Work

A nomogram for predicting subsequent liver metastasis in patients with metastatic breast cancer

Year: 2025

Type: article

Abstract: Background: 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. Methods: 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. Results: 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. Conclusion: 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.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2025.1417858

Authors: Xuanchen Liu, Weipeng Zhao, Yongsheng Jia, Li Zhang, Zhongsheng Tong

Institutions: Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University

Language: English

Topic: Breast Cancer Treatment Studies

Subfield: Cancer Research

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801