Comparing the Therapeutic Efficacies of Lung Cancer: Network Meta-Analysis Approaches

Int J Environ Res Public Health. 2022 Nov 2;19(21):14324. doi: 10.3390/ijerph192114324.

Abstract

Background: In recent years, reduction of nuclear power generation and the use of coal-fired power for filling the power supply gap might have increased the risk of lung cancer. This study aims to explore the most effective treatment for different stages of lung cancer patients.

Methods: We searched databases to investigate the treatment efficacy of lung cancer. The network meta-analysis was used to explore the top three effective therapeutic strategies among all collected treatment methodologies.

Results: A total of 124 studies were collected from 115 articles with 171,757 participants in total. The results of network meta-analyses showed that the best top three treatments: (1) in response rate, for advanced lung cancer were Targeted + Targeted, Chemo + Immuno, and Targeted + Other Therapy with cumulative probabilities 82.9, 80.8, and 69.3%, respectively; for non-advanced lung cancer were Chemoradio + Targeted, Chemoradi + Immuno, and Chemoradio + Other Therapy with cumulative probabilities 69.0, 67.8, and 60.7%, respectively; (2) in disease-free control rate, for advanced lung cancer were Targeted + Others, Chemo + Immuno, and Targeted + Targeted Therapy with cumulative probabilities 93.4, 91.5, and 59.4%, respectively; for non-advanced lung cancer were Chemo + Surgery, Chemoradio + Targeted, and Surgery Therapy with cumulative probabilities 80.1, 71.5, and 43.1%, respectively.

Conclusion: The therapeutic strategies with the best effectiveness will be different depending on the stage of lung cancer patients.

Keywords: air pollution; fine particulate matter (PM2.5); lung cancer; network meta-analysis.

Publication types

  • Meta-Analysis
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Coal / analysis
  • Disease-Free Survival
  • Humans
  • Lung Neoplasms* / drug therapy
  • Lung Neoplasms* / therapy
  • Network Meta-Analysis
  • Particulate Matter

Substances

  • Coal
  • Particulate Matter

Grants and funding

This work was supported by the Ministry of Science and Technology of Taiwan [grant number MOST 107-2118-M-032-004].