Liu Xuanchen, Zhao Weipeng, Jia Yongsheng, Zhang Li, Tong Zhongsheng
Department of Breast Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China.
Front Oncol. 2025 Apr 16;15:1417858. doi: 10.3389/fonc.2025.1417858. eCollection 2025.
To investigate the clinical characteristics of liver metastasis from metastatic breast cancer and construct a competing risk nomogram for predicting the probability of liver metastasis.
Clinical data of patients with metastatic breast cancer from Tianjin Medical University Cancer Institute during 2008-2018 were retrospectively collected. Independent prognostic factors were assessed by the Fine-Gray competing risk model. A competing risk nomogram was constructed by integrating those independent prognostic factors and evaluated with concordance index (C-index) and calibration curves.
A total of 1406 patients were retrospectively analyzed, and randomly divided into the training set (n=986) and the validation set (n=420). Multivariate analysis showed that menopausal status, HER-2 status, bone metastasis and lung metastasis were identified as independent prognostic factors in the nomogram. The C-index in the training set was 0.719 (95% CI: 0.706-0.732), and in the validation set was 0.740 (95% CI: 0.720-0.732). The calibration curves in the training set and validation set showed that the nomogram had a sufficient level of calibration. A risk stratification was further established to divide all the patients into three prognostic groups.
We had developed a tool that can predict subsequent liver metastasis from metastatic breast cancer, which may be useful for identifying the patients at risk of liver metastasis and guiding the individualized treatment. It had been verified that the nomogram has good discrimination and calibration, and had certain potential clinical value. This nomogram can be used to screen patients with low, intermediate and high risk of liver metastasis from metastatic breast cancer, so as to develop a more complete follow-up plan.
探讨转移性乳腺癌肝转移的临床特征,并构建竞争风险列线图以预测肝转移概率。
回顾性收集2008年至2018年天津医科大学肿瘤研究所转移性乳腺癌患者的临床资料。采用Fine-Gray竞争风险模型评估独立预后因素。通过整合这些独立预后因素构建竞争风险列线图,并使用一致性指数(C指数)和校准曲线进行评估。
共回顾性分析了1406例患者,并随机分为训练集(n = 986)和验证集(n = 420)。多因素分析显示,绝经状态、HER-2状态、骨转移和肺转移被确定为列线图中的独立预后因素。训练集的C指数为0.719(95%CI:0.706 - 0.732),验证集的C指数为0.740(95%CI:0.720 - 0.732)。训练集和验证集的校准曲线表明列线图具有足够的校准水平。进一步建立风险分层,将所有患者分为三个预后组。
我们开发了一种可预测转移性乳腺癌后续肝转移的工具,这可能有助于识别有肝转移风险的患者并指导个体化治疗。已证实该列线图具有良好区分度和校准度,具有一定潜在临床价值。此列线图可用于筛选转移性乳腺癌肝转移低、中、高风险患者,从而制定更完善的随访计划。