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初始Ⅳ期乳腺癌预后因素分析

Prognostic factors of primary stage Ⅳ breast cancer
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摘要 目的通过建立列线图模型对影响初始Ⅳ期乳腺癌患者的预后因素进行分析。方法回顾性分析204例初始Ⅳ期乳腺癌患者临床资料,应用Kaplan-Meier法确定临床病理参数、治疗因素与总生存期(OS)的关系,基于Cox风险比例回归模型的单因素及多因素分析,构建列线图并进行内部验证。一致性指数和校准曲线用于评估列线图的有效性。结果患者是否接受乳腺局部手术治疗(P=0.001)、分子分型(P=0.009)及内脏转移(P=0.019)是影响患者生存的独立预后因素。由此三因素建立列线图模型,内部验证的一致性指数为0.671(95%CI:0.647~0.695)。校准曲线表明本预测模型在1年、3年和5年时生存概率与实际情况的符合度较高,具有良好的一致性。结论初始Ⅳ期乳腺癌患者是否接受乳腺局部病灶手术,分子分型和内脏转移是影响预后的独立因素,由此建立的列线图可以较好地预测患者预后,并指导个体化治疗。 Objective To construct a nomogram for the analysis of prognostic factors affecting the survival rate of patients with stage Ⅳ breast cancer.Methods In total,204 cases of primary stageⅣbreast cancer were retrospectively analyzed.The Kaplan-Meier method was used to determine the relationship between clinicopathological parameters,treatment factors,and overall survival.Univariate and multivariate analyses using a Cox risk proportional regression model were performed to construct a nomogram,and the internal concordance index and calibration curves were used to assess the effectiveness of the nomogram.Results In multivariate analysis,surgery(P=0.001),molecular typing(P=0.009),and visceral metastases(P=0.019)were independent prognostic factors;hence,they were included in the nomogram,which had a concordance index of 0.671(95%confidence interval,0.647-0.695).The calibration curves indicated good consistency at 1,3,and 5 years.Conclusion In this single-center study,local breast surgery,molecular typing,and visceral metastasis were independent factors affecting patient prognosis.The nomogram established by the study method was able to better predict the prognosis of patients with initial stage Ⅳ breast cancer and guide individualized treatment.
作者 孙源迪 杨博文 李智 赵雷 滕月娥 SUN Yuandi;YANG Bowen;LI Zhi;ZHAO Lei;TENG Yuee(Department of Medical Oncology,The First Hospital,China Medical University,Shenyang 110001,China)
出处 《中国医科大学学报》 CAS CSCD 北大核心 2020年第12期1118-1124,1129,共8页 Journal of China Medical University
关键词 初始Ⅳ期乳腺癌 临床病理特征 列线图 预后 primary stageⅣbreast cancer clinicopathological feature nomogram prognosis
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