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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/122896


    Title: Comparing the Therapeutic Efficacies of Lung Cancer: Network Meta-analysis Approaches
    Authors: Chang, Chuan-Hsin;Chang, Yue-Cune
    Keywords: air pollution;fine particulate matter (PM2.5);lung cancer;network meta-analysis
    Date: 2022-11-02
    Issue Date: 2023-04-28 16:23:17 (UTC+8)
    Publisher: MDPI AG
    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 me-ta-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%, re-spectively; 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 + Im-muno, and Targeted + Targeted Therapy with cumulative probabilities 93.4, 91.5, and 59.4%, re-spectively; 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.
    Relation: International Journal of Environmental Research and Public Health 19(21), 14324
    DOI: 10.3390/ijerph192114324
    Appears in Collections:[應用數學與數據科學學系] 期刊論文

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