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A Bayesian Network Meta-Analysis of First-Line Treatments for Non-Small Cell Lung Cancer with High Programmed Death Ligand-1 Expression. | LitMetric

A Bayesian Network Meta-Analysis of First-Line Treatments for Non-Small Cell Lung Cancer with High Programmed Death Ligand-1 Expression.

J Clin Med

Division of Hemato-Oncology, Department of Internal Medicine, Kangnam Sacred-Heart Hospital, Hallym University Medical Center, Hallym University College of Medicine, Seoul 07441, Korea.

Published: March 2022

We performed a Bayesian network meta-analysis (NMA) to suggest frontline treatments for advanced non-small cell lung cancer (NSCLC) showing high programmed death ligand-1 (PD-L1) expression. A total of 5237 patients from 22 studies were included. In terms of progression-free survival, immune checkpoint inhibitors (ICIs) plus bevacizumab plus chemotherapy had the highest surface under the cumulative ranking curve (SUCRA) value (98.1%), followed by ICI plus chemotherapy (82.9%). In terms of overall survival (OS), dual immunotherapy plus chemotherapy had the highest SUCRA value (79.1%), followed by ICI plus bevacizumab plus chemotherapy (73.4%). However, there was no significant difference in survival outcomes among treatment regimens combined with immunotherapy. Moreover, ICI plus chemotherapy failed to reveal a significant OS superiority to ICI monotherapy (hazard ratio = 0.978, 95% credible internal: 0.771-1.259). In conclusion, this NMA indicates that ICI plus chemotherapy with/without bevacizumab might to be the best options in terms of OS for advanced NSCLC with high PD-L1 expression. However, considering that there was no significant difference in survival outcomes among treatment regimens incorporating immunotherapy and that ICI plus chemotherapy failed to show significant survival benefits over ICI monotherapy, ICI monotherapy may be reasonable as first-line treatment for advanced NSCLC with high PD-L1 expression.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950568PMC
http://dx.doi.org/10.3390/jcm11061492DOI Listing

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