Li Jun, Xu Hui-Lin, Li Wei-Xi, Ma Xiao-Yu, Liu Xiao-Hua, Zhang Zuo-Feng
Department of Non-Communicable Diseases Prevention and Control, Shanghai Minhang Center for Disease Control and Prevention, Shanghai, 201101, China.
Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA.
BMC Cancer. 2025 Apr 9;25(1):646. doi: 10.1186/s12885-025-14036-9.
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.
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.
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.
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.
本研究旨在评估影响社区肺癌筛查队列中肺癌患者生存的预后因素。
本基于人群的前瞻性队列研究共纳入25310名符合条件的参与者,这些参与者来自2013年至2017年启动的社区肺癌筛查项目。采用Kaplan-Meier法和对数秩检验进行生存分析。利用Cox比例风险回归模型确定预后因素,包括人口统计学特征、危险因素、低剂量CT(LDCT)筛查和治疗信息。
在研究期间,筛查队列共识别出429例肺癌患者(男性276例,女性153例)。1年、3年和5年生存率分别为74.4%、59.4%和54.5%。多因素分析发现的预后因素包括性别(男性与女性,HR:2.96,95%CI:1.88 - 4.64)、年龄(HR:1.02,95%CI:1.00 - 1.05)、个人月收入(2000 - 3999元与<2000元,HR:0.70,95%CI:0.52 - 0.95)、病理类型(小细胞癌与腺癌,HR:2.55,95%CI:1.39 - 4.66)、分期(IV期与0 - I期,HR:5.21,95%CI:2.78 - 9.75;III期与0 - I期,HR:3.81,95%CI:1.88 - 7.74)、手术(是与否,HR:0.36,95%CI:0.23 - 0.57)以及基础模型识别出的肺癌患者中的KPS(HR:0.98,95%CI:0.98 - 0.99)。此外,在LDCT筛查模型中,实性结节(非实性结节与实性结节,HR:0.47,95%CI:0.23 - 0.96)和较大尺寸结节(HR:1.02,95%CI:1.00 - 1.03)与肺癌预后较差相关。
确定了LDCT筛查检测出的肺癌患者的预后因素,这可能有助于指导临床医生在肺癌管理和治疗的决策过程。需要进行更大样本量和更详细随访数据的进一步研究以进行预后预测。