乳腺癌肺转移患者预后危险因素的分析及诺模图的构建

Analysis in prognostic risk factors of patients with lung metastasis of breast cancer and establishment of nomogram

目的探讨乳腺癌肺转移患者预后危险因素,构建生存预测的诺模图(Nomogram图)模型。方法数据来源于监测、流行病学和最终结果数据库(SEER数据库)。筛选2010—2015年间5334例诊断为乳腺癌肺转移的患者,按2∶1的比例分为建模集3556例和验证集1778例。采用多变量Cox回归模型分析与总生存率(OS)相关的因素,采用Fine-Gray竞争风险模型评估与乳腺癌特异性生存率(BCSS)相关的因素。结果建模集与验证集在年龄、种族、性别、原发肿瘤部位、分化分级、T分期、N分期、病理类型、是否手术、是否放化疗、区域淋巴结检出数目、其他远处转移位点(骨、脑、肝)、分子亚型、恶性肿瘤数目、婚姻状态、保险状态等方面比较,差异均无统计学意义(P>0.05)。多变量Cox回归结果显示,年龄、肿瘤分级、T分期、病理组织类型、手术、骨转移、脑转移、肝转移、分子分型与乳腺癌肺转移患者的预后具有相关性(P<0.05)。采用受试者工作特征(ROC)曲线和3年生存率的校准曲线对总生存率预测模型进行内部和外部的验证,结果显示内部验证ROC曲线的曲线下面积(AUC)为0.738,外部验证ROC曲线的AUC为0.746,说明构建的预测模型具有高度的辨别能力和准确性。应用Fine-Gray竞争风险模型对乳腺癌肺转移癌症特异性生存率影响因素进行分析,结果显示年龄、分化程度、T分期、手术、化疗、骨转移、脑转移、肝转移、分子亚型是乳腺癌特异性预后的独立影响因素。根据建模集Fine-Gray竞争风险模型的分析结果,采用ROC曲线和3年生存率的校准曲线对癌症特异性生存率模型进行内部和外部的验证,内部验证ROC曲线的AUC为0.722,外部验证ROC曲线的AUC为0.708,说明构建的模型有较好的辨别能力和准确性。结论基于筛选出的与乳腺癌肺转移患者预后相关的危险因素,构建具有良好准确性的预测Nomogram图,提供了一种有效预测个体生存率的方法。

Objective To explore the prognostic risk factors of patients with lung metastasis of breast cancer and construct nomogram model for survival prediction.Methods Datum were collected from the database of the Surveillance,Epidemiology,and End Results(SEER database).A total of 5334 patients diagnosed as lung metastasis of breast cancer from 2010 to 2015 were screened and divided into the modeling set with 3556 cases and the validation set with 1778 cases according to the ratio of 2 to 1.Multivariate Cox regression model was used to analyze the factors related to overall survival(OS),and Fine-Gray competitive risk model was used to evaluate the factors related to breast cancer specific survival(BCSS).Results There were no significant differences in terms of age,race,gender,primary tumor location,differentiation and grading,T stage,N stage,pathological type,accepting operation or not,accepting radiotherapy and chemotherapy or not,the number of regional lymph nodes,other distant metastasis sites(bone,brain,liver),molecular subtype,number of malignant tumors,marital status and insurance status between the modeling set and the validation set(P>0.05).Multivariate Cox regression analysis showed that age,tumor grading,T stage,pathological tissue type,operation,bone metastasis,brain metastasis,liver metastasis and molecular typing were correlated with the prognosis of patients with lung metastasis of breast cancer(P<0.05).The receiver operating characteristic(ROC)curve and the calibration curve of 3-year survival were used for internal and external validations of the prediction model of overall survival,and the results showed that the area under the ROC curve(AUC)was 0.738 for internal validation and 0.746 for external validation,which indicated that the prediction model was highly discriminative and accurate.Fine-Gray competitive risk model was applied to analyze the factors affecting the cancer specific survival rate of patients with lung metastasis of breast cancer,and the results showed that age,differentiation degree,T stage,surgery,chemotherapy,bone metastasis,brain metastasis,liver metastasis and molecular subtype were independent factors affecting the specific prognosis of breast cancer.According to the analysis results of Fine-Gray competitive risk model based on the modeling set,the ROC curve and the 3-year survival calibration curve were used for internal and external validation of the cancer specific survival rate model,and the AUC of the ROC curve was 0.722 for internal validation and 0.708 for external validation,which indicated that the prediction model was highly discriminative and accurate.Conclusion Based on the selected risk factors related to the prognosis of patients with lung metastasis of breast cancer,we establish a prediction nomogram with good accuracy,which provides an effective method for prediction of individual survival.