The lung immune prognostic index as a predictive biomarker in urological malignancies undergoing immune checkpoint inhibitor therapy
Bibliographic record
Abstract
Objective We conducted a meta-analysis to evaluate the prognostic utility of the Lung Immune Prognostic Index (LIPI) in patients with urological malignancies treated with immune checkpoint inhibitors (ICIs).Methods We systematically searched PubMed, the Cochrane Library, and EMBASE up to March 3, 2025. Clinical outcomes included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR). Study quality was assessed using the Newcastle-Ottawa Scale (NOS), with a threshold score ≥6 defining high-quality studies.Results This meta-analysis incorporated seven studies comprising 2498 patients. Our findings demonstrated that patients with good LIPI index exhibited significantly prolonged OS (good vs. intermediate: HR = 0.51, 95% CI 0.42–0.62, p < 0.001; good vs. poor: HR = 0.15, 95% CI 0.11–0.20, p < 0.001; good vs. Intermediate and poor: HR = 0.48, 95% CI 0.38–0.61, p < 0.001). The data further showed that patients with good LIPI index exhibited significantly prolonged PFS; (good vs. intermediate: HR = 0.66, 95% CI 0.57–0.76, p < 0.001; good vs. poor: HR = 0.23, 95% CI 0.14–0.37, p < 0.001; good vs. Intermediate and poor: HR = 0.73, 95% CI 0.66–0.81, p < 0.001). Additionally, we found that the good LIPI index correlated with higher ORR (good vs. intermediate: OR = 1.50, 95% CI: 0.98–2.28, p = 0.061; good vs. poor: OR = 1.96, 95% CI: 1.01–3.83, p = 0.047). The good LIPI index correlated with higher DCR (good vs. intermediate: OR = 2.15, 95% CI: 1.53–3.01, p < 0.001; good vs. poor: OR = 5.08, 95% CI: 2.85–9.05, p < 0.001). No publication bias existed, and sensitivity analysis confirmed stable results.Conclusion The LIPI emerges as a valuable prognostic biomarker in patients with urological malignancies treated with ICIs therapy.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".