Prognostic role of platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) in advanced gastric cancer treated with immunotherapy: A systematic review and meta-analysis.
Bibliographic record
Abstract
397 Background: Recent advances in the treatment of Gastric Cancer (GC) have introduced immunotherapy as a promising approach for advanced disease. In this context, potential biomarkers such as the platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) have gained attention for their roles in predicting treatment outcomes. In this meta-analysis, we explore the clinical significance of PLR and NLR as predictors of Overall Survival (OS) and Progression-Free Survival (PFS) in advanced GC patients undergoing immunotherapy. Methods: Two independent reviewers systematically searched electronic databases, including PubMed, Embase and Scopus. The search incorporated controlled vocabulary (MeSH terms) and keywords related to Gastric Cancer, immunotherapy, platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR). Eligible studies were selected based on predefined criteria, including HR availability, and were assessed for quality and bias using the Newcastle-Ottawa Scale. Data extraction was independently performed by two reviewers, and discrepancies in study selection and data extraction were resolved by a third reviewer. Subgroup analyses were performed to explore potential variations in the study outcomes. All analyses were conducted with RevMan 5.4 and employed random-effects model. Results: In total, 16 studies encompassing 1176 patients for NLR and 8 studies covering 766 patients for PLR were included in this systematic review and meta-analysis. The studies spanned the years between 2018 and 2023. Our analysis revealed significant associations between high NLR and poor OS (HR: 2.11, 95% CI: 1.70-2.62, p < 0.00001) and PFS (HR: 1.76, 95% CI: 1.43-2.17, p < 0.00001). Similarly, elevated PLR was significantly associated with worse OS (HR: 1.77, 95% CI: 1.44-2.17, p < 0.00001) and PFS (HR: 1.61, 95% CI: 1.33-1.96, p < 0.00001). In all conducted subgroup analyses, high NLR and PLR were consistently associated with poor survival outcomes. Conclusions: Our analysis supports the significance of NLR and PLR as accessible and cost-effective biomarkers for predicting survival outcomes in the context of immunotherapy for advanced GC. These findings underscore their potential significance in guiding clinical decisions. Further investigation should be carried out to validate our findings.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".