The Prognostic Significance of Baseline Neutrophil-to-Lymphocyte Ratio in Melanoma Patients Receiving Immunotherapy
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Bibliographic record
Abstract
Immunotherapy has revolutionized the treatment in metastatic melanoma, but alternative biomarkers that are economical, simple and reliable still need to be clarified. In this study, we aimed to comprehensively analyze the prognostic significance of baseline neutrophil-to-lymphocyte ratio (NLR) in melanoma patients with immunotherapy. We searched PubMed, Embase, and Cochrane Library until September 16, 2020. Hazard ratio (HR) and 95% confidence intervals (CIs) were pooled to investigate the association of baseline NLR with overall survival (OS) and progression-free survival (PFS). Sensitivity analysis, subgroup analyses, publication bias assessment, and the Duval and Tweedie trim-and-fill method were used to evaluate the stability of results. A total of 18 studies including 2054 patients were included in our analysis. Pooled data demonstrated that higher baseline NLR was associated with a poorer OS (HR=2.46, 95% CI=1.77, 3.43) and PFS (HR=2.38, 95% CI=1.95, 2.89) of melanoma patients receiving immunotherapy. Subgroup analysis according to immunotherapy type showed that the prognostic effects of baseline NLR existed in all the subtypes of immunotherapy, including anticytotoxic T lymphocyte-associated protein 4 therapy (OS HR=2.26, 95% CI=1.43, 3.59; PFS HR=2.68, 95% CI=1.79, 4.02), antiprogrammed cell death-1 therapy (OS HR=3.08, 95% CI=2.21, 4.27; PFS HR=2.01, 95% CI=1.64, 2.47), and combination therapy (OS HR=1.75, 95% CI=1.13, 2.72; PFS HR=3.13, 95% CI=1.63, 6.03). Conclusions were still consistent in subgroup analyses stratified by study year, region, study type, sample size, analysis of HR and cuttoff of baseline NLR. Altogether, baseline NLR is a promising prognostic biomarker for melanoma patients receiving immunotherapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it