摘要
目的 分析Ⅳ期结直肠癌患者早期死亡的危险因素,进一步构建并验证Ⅳ期结直肠癌早期死亡的Nomogram预测模型.方法 回顾性分析美国监测、流行病学和结果(the Surveillance,Epidemiology,and End Results,SEER)数据库中2018-2020年期间的Ⅳ期结直肠癌患者的临床病理学数据,按照8:2随机分为训练队列和验证队列,训练队列中采用多因素logistic回归分析筛选出Ⅳ期结直肠癌患者早期死亡的危险因素,进一步构建Nomogram预测模型,通过绘制受试者工作特征曲线(receiver operating characteristic curve,ROC)、校准曲线、临床决策曲线分析(decision curve analysis,DCA)对模型进行评价.结果 年龄(50～70岁组:OR=1.984,P=0.007;＞70 岁组,OR=1.997,P=0.008)、未婚(OR=1.342,P=0.025)、原发肿瘤分化等级 G3+G4(OR=1.817,P＜0.001)、T4 期(OR=1.434,P=0.009)、N2 期(OR=1.621,P＜0.001)、M1c 期(OR=1.439,P=0.036)、未化疗(OR=21.820,P＜0.001)、骨转移(OR=2.000,P=0.042)、脑转移(OR=6.715,P=0.001)、肝转移(OR=1.886,P＜0.001)是Ⅳ期结直肠癌患者发生全因早期死亡的危险因素;年龄(50～70岁组,OR=2.025,P=0.008;＞70岁组,OR=1.925,P=0.017)、原发肿瘤分化等级 G3+G4(OR=1.818,P＜0.001)、T4 期(OR=1.424,P=0.013)、N2 分期(OR=1.637,P＜0.001)、M1c 期(OR=1.541,P=0.016)、未化疗(OR=21.832,P＜0.001)、脑转移(OR=6.089,P=0.001)、肝转移(OR=2.100,P＜0.001)是Ⅳ期结直肠癌患者发生癌症特异性早期死亡的危险因素.基于这些变量构建Ⅳ期结直肠癌患者发生全因早期死亡及癌症特异性早期死亡的2个Nomogram预测模型.训练队列中全因早期死亡预测模型的曲线下面积(area under curve,AUC)值为0.874[95％CI(0.855,0.893)],癌症特异性早期死亡预测模型的AUC值为0.874[95％CI(0.855,0.894)];验证队列中全因早期死亡预测模型的AUC值为0.868[95％CI(0.829,0.907)],癌症特异性早期死亡预测模型的AUC值为0.867[95％CI(0.827,0.907)],显示模型具有很好的预测能力.校准曲线显示预测模型对Ⅳ期结直肠癌早期死亡预测结果与实际结果间有良好的一致性,DCA曲线显示模型能使患者有良好的临床获益.结论 本研究建立的预测模型对Ⅳ期结直肠癌患者的早期死亡具有较好的预测性能,有助于临床医师早期识别高危患者,临床制定个体化治疗方案.
Abstract
Objective To analyze the risk factors for early mortality in patients with stage Ⅳ colorectal cancer,and further construct and validate Nomogram prediction model for early mortality in stage Ⅳ colorectal cancer.Methods A retrospective analysis was conducted on the clinical and pathological data of stage Ⅳ colorectal cancer patients from the Surveillance,Epidemiology,and End Results(SEER)database in the United States from 2018 to 2020.The study data was randomly divided into a training cohort and a validation cohort at a ratio of 8:2.Multivariate logistic regression analysis was performed in the training cohort to screen for risk factors for early mortality in stage N colorectal cancer patients,and Nomogram prediction model was further constructed.Receiver operating characteristic curve(ROC),calibration curve,and clinical decision curve analysis(DCA)were plotted.Results Age(50-70 group,OR=1.984,P=0.007;＞70 group,OR=1.997,P=0.008),unmarried(OR=1.342,P=0.025),primary tumor differentiation of G3+G4(OR=1.817,P＜0.001),T4 stage(OR=1.434,P=0.009),N2 stage(OR=1.621,P＜0.001),M1c stage(OR=1.439,P=0.036),no chemotherapy(OR=21.820,P＜0.001),bone metastasis(OR=2.000,P=0.042),brain metastasis(OR=6.715,P=0.001)and liver metastasis(OR=1.886,P＜0.001)were risk factors for all-cause early death in stage Ⅳ colorectal cancer patients.Age(50-70 group,OR=2.025,P=0.008;＞70 group,OR=1.925,P=0.017),primary tumor differentiation grade of G3+G4(OR=1.818,P＜0.001),T4 stage(OR=1.424,P=0.013),N2 stage(OR=1.637,P＜0.001),M1c stage(OR=1.541,P=0.016),no chemotherapy(OR=21.832,P＜0.001),brain metastasis(OR=6.089,P=0.001),liver metastasis(OR=2.100,P＜0.001)were factors for cancer-specific early death of stages Ⅳ colorectal cancer patients.Based on these variables,we constructed two Nomogram prediction models for all-cause early death and cancer-specific early death in stage Ⅳ colorectal cancer patients.The area under curve(AUC)value of the all-cause early death prediction model in the training queue was 0.874[95％CI(0.855,0.893)],and the AUC value of the cancer specific early death prediction model was 0.874[95％CI(0.855,0.894)];the AUC value of the all-cause early death prediction model in the validation queue was 0.868[95％CI(0.829,0.907)],and the AUC value of the cancer specific early death prediction model was 0.867[95％CI(0.827,0.907)],indicating that the model had good predictive ability.The calibration curve showed that the predictive models had good consistency with the actual results for predicting early mortality in stage Ⅳ colorectal cancer,and the DCA curve showed that the models could provide patients with higher clinical benefits.Conclusion The predictive models established in this study have good predictive performance for early mortality in stage Ⅳ colorectal cancer patients,which is helpful for clinical physicians to identify high-risk patients in the early stage and develop personalized treatment plans in clinical practice.
基金项目
四川省科技厅应用基础研究重点项目(2017JY0020)
四川大学华西医院临床研究孵化项目(2022HXFH028)
NETL
NSTL
万方数据