The prognostic importance of the pan-immune-inflammation value (PIV) in lung cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Preliminary studies suggest that pan-immune-inflammation value (PIV) has the potential to serve as a prognostic tool for lung cancer. However, existing studies are limited by inconsistent findings regarding the impact of high PIV on patient outcomes. To provide a more comprehensive assessment, we conducted a meta-analysis to clarify the prognostic value of PIV in lung cancer. Methods: Two researchers independently searched the PubMed, Cochrane, Embase, and Web of Science databases for studies evaluating the associations between PIV and prognoses in lung cancer patients (up to July 15, 2025). Studies were included if they reported high versus low PIV and provided hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS), progression-free survival (PFS), etc. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Pooled HRs and 95% CIs were calculated to determine the associations between PIV and patient prognosis. Results: A total of ten studies comprising 1,969 patients were included. Meta-analyses demonstrated that high PIV was significantly associated with OS (HR =2.86, 95% CI: 2.23-3.65, P<0.001) and PFS (HR =2.06, 95% CI: 1.65-2.59, P<0.001) in lung cancer patients. In non-small cell lung cancer (NSCLC) patients, high PIV was significantly associated with worse OS (HR =2.76, 95% CI: 2.14-3.56, P<0.001) and PFS (HR =1.94, 95% CI: 1.55-2.42, P<0.001). In small cell lung cancer (SCLC) patients, even stronger associations were observed for OS (HR =3.47, 95% CI: 2.21-5.44, P<0.001) and PFS (HR =2.33, 95% CI: 1.63-3.33, P<0.001). Subgroup analyses further confirmed that PIV served as a critical prognostic marker for both OS and PFS. All studies were of high quality according to the NOS. Conclusions: PIV can serve as an independent prognostic biomarker for survival outcomes in lung cancer patients. Therefore, incorporating PIV into prognostic assessments may provide additional support for individualized treatment decision-making.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".