作者：周雨迪，杨刚，周启阳，陈昕，胡优，周晓俊

单位：苏州大学附属第一医院 普通外科，江苏 苏州 215006

Authors: Zhou Yudi, Yang Gang, Zhou Qiyang, Chen Xin, Hu You, Zhou Xiaojun

Unit: Department of General Surgery, the First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu,China

摘要：

目的 本研究旨在基于美国监测、流行病学、最终结果(SEER)数据库数据,建立预测接受手术的Ⅱ期结肠腺癌患者的肿瘤特异生存(CSS)率和总生存(OS)率的列线图模型。方法 从SEER数据库中,筛选出确诊为Ⅱ期结肠腺癌的术后患者,通过单因素与多因素Cox回归分析,分别筛选出OS率及CSS率的独立危险因素,依此构建关于OS率及CSS率的预后列线图预测模型。通过计算一致性指数(C-index)、受试者操作特征曲线(ROC曲线)并绘制校准曲线,检验模型预测准确性。并根据列线图结果,进行危险分层。结果 多因素Cox回归分析结果提示:年龄、种族、性别、AJCC T分期、送检淋巴结数、癌胚抗原(CEA)水平、肿瘤沉积、神经侵犯、婚姻状态为OS率的独立危险因素,年龄、种族、肿瘤直径、AJCC T分期、活检淋巴结数、CEA水平、肿瘤沉积、神经侵犯、保险状态、婚姻状态是CSS率的独立危险因素。基于多因素Cox回归分析结果,建立了预测CSS率、OS率的预后列线图模型,列线图所示的校准曲线、C-index和ROC曲线显示了较好的预测精确度。而后通过列线图计算不同个体得分,进行危险分层,高危患者接受化疗具有较好的OS获益。结论 本研究建立了基于主要临床病理特征的预后列线图预测模型,可作为临床医生预测接受手术的Ⅱ期结肠腺癌患者CSS率和OS率的工具。并可通过此模型,进行风险分层,依据此危险分层,筛选Ⅱ期结肠腺癌化疗对象。

关键词：结肠癌；预后分析；危险分层

Abstract：

Objective This study aims to establish nomogram for predication of cancer -specific survival（CSS）and overall survival（OS）in patients with stage Ⅱ colon adenocarcinoma undergoing surgery based on data from the US Surveillance, Epidemiology, and End Results（SEER）database. Method From the SEER database, patients diagnosed with stage Ⅱ colon adenocarcinoma were extracted. Univariate and multivariate Cox regression analysis were used to determine the independent prognostic factors affecting OS and CSS, and the prognostic nomogram model of OS and CSS were constructed. Accuracy of these nomograms were measured by the consistency index（C-index）, receiver operating characteristic curve（ROC curve）and calibration curves. According to the results of the nomogram, risk stratification was carried out. Result Multivariate Cox regression analysis showed that: Age, race, sex, AJCC T stage, the number of examined lymph node, CEA and tumor deposition, perineural invasion and marital status were independent risk factors for the OS. While age, race, tumor size, AJCC T stage, the number of examined lymph node, CEA, tumor deposition, perineural invasion, insurance status, marital status were independent risk factors of CSS. Based on the results of multivariate Cox regression analysis , the prognostic nomogram models for CSS and OS were established, and the calibration curve, C-index and ROC curve showed good prediction accuracy. Then the risk stratification was carried out by calculating the scores of different individuals through the nomogram. High risk patients tended to have a better OS when they received chemotherapy. Conclusion This study established a prognostic nomogram model based on major clinicopathological features, which can be used as a tool for clinicians to predict CSS and OS in patients with stage Ⅱ colon adenocarcinoma undergoing surgery. This model can be used to stratify the risk of stage Ⅱ colon adenocarcinoma and screen out patients that should receive chemotherapy.

Key Words: Colon cancer; Prognostic analysis; Risk stratification