Breast cancer histology and non-sentinel lymph node involvement following a positive sentinel lymph node biopsy: A multi-institutional cohort study.
Bibliographic record
Abstract
1043 Background: Management of the axilla in early breast cancer has shifted towards a more conservative surgical approach. The omission of a completion axillary lymph node dissection (cALND) in the context of a positive sentinel lymph node (SLN) has become common, even though this practice may result in residual non-sentinel positive nodes left behind. Furthermore, the axillary management of both invasive ductal and invasive lobular carcinomas (IDCs and ILCs) has traditionally been the same despite the different pattern of invasion and metastases in ILCs. The objective of this study was to determine if lobular histology is an independent predictor of non-sentinel lymph node (NSLN) involvement following a positive SLN biopsy (SLNB). Methods: A multi-institutional cohort study was completed. Patients with node positive IDC or ILC who were treated with both a SLNB followed by cALND from November 1997 to June 2009 were included. The primary outcome was NSLN involvement, defined as having at least one positive lymph node within the cALND specimen. Univariate analysis was performed to determine baseline differences between the IDC and ILC subgroups. A multivariable logistic regression analysis was performed to determine the independent effect of lobular histology on NSLN involvement. Results: A total of 261 cALNDs from 259 patients were included. The primary histology was ductal for 200 (77%) of the tumors. Overall, 35.6% of all cALNDs had NSLNs involved. The presence of lymphovascular invasion (LVI) within the primary tumor (OR 3.28, p = 0.0009) and the absolute number of SLNs involved (OR 2.54, p < 0.0001) were both found to be independent predictors of NSLN involvement. Lobular histology was not an independent predictor (OR 1.42, p = 0.42). Conclusions: Within our cohort, lobular histology is not an independent predictor of residual disease in NSLNs. Predictors of nodal involvement included presence of LVI and the absolute number of positive SLNs. Overall, the clinical practice changes to axillary management following a positive SLN biopsy appear to be generalizable to both ductal and lobular breast cancers.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".