARY BACKGROUND DATA: 2020 guidelines do not recommend SNB in most cN1 patients with clear SNs after primary chemotherapy because the high SNB false negative rate might lead to poorer outcomes. METHODS: We prospectively assigned SNB after primary chemotherapy to 353 consecutive cT2 cN0/1 patients, median age 47 years (range 22-76) treated from 2007 to 2015. If the SNs were pN0, patients generally received no further axillary treatment (SNB only); if the SNs were pN1, completion axillary dissection (AD) (SNB + AD) was usually performed. Primary outcomes were overall (OS) and disease-free (DFS) survival in SNB only versus SNB + AD patients, assessed by Kaplan-Meier and compared using log-rank test, with use of propensity scores to account for bias due to nonrandom assignment to SNB versus SNB + AD. RESULTS: Median follow-up was 108 months, interquartile range 66 to 136. OS and DFS did not differ significantly between the groups by propensity score- weighted comparison: 10-year OS 89% [95% confidence interval (CI): 81%- 99%] in SNB only patients versus 86% (95%CI: 78%-95%) in SNB + AD patients; 10-year DFS 79% (95%CI: 68%-92%) versus 69% (95%CI: 58%-81%). No SNB-only patient developed axillary failure. CONCLUSIONS: cT2 cN0/1 patients whose SNs are disease-free (pN0) after primary chemotherapy can be offered SNB (with no further axillary treatment if the SNs are negative), irrespective of axillary status beforehand, without affecting OS or DFS.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".