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Article Abstract
Objective: This study aimed to evaluate the prognostic factors influencing the survival of patients with lung cancer identified from a lung cancer screening cohort in the community.
Methods: A total of 25,310 eligible participants were enrolled in this population-based prospective cohort study, derived from a community lung cancer screening program started from 2013 to 2017. Survival analyses were conducted using the Kaplan-Meier method and the log-rank test. Cox proportional hazards regression models were utilized to identify prognostic factors, including demographic characteristics, risk factors, low-dose CT (LDCT) screening, and treatment information.
Results: The screening cohort identified a total of 429 patients with lung cancer (276 men, 153 women) during the study period. The 1-year, 3-year, and 5-year survival rates were 74.4%, 59.4% and 54.5%, respectively. The prognostic factors discovered by the multivariate analysis include gender (male vs. female, HR: 2.96, 95% CI: 1.88-4.64), age (HR: 1.02, 95% CI: 1.00-1.05), personal monthly income (2000-3999 CNY vs. < 2000 CNY, HR: 0.70, 95% CI: 0.52-0.95), pathological type (small cell carcinoma vs. adenocarcinoma, HR: 2.55, 95% CI: 1.39-4.66), stage (IV vs. 0-I, HR: 5.21, 95% CI: 2.78-9.75; III vs. 0-I, HR: 3.81, 95% CI: 1.88-7.74), surgery (yes vs. no, HR: 0.36, 95% CI: 0.23-0.57), and KPS (HR: 0.98, 95% CI: 0.98-0.99) among lung cancer patients identified by the basic model. Furthermore, solid nodule (non-solid nodule vs. solid nodule, HR: 0.47, 95% CI: 0.23-0.96) and larger-sized nodule (HR: 1.02, 95% CI: 1.00-1.03) were associated with a worse prognosis for lung cancer in the LDCT screening model.
Conclusion: Prognostic factors of patients with lung cancer detected by LDCT screening were identified, which could potentially guide clinicians in the decision-making process for lung cancer management and treatment. Further studies with larger sample sizes and more detailed follow-up data are warranted for prognostic prediction.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984240 | PMC |
| http://dx.doi.org/10.1186/s12885-025-14036-9 | DOI Listing |
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