Impact of Pre-Treatment NLR and Other Hematologic Biomarkers on the Outcomes of Early-Stage Non-Small-Cell Lung Cancer Treated with Stereotactic Body Radiation Therapy
Bibliographic record
Abstract
Introduction: We evaluated the association of pre-treatment immunologic biomarkers on the outcomes of early-stage non-small-cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). Materials and methods: In this retrospective study, all newly diagnosed early-stage NSCLC treated with SBRT between January 2010 and December 2017 were screened and included for further analysis. The pre-treatment neutrophil-lymphocyte ratio (NLR), monocyte lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) were calculated. Overall survival (OS) and recurrence-free survival (RFS) were estimated by Kaplan–Meier. Multivariable models were constructed to determine the impact of different biomarkers and the Akaike information criterion (AIC), index of adequacy, and scaled Brier scores were calculated. Results: A total of 72 patients were identified and 61 were included in final analysis. The median neutrophil count at baseline was 5.4 × 109/L (IQR: 4.17–7.05 × 109/L). Median lymphocyte count was 1.63 × 109/L (IQR: 1.29–2.10 × 109/L), median monocyte count was 0.65 × 109/L (IQR: 0.54–0.83 × 109/L), median platelet count was 260.0 × 109/L (IQR: 211.0–302.0 × 109/L). The median NLR was 3.42 (IQR: 2.38–5.04), median MLR was 0.39 (IQR: 0.31–0.53), and median PLR was 156.4 (IQR: 117.2–197.5). On multivariable regression a higher NLR was associated with worse OS (p = 0.01; HR-1.26; 95% CI 1.04–1.53). The delta AIC between the two multivariable models was 3.4, suggesting a moderate impact of NLR on OS. On multivariable analysis, higher NLR was associated with poor RFS (p = 0.001; NLR^1 HR 0.36; 0.17–0.78; NLR^2 HR-1.16; 95% CI 1.06–1.26) with a nonlinear relationship. The delta AIC between the two multivariable models was 16.2, suggesting a strong impact of NLR on RFS. In our cohort, MLR and PLR were not associated with RFS or OS in multivariable models. Conclusions: Our study suggests NLR, as a biomarker of systemic inflammation, is an independent prognostic factor for OS and RFS. The nonlinear relationship with RFS may indicate a suitable immunological environment is needed for optimal SBRT action and tumoricidal mechanisms. These findings require further validation in independent cohorts.
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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 | 0.001 | 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 | 0.001 | 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".