Sentinel Node Biopsy After Neoadjuvant Chemotherapy in Biopsy-Proven Node-Positive Breast Cancer: The SN FNAC Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: An increasing proportion of patients (> 30%) with node-positive breast cancer will obtain an axillary pathologic complete response after neoadjuvant chemotherapy (NAC). If sentinel node (SN) biopsy (SNB) is accurate in this setting, completion node dissection (CND) morbidity could be avoided. PATIENTS AND METHODS: In the prospective multicentric SN FNAC study, patients with biopsy-proven node-positive breast cancer (T0-3, N1-2) underwent both SNB and CND. Immunohistochemistry (IHC) use was mandatory, and SN metastases of any size, including isolated tumor cells (ypN0[i+], ≤ 0.2 mm), were considered positive. The optimal SNB identification rate (IR) ≥ 90% and false-negative rate (FNR) ≤ 10% were predetermined. RESULTS: From March 2009 to December 2012, 153 patients were accrued to the study. The SNB IR was 87.6% (127 of 145; 95% CI, 82.2% to 93.0%), and the FNR was 8.4% (seven of 83; 95% CI, 2.4% to 14.4%). If SN ypN0(i+)s had been considered negative, the FNR would have increased to 13.3% (11 of 83; 95% CI, 6.0% to 20.6%). There was no correlation between size of SN metastases and rate of positive non-SNs. Using this method, 30.3% of patients could potentially avoid CND. CONCLUSION: In biopsy-proven node-positive breast cancer after NAC, a low SNB FNR (8.4%) can be achieved with mandatory use of IHC. SN metastases of any size should be considered positive. The SNB IR was 87.6%, and in the presence of a technical failure, axillary node dissection should be performed. We recommend that SN evaluation with IHC be further evaluated before being included in future guidelines on the use of SNB after NAC in this setting.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it