基于SEER数据库恶性脑膜瘤预后列线图的构建与验证

Construction and validation of prognostic nomogram for malignant meningioma based on SEER database

目的 基于SEER数据库构建并验证恶性脑膜瘤患者的预后列线图模型。方法 通过SEER*Stat 8.4.0软件获取SEER数据库1992年至2019年恶性脑膜瘤患者的病例资料,以8∶2比例将筛选所得病例随机分为训练集及验证集。运用R软件4.1.3版进行Lasso回归及多因素Cox回归分析确定恶性脑膜瘤的独立预后因素,并构建其1年、3年及5年生存率的列线图模型。通过一致性指数(C-index)、受试者工作特征曲线(ROC)、校准曲线及风险分层分析评估列线图的可靠性。结果 共筛选出717例患者,其中训练集576例,验证集141例。经Lasso回归及多因素Cox回归分析后,确定年龄、性别、婚姻状态及手术情况为恶性脑膜瘤的独立预后因素(P<0.05)。训练集与验证集的C-index分别为0.683、0.681。对于1年、3年及5年生存率的曲线下面积(AUC)值,训练集分别为0.738、0.717和0.747,验证集分别为0.704、0.664和0.700。模型的校正曲线显示预测值与实际观测值具有良好一致性,决策曲线分析(DCA)显示列线图具有较好的获益性。根据预后因素将恶性脑膜瘤患者进行风险分层后,高低风险两组患者的总生存率存在显著差异(P<0.001)。结论 年龄、性别、婚姻状态及手术与恶性脑膜瘤患者的预后相关,本研究构建的列线图模型能较准确地预测恶性脑膜瘤患者的预后状态,其广泛应用仍需进行外部和前瞻性验证。

Objective To construct and validate a nomogram model for the prognosis of malignant meningioma based on SEER database. Methods The clinical data of patients with malignant meningioma from 1992 to 2019 were obtained from SEER database by SEER*Stat 8.4.0 software, and the screened cases were randomly divided into training set and validation set in a ratio of 8∶2. Using R 4.1.3 software, Lasso regression and multivariate Cox regression analysis were used to determine the independent prognostic factors of malignant meningioma, and the nomogram model of 1-year, 3-year and 5-year overall survival was constructed. The reliability of the nomogram was assessed by C-index, receiver operating characteristic curve(ROC), calibration curve and the risk stratification analysis. Results A total of 717 patients were screened, including 576 in the training set and 141 in the validation set. After Lasso regression and multivariate Cox regression, age, gender, marital status and surgery were determined as independent prognostic factors for malignant meningioma(P<0.05). The C-index of the training set and validation set were 0.683 and 0.681, respectively. For the area under the curve(AUC) values of 1-year, 3-year and 5-year overall survival, the training set was 0.738, 0.717 and 0.747, the validation set was 0.704, 0.664 and 0.700. The calibration curve of the model showed that the predicted survival probability derived from the nomogram was in good agreement with the actual observed value. In addition, decision curve analysis(DCA) also showed that the nomogram had better benefit. After risk stratification of patients with malignant meningioma according to prognostic factors, there was a significant difference in the overall survival between the high and low risk groups(P<0.001). Conclusion Age, gender, marital status and surgery are related to the prognosis of patients with malignant meningioma. The nomogram model constructed in this study can more accurately predict the prognosis of patients with malignant meningioma, but its wide application still needs external and prospective verify.