Literature DB >> 32191390

Analysis of clinical features and prognostic factors of lung cancer patients: A population-based cohort study.

Yuan Gao1, Xinjia Zhou2.   

Abstract

OBJECTIVES: This paper analyses clinical features of lung cancer patients and discusses factors influencing the lung cancer occurrence and prognosis.
METHODS: Patients diagnosed with lung cancer from 1975 to 2016 are analysed based on SEER database. The samples are divided into groups according to the number of positive lymph nodes of LN > 3 and LN ≤ 3. Univariate and multivariate Cox risk models are performed. After balancing the clinicopathological features of the two groups with the propensity score matching (PSM) method, the survival rates of the two groups are compared.
RESULTS: A total of 30 864 patients are included in this study. Kaplan-Meier curves show that the survival rate of patients with LN ≤ 3 is higher than that of patients with LN > 3 (P < 0.0001). Univariate and multivariate Cox proportional risk model analysis suggests that the number of lymph nodes is an independent prognostic risk factor for lung cancer. LN ≤ 3 group shows better OS (HR2.066; 95% CI 1.941-2.199, P < 0.01) and better CSS (HR 2.461; 95% CI 2.304-2.629, P < 0.01). In addition, age at diagnosis, gender, Laterality, Derived AJCC T, 7th ed (2010-2015), Derived AJCC N, 7th ed (2010-2015) and Derived AJCC M, 7th ed, (2010-2015) have also been proved to be potential prognostic factors. A total of 1,851 pairs of patients are screened after 1:1 PSM matching. Patients with LN ≤ 3 have significant improvements in OS and CSS (HR 1.09; 95% CI 1.001-1.187, P < 0.05 and HR 1.127; 95% CI 1.03-1.232, P < 0.001).
CONCLUSION: The number of lymph nodes is an independent prognostic risk factor for lung cancer. Patients with fewer lymph node positives have a better survival prognosis than patients with more lymph nodes.
© 2020 John Wiley & Sons Ltd.

Entities:  

Keywords:  SEER database; lung cancer; lymph nodes; survival

Year:  2020        PMID: 32191390     DOI: 10.1111/crj.13188

Source DB:  PubMed          Journal:  Clin Respir J        ISSN: 1752-6981            Impact factor:   2.570


  2 in total

1.  A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers.

Authors:  Yueying Wang; Shuai Liu; Zhao Wang; Yusi Fan; Jingxuan Huang; Lan Huang; Zhijun Li; Xinwei Li; Mengdi Jin; Qiong Yu; Fengfeng Zhou
Journal:  Medicina (Kaunas)       Date:  2021-01-22       Impact factor: 2.430

2.  Clinical characteristics and prediction model of long-term survival of patients with stage III non-small cell lung cancer.

Authors:  Jun Zhang; Deruo Liu
Journal:  Transl Cancer Res       Date:  2021-03       Impact factor: 1.241

  2 in total

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