乳头状肾细胞癌预后预测模型的构建与验证：一项基于SEER数据库的回顾性研究

Construction and validation of a predictive model for the prognosis of papillary renal cell carcinoma： a retrospective study based on the Surveillance，Epidemiology，and End Results database

摘要:

目的 建立一个用于评估乳头状肾细胞癌（papillary renal cell carcinoma，PRCC）预后的列线图。方法 获取SEER（Surveillance，Epidemiology，and End Results）数据库的6 028例PRCC患者的临床数据，并将其随机分为训练队列（n=4 220）和验证队列（n=1 808）。使用Cox比例风险回归分析来筛选与PRCC预后相关的临床病理特征。基于Cox模型，构建一个列线图预测PRCC患者的预后，用受试者操作特征曲线及C指数检测模型的区分度，用校准图来评估列线图的预测准确性。结果 从SEER数据库中检索到6 028例PRCC患者的数据。Cox比例风险回归分析结果显示，诊断时的年龄、级别、肿瘤淋巴结转移分期（TNM，AJCC，第7版）、手术治疗、肿瘤数量和婚姻状况是重要的独立预后变量。将所有变量合并以建立列线图。在训练和验证队列中，列线图模型的C指数分别为0.807（95%CI=0.779~0.834）和0.800（95%CI=0.759~0.841），而AJCC TNM分期的C指数分别为0.686（95%CI=0.667~0.706）和0.668（95%CI=0.638~0.697），表明与AJCC TNM分期系统相比，列线图在训练和验证队列中都表现出了良好的总生存率（overall survival，OS）预测能力。校准曲线显示列线图的生存率预测与实际生存率之间高度一致。结论 本研究构建的列线图显示出良好的预测性能，有助于临床评估PRCC患者OS，从而为患者制定个体化的治疗策略提供依据。

Abstract:

Objective To establish a nomogram model for evaluating the prognosis of papillary renal cell carcinoma（PRCC）.Methods Clinical data were collected from 6 028 patients with PRCC in the Surveillance，Epidemiology，and End Results（SEER） database，and they were randomly divided into the training cohort with 4 220 patients and the validation cohort with 1 808 patients. A Cox proportional-hazards regression model analysis was used to identify the clinicopathological features associated with the prognosis of PRCC. A nomogram was established based on the Cox model to predict the prognosis of patients with PRCC； the receiver operating characteristic（ROC） curve and C-index were used to evaluate the discriminatory ability of the model，and the calibration curve was used to assess the predictive accuracy of the nomogram model.Results The data of 6 028 patients with PRCC were retrieved from the SEER database. The Cox proportional-hazards regression model analysis showed that age at diagnosis，grade，tumor-node-metastasis stage（TNM，AJCC，7th edition），surgical treatment，tumor number，and marital status were significant independent prognostic variables，and all the variables were combined to establish a nomogram. The nomogram model had a C-index of 0.807（95%CI=0.779-0.834） in the training cohort and 0.800（95%CI=0.759-0.841） in the validation cohort，and AJCC TNM stage had a C-index of 0.686（95%CI=0.667-0.706） in the training cohort and 0.668（95%CI=0.638-0.697） in the validation cohort，suggesting that compared with AJCC TNM stage，the nomogram model exhibited a good predictive ability for overall survival（OS） rate in the training and validation cohorts. The calibration curve showed high consistency between the OS rate predicted by the nomogram and the actual OS rate.Conclusion The nomogram established in this study shows an excellent predictive performance and can help to evaluate the OS of patients with PRCC，thereby providing a basis for developing individualized treatment strategies.