Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Heilongjiang Province, People's Republic of China.
J Cancer Res Clin Oncol. 2023 Oct;149(13):11779-11790. doi: 10.1007/s00432-023-05073-7. Epub 2023 Jul 5.
Currently, the prognosis of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy is poor. The goal of this research was to develop and validate a novel nomogram for exactly predicting the overall survival (OS) of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy.
The data applied in our research were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. We divided selected data into a training cohort and a validation cohort using R software, with a ratio of 7:3. Univariate Cox regression and multivariate Cox regression were utilized to select significant variables to build the nomogram. To validate our nomogram, calibration curves, receiver operating characteristic curves (ROC), decision curve analysis (DCA), and Kaplan-Meier survival curves were employed. The nomogram model was also compared with the tumor-node-metastasis (TNM) staging system by utilizing net reclassification index (NRI) and integrated discrimination improvement (IDI).
Eight variables-age, sex, operative type, LN removed number, chemotherapy, AJCC stage, M stage, histology-were statistically significant in the multivariate Cox regression analysis and were selected to develop our nomogram. Based on ROC curves, calibration curves, and DCA analysis, our novel nomogram demonstrated good predictive accuracy and clinical utility. Using Kaplan-Meier (KM) survival curves and log-rank tests, the risk stratification system was able to stratify patients based on their estimated mortality risk. The nomogram performed better than the TNM staging system based on the NRI and IDI indexes.
We developed and validated a nomogram to predict prognosis of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. Using this nomogram, clinicians may find this nomogram useful in predicting OS of targeted patients and making more appropriate treatment decisions.
目前，接受新辅助放疗的可切除 N2 非小细胞肺癌患者的预后较差。本研究旨在开发和验证一种新的列线图，以准确预测接受新辅助放疗的可切除 N2 非小细胞肺癌患者的总生存期（OS）。
我们从监测、流行病学和最终结果（SEER）数据库中下载了本研究中使用的数据。我们使用 R 软件将选定的数据分为训练队列和验证队列，比例为 7:3。使用单变量 Cox 回归和多变量 Cox 回归选择显著变量来构建列线图。为了验证我们的列线图，我们使用校准曲线、接收者操作特征曲线（ROC）、决策曲线分析（DCA）和 Kaplan-Meier 生存曲线。我们还通过净重新分类指数（NRI）和综合判别改善（IDI）比较了列线图模型与肿瘤-淋巴结-转移（TNM）分期系统。
多变量 Cox 回归分析中，有 8 个变量（年龄、性别、手术类型、淋巴结切除数、化疗、AJCC 分期、M 分期、组织学）具有统计学意义，并被选择用于开发我们的列线图。基于 ROC 曲线、校准曲线和 DCA 分析，我们的新列线图显示出良好的预测准确性和临床实用性。使用 Kaplan-Meier（KM）生存曲线和对数秩检验，该风险分层系统能够根据估计的死亡率风险对患者进行分层。基于 NRI 和 IDI 指标，列线图的性能优于 TNM 分期系统。
我们开发并验证了一种列线图，以预测接受新辅助放疗的可切除 N2 非小细胞肺癌患者的预后。临床医生可以使用该列线图预测目标患者的 OS，并做出更合适的治疗决策。
