Efficacy of immune checkpoint inhibitors in non-small cell lung cancer patients with different metastatic sites: A systematic review and meta-analysis.
Bibliographic record
Abstract
e21684 Background: Previous studies have demonstrated that bone, brain and liver metastases are poor prognosis factors of immune checkpoint inhibitors (ICIs) therapy in patients with non-small-cell lung cancer (NSCLC). This study aims to compare the efficacy of ICIs with conventional therapy in NSCLC patients with bone, brain or liver metastases. Methods: MEDLINE, Embase and CENTRAL were searched for prospective studies comparing ICIs with conventional therapy in NSCLC patients with bone, brain or liver metastases. Quality assessment was performed using the Newcastle-Ottawa Scale. The pooled hazard ratio (HR) of overall survival (OS) and progression-free survival (PFS) among included studies was analyzed using the random-effects model. Results: From 1,195 relevant articles, eight studies with high methodological quality consisting of 988 patients were included in the analysis. ICIs significantly improved OS for patients with brain metastases (HR = 0.57; 95%CI: 0.37-0.86; P = 0.007). Among patients with liver metastases, OS (HR = 0.72; 95%CI: 0.57-0.91; P = 0.006) and PFS (HR = 0.72; 95%CI: 0.49-0.87; P = 0.004) improvement was observed in the ICI treatment arm. No available study with bone metastases information was identified. Subgroup analysis revealed that PD-1 inhibitors could benefit patients on OS and PFS regardless of metastatic sites. Sensitivity analysis indicated good stability of this analysis. No obvious heterogeneity or publication bias was detected. Conclusions: ICIs could significantly improve OS in patients with brain metastases and both OS and PFS in patients with liver metastases. Although brain and liver metastases are generally regarded as poor prognostic factors for immunotherapy, this study still indicates ICIs are effective therapeutic options for NSCLC patients with these metastatic sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".