Predictive Value of the Lung Immune Prognostic Index for Immune Checkpoint Inhibitor Therapy Outcomes in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Non-Small Cell Lung Cancer (NSCLC) patients undergoing Immune Checkpoint Inhibitors (ICIs) therapy exhibit diverse clinical outcomes. The Lung Immune Prognostic Index (LIPI) may emerge as a potential prognostic marker. This study systematically reviews and meta-analyzes the prognostic value of LIPI in predicting the clinical efficacy of ICIs therapy for NSCLC patients. A thorough literature review was performed using the Cochrane Library, Web of Science, PubMed, and Embase, following PRISMA guidelines. Studies assessing LIPI's predictive value in NSCLC patients treated with ICIs were included. Effect sizes were aggregated utilizing a fixed-effects model. The studies featured in the review were appraised using the Newcastle-Ottawa Scale for quality assessment. Eight studies were incorporated into the meta-analysis, encompassing various treatment lines and ICIs. No substantial heterogeneity was detected across the studies. The meta-analysis revealed that the low-risk group exhibited significantly extended overall survival (OS) (HR=3.18, 95%CI: 2.78~3.59 and progression-free survival (PFS) (HR=1.60, 95%CI: 1.4~61.74, underscoring the predictive significance of LIPI for NSCLC patients treated with ICI therapy. No significant publication bias was detected. LIPI demonstrates potential as a prognostic marker for NSCLC patients receiving ICI therapy, contributing to the development of therapeutic strategies. Further prospective researches are required to investigate its relationship with factors such as tumor mutational burden, PD-L1 and PD-1.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.047 |
| Bibliometrics | 0.009 | 0.010 |
| 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) | 0.003 | 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".