Neutrophil–Lymphocyte Ratio Predicts Response to Chemotherapy in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Background: The neutrophil–lymphocyte ratio (NLR) has been reported to correlate with patient outcome in several cancers, including breast cancer. We evaluated whether the NLR can be a predictive factor for pathologic complete response (PCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC). Methods: We analyzed the correlation between response to NAC and various factors, including the NLR, in 87 patients with TNBC who underwent NAC. In addition, we analyzed the association between the NLR and recurrence-free survival (RFS) in patients with TNBC. Results: Of the 87 patients, 25 (28.7%) achieved a PCR. A high Ki-67 index and a low NLR were significantly associated with PCR. The PCR rate was higher in patients having a high Ki-67 index (≥15%) than in those having a low Ki-67 index (35.7% vs. 0%, p = 0.002) and higher in patients having a low NLR (≤1.7) than in those having a high NLR (42.1% vs. 18.4%, p = 0.018). In multiple logistic analysis, a low NLR remained the only predictive factor for PCR (odds ratio: 4.274; p = 0.008). In the survival analysis, the RFS was significantly higher in the low NLR group than in the high NLR group (5-year RFS rate: 83.7% vs. 66.9%; log-rank p = 0.016). Conclusions: Our findings that the NLR is a predictor of PCR to NAC and also a prognosticator of recurrence suggest an association between response to chemotherapy and inflammation in patients with TNBC. The pretreatment NLR can be a useful predictive and prognostic marker in patients with TNBC scheduled for NAC.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".