Neutrophil-to-Lymphocyte Ratio Predicts Overall Survival of Advanced Non-Small Cell Lung Cancer Harboring Mutant Epidermal Growth Factor Receptor
Bibliographic record
Abstract
BACKGROUND: Neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) have been demonstrated to be prognostic biomarkers in various cancers, including non-small cell lung cancer (NSCLC). However, little has been known about these two ratios for a specific population of NSCLC harboring active epidermal growth factor receptor (EGFR) mutation. METHODS: We retrospectively reviewed electrical medical records of 152 patients who met the following criteria: NSCLC harboring mutant EGFR, EGFR-tyrosine kinase inhibitor (EGFR-TKI) monotherapy initiated between October 2007 and February 2017 at our hospital, stage III-IV or post-surgical recurrence. We compared overall survival (OS) and progression-free survival (PFS) between dichotomized groups by the optimal cut-off points of the two biomarkers. Univariate and multivariate Cox hazard analyses also searched for prognostic factors of survival time. RESULTS: OSs of NLR < 2.11 (median 38.6 vs. 24.1 months, P < 0.01) and LMR ≥ 5.09 (median 39.4 vs. 26.4 months, P < 0.01) were significantly longer than those of NLR ≥ 2.11 and LMR < 5.09. Multivariate analyses found lower NLR (hazard ratio (HR) 1.07, 95% CI: 1.01 - 1.14; P = 0.03) as an independent prognostic factor for longer OS, in addition to Eastern Cooperative Oncology Group performance status 0 - 1, first-line EGFR-TKI, higher serum sodium concentration and lower lactate dehydrogenase. However, LMR was not detected as a significant prognostic factor for OS. None of these two biomarkers was selected as an independent prognostic factor for PFS. CONCLUSIONS: This study demonstrated that elevated NLR is an independent prognostic factor for poor survival of patients with EGFR mutant NSCLC. NLR is a useful and simple biomarker for these patients.
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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 | 0.002 |
| 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 | 0.000 |
| 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".