Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian.
Department of Reproductive Medicine Centre, Quanzhou Maternity and Child Health Care Hospital.
Eur J Cancer Prev. 2024 Jul 1;33(4):376-385. doi: 10.1097/CEJ.0000000000000868. Epub 2024 May 16.
The tumor, node and metastasis stage is widely applied to classify lung cancer and is the foundation of clinical decisions. However, increasing studies have pointed out that this staging system is not precise enough for the N status. In this study, we aim to build a convenient survival prediction model that incorporates the current items of lymph node status.
We performed a retrospective cohort study and collected the data from resectable nonsmall cell lung cancer (NSCLC) (IA-IIIB) patients from the Surveillance, Epidemiology, and End Results database (2006-2015). The x-tile program was applied to calculate the optimal threshold of metastatic lymph node ratio (MLNR). Then, independent prognostic factors were determined by multivariable Cox regression analysis and enrolled to build a nomogram model. The calibration curve as well as the Concordance Index (C-index) were selected to evaluate the nomogram. Finally, patients were grouped based on their specified risk points and divided into three risk levels. The prognostic value of MLNR and examined lymph node numbers (ELNs) were presented in subgroups.
RESULTS TOTALLY,: 40853 NSCLC patients after surgery were finally enrolled and analyzed. Age, metastatic lymph node ratio, histology type, adjuvant treatment and American Joint Committee on Cancer 8th T stage were deemed as independent prognostic parameters after multivariable Cox regression analysis. A nomogram was built using those variables, and its efficiency in predicting patients' survival was better than the conventional American Joint Committee on Cancer stage system after evaluation. Our new model has a significantly higher concordance Index (C-index) (training set, 0.683 v 0.641, respectively; P < 0.01; testing set, 0.676 v 0.638, respectively; P < 0.05). Similarly, the calibration curve shows the nomogram was in better accordance with the actual observations in both cohorts. Then, after risk stratification, we found that MLNR is more reliable than ELNs in predicting overall survival.
We developed a nomogram model for NSCLC patients after surgery. This novel and useful tool outperforms the widely used tumor, node and metastasis staging system and could benefit clinicians in treatment options and cancer control.
肿瘤、淋巴结和转移分期广泛应用于肺癌分类，是临床决策的基础。然而，越来越多的研究指出，该分期系统对 N 分期不够精确。本研究旨在建立一个方便的生存预测模型，纳入当前淋巴结状态项目。
我们进行了一项回顾性队列研究，从监测、流行病学和最终结果数据库（2006-2015 年）中收集可切除非小细胞肺癌（NSCLC）（IA-IIIB）患者的数据。应用 X-tile 程序计算转移性淋巴结比率（MLNR）的最佳阈值。然后，通过多变量 Cox 回归分析确定独立预后因素，并纳入建立列线图模型。通过校准曲线和一致性指数（C-index）选择评估列线图。最后，根据指定的风险点对患者进行分组，并分为三个风险级别。在亚组中展示 MLNR 和检查淋巴结数量（ELNs）的预后价值。
共纳入 40853 例手术后 NSCLC 患者进行分析。多变量 Cox 回归分析后，年龄、转移性淋巴结比率、组织学类型、辅助治疗和美国癌症联合委员会第 8 期被认为是独立的预后参数。使用这些变量构建了一个列线图模型，其预测患者生存的效率优于评估后的传统美国癌症联合委员会分期系统。我们的新模型具有显著更高的一致性指数（C-index）（训练集，分别为 0.683 和 0.641，P<0.01；测试集，分别为 0.676 和 0.638，P<0.05）。同样，校准曲线表明列线图在两个队列中与实际观察结果更吻合。然后，在风险分层后，我们发现 MLNR 比 ELNs 更能可靠地预测总生存期。
我们为 NSCLC 术后患者开发了一个列线图模型。这种新颖且有用的工具优于广泛使用的肿瘤、淋巴结和转移分期系统，可使临床医生在治疗选择和癌症控制方面受益。
