Thoracic Surgery Division, The Ohio State University Wexner Medical Center, Columbus, OH.
Center for Biostatistics, Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH.
Clin Lung Cancer. 2021 Jul;22(4):e574-e583. doi: 10.1016/j.cllc.2020.10.009. Epub 2020 Oct 22.
Stage I non-small-cell lung cancer (NSCLC) is potentially curable with surgical resection. Significant proportions of patients may still experience recurrence and death despite undergoing curative surgery. This study describes predictive nomograms for recurrence-free (RFS) and overall survival (OS) after lobectomy.
A total of 301 patients with the American Joint Committee on Cancer pathologic stage IA and IB NSCLC who underwent open, thoracoscopic, or robotic lobectomy from January 2011 to April 2017 were analyzed. Multivariate Cox proportional hazards regression models were used to create nomograms for OS and RFS. Kaplan-Meier survival curves were calculated for OS and RFS comparing high-risk and low-risk cohorts based on nomogram scores.
Histology (hazard ratio [HR], 0.24; 95% confidence interval [CI], 0.10-0.56; P = .002), lymphovascular invasion (HR, 0.46; 95% CI, 0.29-0.74; P = .001), smoking status (HR, 3.46; 95% CI, 1.25-9.55: P = .02), and total lymph nodes removed (HR, 1.05; 95% CI, 1.01-1.10; P = .021) were significant predictors for OS in a multivariate model. Lymphovascular invasion (HR, 0.55; 95% CI, 0.36-0.83; P = .0040), smoking status (HR, 2.56; 95% CI, 1.16-5.62; P = .02), total lymph nodes removed (HR, 1.04; 95% CI, 1.00-1.08; P = .029), and tumor size (HR, 1.30; 95% CI, 1.30-1.68; P = .047) were significant predictors of RFS in a multivariate model.
Nomograms can predict OS and RFS for pathologic stage IA and IB NSCLC after lobectomy regardless of operative approach. The risk for death and recurrence after stratification by the nomogram scores may provide guidance regarding adjuvant therapy and surveillance.
Ⅰ期非小细胞肺癌（NSCLC）通过手术切除有治愈的可能。尽管接受了根治性手术，仍有相当比例的患者会出现复发和死亡。本研究描述了肺叶切除术后无复发生存（RFS）和总生存（OS）的预测列线图。
分析了 2011 年 1 月至 2017 年 4 月期间接受开胸、胸腔镜或机器人肺叶切除术的 301 例美国癌症联合委员会（AJCC）病理分期为ⅠA 和ⅠB 的 NSCLC 患者。使用多变量 Cox 比例风险回归模型为 OS 和 RFS 创建列线图。根据列线图评分，通过 Kaplan-Meier 生存曲线比较高危和低危队列的 OS 和 RFS。
组织学（风险比 [HR]，0.24；95%置信区间 [CI]，0.10-0.56；P =.002）、脉管侵犯（HR，0.46；95% CI，0.29-0.74；P =.001）、吸烟状态（HR，3.46；95% CI，1.25-9.55；P =.02）和清除的总淋巴结数（HR，1.05；95% CI，1.01-1.10；P =.021）是多变量模型中 OS 的显著预测因素。脉管侵犯（HR，0.55；95% CI，0.36-0.83；P =.0040）、吸烟状态（HR，2.56；95% CI，1.16-5.62；P =.02）、清除的总淋巴结数（HR，1.04；95% CI，1.00-1.08；P =.029）和肿瘤大小（HR，1.30；95% CI，1.30-1.68；P =.047）是多变量模型中 RFS 的显著预测因素。
无论手术方式如何，列线图都可以预测肺叶切除术后ⅠA 和ⅠB 期 NSCLC 的 OS 和 RFS。通过列线图评分分层后的死亡和复发风险可能为辅助治疗和监测提供指导。
