Zhang Chen-Chen, Hou Run-Ping, Feng Wen, Fu Xiao-Long
Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Front Oncol. 2021 Sep 17;11:736892. doi: 10.3389/fonc.2021.736892. eCollection 2021.
Pathologic N2 non-small cell lung cancer (NSCLC) is prominently intrinsically heterogeneous. We aimed to identify homogeneous prognostic subgroups and evaluate the role of different adjuvant treatments. We retrospectively collected patients with resected pathologic T1-3N2M0 NSCLC from the Shanghai Chest Hospital as the primary cohort and randomly allocated them (3:1) to the training set and the validation set 1. We had patients from the Fudan University Shanghai Cancer Center as an external validation cohort (validation set 2) with the same inclusion and exclusion criteria. Variables significantly related to disease-free survival (DFS) were used to build an adaptive Elastic-Net Cox regression model. Nomogram was used to visualize the model. The discriminative and calibration abilities of the model were assessed by time-dependent area under the receiver operating characteristic curves (AUCs) and calibration curves. The primary cohort consisted of 1,312 patients. Tumor size, histology, grade, skip N2, involved N2 stations, lymph node ratio (LNR), and adjuvant treatment pattern were identified as significant variables associated with DFS and integrated into the adaptive Elastic-Net Cox regression model. A nomogram was developed to predict DFS. The model showed good discrimination (the median AUC in the validation set 1: 0.66, range 0.62 to 0.71; validation set 2: 0.66, range 0.61 to 0.73). We developed and validated a nomogram that contains multiple variables describing lymph node status (skip N2, involved N2 stations, and LNR) to predict the DFS of patients with resected pathologic N2 NSCLC. Through this model, we could identify a subtype of NSCLC with a more malignant clinical biological behavior and found that this subtype remained at high risk of disease recurrence after adjuvant chemoradiotherapy.
病理性N2期非小细胞肺癌（NSCLC）在本质上具有显著的异质性。我们旨在识别出具有相同预后的亚组，并评估不同辅助治疗的作用。我们回顾性收集了来自上海胸科医院的接受手术切除的病理性T1-3N2M0期NSCLC患者作为主要队列，并将他们以3:1的比例随机分配到训练集和验证集1。我们将来自复旦大学附属肿瘤医院的患者作为外部验证队列（验证集2），其纳入和排除标准相同。使用与无病生存期（DFS）显著相关的变量构建自适应弹性网络Cox回归模型。使用列线图来可视化该模型。通过受试者操作特征曲线（AUC）下的时间依赖性面积和校准曲线来评估模型的判别能力和校准能力。主要队列由1312名患者组成。肿瘤大小、组织学类型、分级、跳跃式N2、受累N2站数、淋巴结比率（LNR）和辅助治疗模式被确定为与DFS相关的显著变量，并被纳入自适应弹性网络Cox回归模型。开发了一个列线图来预测DFS。该模型显示出良好的判别能力（验证集1的中位AUC：0.66，范围0.62至0.71；验证集2：0.66，范围0.61至0.73）。我们开发并验证了一个包含多个描述淋巴结状态变量（跳跃式N2、受累N2站数和LNR）的列线图，以预测接受手术切除的病理性N2期NSCLC患者的DFS。通过该模型，我们可以识别出一种具有更恶性临床生物学行为的NSCLC亚型，并发现该亚型在辅助放化疗后疾病复发风险仍然很高。
