Prognostic Value of Pretreatment Neutrophil-to-Lymphocyte Ratio in HER2-Positive Metastatic Breast Cancer
Bibliographic record
Abstract
This study aimed to examine the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) and other clinicopathological features in HER2+ MBC patients who received first-line anti-HER2 therapy. A total of 129 patients were assigned to NLR-low and NLR-high groups based on a cutoff value of 3.0 at baseline. Peripheral blood lymphocyte subsets and gene mutations in circulating tumor DNA were analyzed by flow cytometry and Next-generation sequencing, respectively. Survival was evaluated by the Kaplan−Meier method and Cox regression analysis. Of the 129 patients, 77 and 52 were assigned to the NLR-low (≤3) and NLR-high (>3) groups, respectively. Compared with NLR-high patients, the NLR-low patients had significantly longer median progression-free survival (PFS) (11.7 vs. 7.7 months) (p = 0.001, HR = 2.703 95% CI 1.543−4.736 and overall survival (OS) (37.4 vs. 28.7 months) (p = 0.044, HR = 2.254 95% CI 1.024−4.924). Furthermore, this association was independent of metastatic sites or estrogen receptor status. Peripheral blood CD3+ (p = 0.034) and CD4+ (p = 0.010) T cell numbers were significantly higher in the NLR-low group than the NLR-high group. The mutational profile of MBC was generally similar between the two groups. Baseline NLR was a prognostic factor of PFS and OS for patients with HER2+ MBC in the first-line setting. These results may facilitate the selection of patients who will benefit most from anti-HER2 treatment.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".