Wu Lei-Lei, Liu Xuan, Jiang Wen-Mei, Huang Wei, Lin Peng, Long Hao, Zhang Lan-Jun, Ma Guo-Wei
State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Front Oncol. 2020 Apr 21;10:571. doi: 10.3389/fonc.2020.00571. eCollection 2020.
To assess the postoperative prognosis of patients with stage IB non-small cell lung cancer (NSCLC), using a prognostic model (PM). Patients with stage IB of NSCLC from the two academic databases {the Surveillance, Epidemiology, and End Results [SEER-A, = 1,746 (training cohort)], Sun Yat-sen University Cancer Center [SYSUCC, = 247 (validation cohort)], and SEER-B ( = 1,745)} who had undergone lung surgery from 2001 to 2015 were enrolled. The primary clinical endpoint was cancer-specific survival (CSS). Covariate inclusion of prognostic indicators was carried out using a multivariable two-sided < 0.05. We identified and integrated significant prognostic factors for survival in the training cohort to build a model that could be validated in the validation cohort. We used univariate analysis to evaluate the utilized ability of PM in the different races/ethnicities. CSS discrimination in the PM was comparable in both the training and validation cohorts [C index = 0.66(SEER-A), 0.67(SYSUCC), and 0.61(SEER-B), respectively]. Discretization with a fixed PM cutoff of 291.5 determined from the training dataset yielded low- and high-risk subgroups with disparate CSS in the validation cohort (training cohort: hazard ratio [HR] 2.724, 95% confidence intervals [CI], 2.074-3.577; validation cohort: SEER-B HR 1.679, 95% CI, 1.310-2.151, SYSUCC HR 3.649, 95% CI 2.203-6.043, all < 0.05). Our five-factor PM was able to predict CSS; 48-month CSS was 87% in the low-risk subgroup vs. 69% in the high-risk subgroup for the training cohort, while in the validation cohort, they were 80 vs. 73%(SEER-B) and 84 vs. 60% (SYSUCC), respectively. In addition, the results showed that PM with all unadjusted HR > 1 was a significant risk prognostic indictor in white men ( < 0.001), Chinese people ( < 0.001), and other races ( = 0.012). We established and validated a PM that may predict CSS for patients with IB NSCLC in different races/ethnicities, and thus, help clinicians screen subgroups with poor prognosis. In addition, further prospective studies and more cases from different regions are necessary to confirm our findings.
为了使用一种预后模型(PM)评估IB期非小细胞肺癌(NSCLC)患者的术后预后。纳入了来自两个学术数据库{监测、流行病学和最终结果[SEER - A,n = 1746(训练队列)]、中山大学肿瘤防治中心[SYSUCC,n = 247(验证队列)]以及SEER - B(n = 1745)}中在2001年至2015年期间接受肺手术的IB期NSCLC患者。主要临床终点是癌症特异性生存(CSS)。使用多变量双侧检验(P < 0.05)纳入预后指标的协变量。我们在训练队列中识别并整合了生存的显著预后因素,以构建一个可在验证队列中进行验证的模型。我们使用单变量分析来评估PM在不同种族/民族中的应用能力。PM在训练队列和验证队列中的CSS辨别能力相当[C指数分别为0.66(SEER - A)、0.67(SYSUCC)和0.61(SEER - B)]。根据训练数据集确定的固定PM临界值291.5进行离散化,在验证队列中产生了CSS不同的低风险和高风险亚组(训练队列:风险比[HR] 2.724,95%置信区间[CI],2.074 - 3.577;验证队列:SEER - B HR 1.679,95% CI,1.310 - 2.151,SYSUCC HR 3.649,95% CI 2.203 - 6.043,均P < 0.05)。我们的五因素PM能够预测CSS;训练队列中低风险亚组的48个月CSS为87%,高风险亚组为69%,而在验证队列中,SEER - B分别为80%和73%,SYSUCC分别为84%和60%。此外,结果表明所有未调整HR > 1的PM在白人男性(P < 0.001)、中国人(P < 0.001)和其他种族(P = 0.012)中是显著的风险预后指标。我们建立并验证了一个PM,其可能预测不同种族/民族的IB期NSCLC患者的CSS,从而帮助临床医生筛选出预后不良的亚组。此外,需要进一步的前瞻性研究和来自不同地区的更多病例来证实我们的发现。