Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
School of Basic Medical Sciences, Xi'an Key Laboratory of Immune Related Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Clin Breast Cancer. 2024 Jun;24(4):351-362. doi: 10.1016/j.clbc.2024.02.001. Epub 2024 Feb 7.
Currently, research on the prognostic factors of unilateral breast cancer (UBC) patients receiving contralateral prophylactic mastectomy (CPM) is limited. This study aimed to construct a new nomogram to predict these patients' overall survival (OS).
In this retrospective study, 88,477 patients who underwent CPM or unilateral mastectomy (UM) were selected from the Surveillance, Epidemiology, and End Results database. Kaplan-Meier curves and Cox regression analyses were used to determine the difference in the impact of the 2 surgical methods on the prognosis. Multivariate Cox analysis was used to determine the best prognostic variable and construct a nomogram. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the discrimination capability and clinical effectiveness of the nomogram.
The prognosis of patients receiving CPM and UM was significantly different. The DCA curves indicated that the nomogram could provide more excellent clinical net benefits for these patients. The NRI and IDI of the nomogram demonstrated that its performance was better than that of the classical tumor-node-metastasis (TNM) staging system.
This study developed and validated a practical nomogram to predict the OS of UBC patients undergoing CPM, which provided a beneficial tool for clinical decision-making management.
目前,关于接受对侧预防性乳房切除术(CPM)的单侧乳腺癌(UBC)患者的预后因素的研究有限。本研究旨在构建一个新的列线图来预测这些患者的总生存(OS)。
在这项回顾性研究中,从监测、流行病学和结果数据库中选择了 88477 名接受 CPM 或单侧乳房切除术(UM)的患者。Kaplan-Meier 曲线和 Cox 回归分析用于确定两种手术方法对预后的影响差异。多变量 Cox 分析用于确定最佳预后变量并构建列线图。一致性指数(C 指数)、接收者操作特征(ROC)曲线、校准曲线、决策曲线分析(DCA)、净重新分类改善(NRI)和综合判别改善(IDI)用于评估列线图的判别能力和临床效果。
接受 CPM 和 UM 的患者的预后明显不同。DCA 曲线表明,该列线图可以为这些患者提供更优的临床净效益。列线图的 NRI 和 IDI 表明其性能优于经典的肿瘤-淋巴结-转移(TNM)分期系统。
本研究开发并验证了一个实用的列线图,用于预测接受 CPM 的 UBC 患者的 OS,为临床决策管理提供了有益的工具。