Complete Blood Count-Based Biomarkers as Predictors of Clinical Outcomes in Advanced Non-Small Cell Lung Cancer Patients with PD-L1 < 50% Treated with First-Line Chemoimmunotherapy
Bibliographic record
Abstract
Background: The aim of the study was to investigate a series of complete blood cell count-based biomarkers of systemic inflammation as predictors of clinical outcomes in patients who underwent first-line chemoimmunotherapy for advanced NSCLC. Methods: Consecutive patients with pathologically diagnosed stage III/IV NSCLC and PD-L1 < 50% who underwent first-line chemoimmunotherapy were retrospectively enrolled. The clinical outcomes used for biomarker evaluation were Objective Response Rate (ORR) and Overall Survival (OS). Results: Non-responders had significantly higher values of neutrophil to lymphocyte ratio (NLR, median: 5.36; IQR: 2.78–10.82 vs. 3.31; IQR: 2.15–4.12, p = 0.019), neutrophil to monocyte ratio (NMR, median: 14.00; IQR: 8.82–21.20 vs. 9.20; IQR: 7.45–11.20, p = 0.013), and systemic inflammation index (SII, median: 1395; IQR: 929–3334 vs. 945; IQR: 552–1373, p = 0.025), but only NLR and NMR remained independently associated with clinical response in multivariate logistic regression. In the univariate analysis, white blood cells (OR:1.2202; 95% CI: 1.0339–1.4400, p = 0.019), neutrophils (OR:1.2916; 95% CI: 1.0692–1.5604, p = 0.008), NLR (OR:1.3601: 95% CI: 1.0949–1.6896, p = 0.005) and NMR (OR:1.2159; 95% CI: 1.00396–1.4221, p = 0.015) were significantly associated with survival; Cox regression models confirmed that neutrophils, NLR, and MLR were independently associated with survival; NLR, at a cut-off value of 4.0, showed the better AUC (0.749) in predicting OS. Conclusions: Baseline complete blood cell count biomarkers, especially the NLR, can predict clinical outcomes in patients with advanced NSCLC treated with first-line chemoimmunotherapy.
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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".