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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".