Surgical Management of the Axilla in Invasive Lobular Carcinoma in the Z1071 Era: A Propensity-Score Matched Analysis of the National Cancer Database
Bibliographic record
Abstract
In patients with invasive lobular carcinoma (ILC) and clinically positive nodes (cN1) who demonstrate an axillary clinical response to neoadjuvant-chemotherapy (NAC), the outcomes of sentinel lymph node biopsy (SLNB) compared to axillary lymph node dissection (ALND) are not well studied. We sought to evaluate axillary surgery practice patterns and the resultant impact on overall survival (OS) in cN1 ILC. The National Cancer Database (NCDB) was queried (2012–2017) for women with cN1 ILC who were treated with NAC followed by surgery. Propensity-score matching was performed between SLNB and ALND cohorts. Kaplan–Meier and Cox regression analyses were performed to identify predictors of OS. Of 1390 patients, 1192 were luminal A ILCs (85.8%). 143 patients (10.3%) had a complete axillary clinical response, while 1247 (89.7%) had a partial clinical response in the axilla. Definitive axillary surgery was SLNB in 211 patients (15.2%). Utilization of SLNB for definitive axillary management increased from 8% to 16% during the study period. Among 201 propensity-score matched patients stratified by SLNB vs. ALND, mean OS did not significantly differ (81.6 ± 1.8 vs. 81.4 ± 2.0 months; p = 0.56). Cox regression analysis of the entire cohort demonstrated that increasing age, grade, HER2+ and triple-negative tumors, and partial clinical response were unfavorable OS predictors (p < 0.02 each). The definitive axillary operation and administration of adjuvant axillary radiation did not influence OS. In cN1 ILC patients with a clinical response to NAC in the axilla, SLNB vs. ALND did not affect OS. Further axillary therapy may be warranted with ypN+ disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".