肺腺癌患者列线图预后模型的构建与验证

目的 基于SEER数据库的大样本数据,构建肺腺癌患者生存预后的列线图预测模型.方法 回顾性分析SEER数据库收集的2010—2015年诊断为肺腺癌患者的临床数据.根据影响肺腺癌患者预后的独立因素,采用Lasso Cox回归分析构建列线图模型.C指数和校准曲线评估列线图的判别和校准能力.使用NRI和DCA曲线评估列线图的...

Construction and Validation of A Nomogram Prognostic Model for Patients with Lung Adenocarcinoma

Objective To construct a nomogram prognostic model for predicting the survival of patients with lung adenocarcinoma based on the large sample data from the SEER database. Methods We retrospectively analyzed the clinical data of patients who were diagnosed with lung adenocarcinoma from 2010 to 2015 in the SEER database. A nomogram model was created based on independent parameters influencing the prognosis of patients with lung adenocarcinoma using Lasso Cox regression analysis. The C-index and calibration curve were utilized to assess the ability to distinguish and calibrate the nomogram. NRI and DCA curves were used to evaluate the prediction ability and net benefit of the nomogram. Results A total of 15 independent risk factors affecting the prognosis of lung adenocarcinoma were identified and integrated into the nomogram model. The C-index of the prediction model was 0.819 in the training cohort and 0.810 in the validation cohort. The predicted specific survival rate of the 1-, 3- and 5-year calibration curves of the training cohort and the validation cohort were consistent with the actual specific survival rate. In comparison to the 7th edition of the AJCC TNM staging system, the NRI and DCA curves demonstrated a considerable boost to the predictive capacity and net benefits achieved by the nomogram model. The risk stratification model constructed with this nomogram model was able to distinguish the patients with different risks well (P<0.0001). Conclusion A nomogram prognostic model is successfully developed and validated, which provides asimple and reliable tool for the survival prediction of the patients with lung adenocarcinoma. Meanwhile, therisk stratification model constructed by the prediction model can conveniently screen patients with different risks, which is important for the individualized treatment of lung adenocarcinoma patients.