# Independent Risk Factors and a New Nomogram for Predicting Breast Cancer Risk for Bone Metastasis in Chinese Women: A Retrospective Study with External Validation

## TL;DR

This study identifies key risk factors for bone metastasis in breast cancer and creates a predictive model to help doctors better manage patient care.

## Contribution

The study introduces a new nomogram for predicting bone metastasis in breast cancer patients based on independent risk factors identified in a Chinese cohort.

## Key findings

- Histological grade, PR negativity, HER-2 negativity, and visceral metastasis are independent predictors of bone metastasis in breast cancer.
- The nomogram model achieved an AUC of 0.720 in the training set and 0.701 in external validation.
- The model's clinical utility was confirmed through decision curve analysis.

## Abstract

Background/Objectives: Bone is the most common organ affected by distant metastasis in advanced breast cancer, and the development of skeletal-related events (SREs) often leads to significant deterioration in patients’ quality of life and survival outcomes. In this study, we aimed to explore the risk factors associated with bone metastasis in breast cancer and to develop a predictive nomogram for identifying high-risk patients, which may facilitate timely preventive interventions and improve clinical prognosis. Methods: A retrospective analysis was conducted on 672 patients with breast cancer who underwent surgery at the Fourth Hospital of Hebei Medical University (Shijiazhuang, China) between 2013 and 2023; this cohort served as the training set. Clinical and pathological characteristics potentially influencing bone metastasis—including age, menopausal status, histological grade, affected side, maximum tumor diameter, lymph node staging, TNM staging, ER status, PR status, HER-2 status, Ki-67, molecular subtypes, vascular tumor thrombus, nerve infiltration and visceral metastasis—were collected. The median follow-up time was 42 months. Patients were stratified into two cohorts based on whether postoperative bone metastasis occurred, with groups matched according to Tumor–Node–Metastasis (TNM) stage. Univariate and multivariate logistic regression models were applied to identify independent factors associated with breast cancer bone metastasis, and a nomogram prediction model was constructed using the variables retained in the final analysis. For external validation, data from 2814 patients with breast cancer who underwent surgery between 2013 and 2021 were extracted from the U.S. Surveillance, Epidemiology, and End Results database. Results: The multivariate logistic regression analysis revealed that histological grade (p = 0.002), progesterone receptor (PR) negativity (p = 0.001), human epidermal growth factor receptor 2 (HER-2) negativity (p = 0.002) and visceral metastasis (p < 0.001) were identified as independent predictors of bone metastasis in breast cancer. A nomogram predictive model was established using these four factors. The area under the receiver operating characteristic curve was 0.720 (95% confidence interval (CI): 0.6797–0.7607) for the training cohort and 0.701 (95% CI: 0.6813–0.7205) for the external validation cohort. Decision curve analysis further confirmed the clinical applicability of the model. Conclusions: The present study confirms that histological grade, PR status, HER-2 status and visceral metastasis are independent factors associated with bone metastasis in breast cancer. The constructed nomogram may effectively predict breast cancer-related bone metastasis and could serve as a practical tool for clinical decision-making.