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Record W4306685697 · doi:10.3389/fonc.2022.978069

Liver metastases and the efficacy of immune checkpoint inhibitors in advanced lung cancer: A systematic review and meta-analysis

2022· review· en· W4306685697 on OpenAlexaboutno aff
Handai Xia, Wengang Zhang, Yuqing Zhang, Xiaoling Shang, Yanguo Liu, Xiuwen Wang

Bibliographic record

VenueFrontiers in Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsMedicineLung cancerMeta-analysisOncologyCancerImmune systemNivolumabInternal medicineImmunotherapyCancer researchImmunology

Abstract

fetched live from OpenAlex

Background: Liver metastasis is the most common type of lung cancer metastasis, and is a significant prognostic factor in lung cancer. However, the effect of liver metastases on the efficacy of immune checkpoint inhibitors (ICIs) remains inconsistent and controversial. The aim of this study was to explore the relationship between liver metastases and ICI efficacy in patients with advanced lung cancer based on data from randomized controlled trials (RCTs) and observational studies. Methods: PubMed, EMBASE, Cochrane Library databases, conference proceedings, as well as grey literature websites were searched for eligible studies without language restrict ion. Study quality was assessed using Cochrane tools and the Newcastle-Ottawa Quality Assessment Scale (NOS). Outcomes of interest were overall survival (OS) and progression-free survival (PFS). The difference in efficacy between patients with and without liver metastases was calculated by pooling ratios of hazard ratios (HR), as calculated using the deft approach. Results: A total of 16 RCTs and 14 observational trials were included. Analyses of RCTs revealed a survival benefit for ICI treatment (i.e., ICI monotherapy, ICI + Chemotherapy, dual ICI therapy and dual ICI + Chemotherapy) versus standard therapies among non-small cell lung cancer (NSCLC) patients with liver metastases (PFS HR, 0.77; 95%CI, 0.61-0.97; OS HR, 0.78; 95%CI, 0.68-0.90). NSCLC patients with liver metastases achieved less PFS benefit and comparable OS benefit from ICI treatment compared with those without liver metastases (ratios of PFS-HRs, 1.19; 95%CI, 1.02-1.39; P=0.029; Ratios of OS-HRs, 1.10; 95%CI, 0.94-1.29; P=0.24). For patients with small cell lung cancer (SCLC), ICI treatment achieved a marginal effect on patients with liver metastases as compared with standard therapies (OS HR, 0.94; 95%CI, 0.73-1.23). SCLC patients with liver metastases benefited less from ICI treatment than patients without liver metastases (ratio of OS-HRs, 1.22; 95%CI, 1.01-1.46; P=0.036). In real-world data analysis, liver metastasis could be used as an independent prognostic risk factor, increasing the risk of death by 21% in lung cancer patients receiving ICI treatment compared with those without liver metastases (OS HR, 1.21; 95%CI, 1.17-1.27; P<0.0001). Subgroup analysis confirmed that this association was not modified by race (Asian vs. Western) or number of treatment lines. Conclusions: The presence of liver metastases does not significantly influence the OS benefit of ICIs in patients with NSCLC. However, a small amount of data shows that liver metastasis restrains the magnitude of OS benefit in patients with SCLC. Liver metastasis has potential as an independent prognostic risk factor for lung cancer patients receiving ICI treatment in clinical practice. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier (CRD42022306449).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.721
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0140.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.365
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations36
Published2022
Admission routes1
Has abstractyes

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