The Usefulness of the Pretreatment Neutrophil/Lymphocyte Ratio as a Predictor of the 5-Year Survival in Stage 1–3 Triple Negative Breast Cancer Patients
Bibliographic record
Abstract
Background: We have previously shown that the neutrophil/lymphocyte ratio (NLR) is a predictor of survival among breast cancer patients. The aim of this study was to determine the predictive value of NLR among different nodal and chemotherapy subgroups of triple negative breast cancer (TNBC). Methods: Patients with stage 1–3 TNBC who underwent treatment from 2007 to 2014 and had blood counts prior to treatments were included. Patients were categorized into high (≥2) and low (<2) NLR groups. Primary outcomes were overall survival (OS) and disease-free survival (DFS). Results: The average follow-up time was 54 months. The high NLR group had worse OS (HR 2.8, CI 1.3–5.9, p < 0.001) and DFS (HR 2.3, CI 1.2–4.2, p < 0.001) than the low NLR group. After adjusting for confounding variables, high NLR was an independent prognostic factor for both OS (HR 5.5, CI 2.2–13.7, p < 0.0001) and DFS (HR 5.2, CI 2.3–11.6, p < 0.0001). Categorization of TNBC patients by NLR (high vs. low) and nodal status (positive vs. negative) resulted in four groups with significantly different OS and DFS (log rank p < 0.0001). Significant improvements in OS (p < 0.001) and DFS (p < 0.001) were observed for patients who received chemotherapy and had high NLR but not for patients with low NLR (p = 0.65 and p = 0.07, respectively). Conclusion: High pretreatment NLR is an independent predictor of poor OS and DFS among TNBC patients. Combining NLR and pN provides better risk stratification for TNBC patients. Chemotherapy appears to be beneficial only in patients with high NLR. Larger prospective studies are needed to validate these findings.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".