阑尾癌患者列线图预后模型的构建

Construction of a nomogram prognostic model for patients with appendiceal cancer

目的分析影响阑尾癌预后的独立因素,建立安全可靠的临床预测模型,对患者远期生存率进行个体化预测。方法回顾性分析SEER数据库收集的2000—2010年诊断为阑尾癌患者的临床数据,将所有纳入对象以7∶3随机分为建模组(1280例)和验证组(640例),并构建列线图进行内部验证,预测阑尾癌患者5、10年生存率。通过一致性指数(C指数)、校准图、曲线下面积(AUC)和受试者工作特征(ROC)曲线来评估列线图的判别和预测能力。通过决策曲线分析(DCA)评估列线图的临床应用价值。结果建立了基于年龄、组织学类型、肿瘤分级、肿瘤分期(TNM)、是否手术等影响预后因素的预测模型。预测模型建模组的C指数为0.710,验证组的C指数为0.722。建模组与验证组5、10年ROC曲线AUC值分别为0.766、0.774以及0.788、0.781,确定生存概率的校准曲线,列线图预测结果与实际观测结果吻合较好。结论列线图可以准确预测阑尾癌患者生存率,具有较好的预测精度,有助于对患者进行个性化的预后评估和指导临床决策。

Objective To analyze the independent factors affecting the prognosis of appendiceal cancer through data calculation,and establish a safe and reliable clinical prediction model for individual prediction of long-term survival rate.Methods The clinical data of patients diagnosed with appendiceal cancer from 2000 to 2010 collected by SEER database were retrospectively analyzed,and all included subjects were randomly divided into a modeling group(1280 cases)and a validation group(640 cases)at a ratio of 7:3.A nomogram was constructed for internal validation to predict the survival rates of patients with appendiceal cancer for 5 and 10 years.The discriminatory and predictive capacities of the nomogram were assessed by C-index,calibration,area under the curve(AUC)and time-dependent receiver operating characteristic(ROC)curve.Decision curve analysis(DCA)was used to evaluate the clinical value of the nomogram.Results A prognostic model based on age,histological type,tumor grade,tumor stage(TNM),and surgery was established.The C-index of the modeling group is 0.710,and that of the validation group is 0.722.The AUC values of ROC curves in the modeling group and the validation group at 5 and 10 years were 0.766,0.774 and 0.788,0.781.The calibration curve of survival probability was determined,and the predicted results of the nomogram were consistent with the actual observation results.Conclusion The nomogram can predict the survival rate of appendiceal cancer patients with good prediction accuracy,which is helpful for personalizing prognosis assessment of patients and guiding clinical decision making.