ArticleFrontiers in surgery2025
Development and validation of a nomogram for predicting bone metastasis in breast cancer: a retrospective study.
Article in Frontiers in surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Bone metastasis is the most common site of distant metastasis in breast cancer. Patients with bone metastasis have their quality of life and survival rate threatened. This study aims to develop a practical nomogram for predicting the risk of bone metastasis in breast cancer by integrating clinical data, assisting doctors in making more scientific clinical decisions. Methods: We conducted a retrospective analysis of the data of newly diagnosed breast cancer patients from the database of the Affiliated Hospital of Qingdao University from January 2015 to December 2017. The cohort is divided into training set and validation set in a ratio of 7.5:2.5. Determine independent risk factors through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and logistic regression, and develop a nomogram prediction model. The model's performance and clinical utility were evaluated by Receiver Operating Characteristic (ROC) curve analysis, Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA). Results: During the 5-year follow-up period, bone metastases developed in 48 of 421 patients (11.40%). Ultimately, six independent risk factors were identified: neoadjuvant chemotherapy, family history of cancer, distant metastasis in other locations, axillary lymph node metastasis, marital status, and primary tumor site. The nomogram demonstrated excellent predictive performance, with AUC values of 0.89 and 0.86 in the training and validation cohorts, respectively. Conclusions: This pioneering nomogram, incorporating baseline, tumor characteristics, and therapeutic parameters, provides visual guidance for breast surgeons to assess bone metastasis risk in breast cancer patients. It enables clinicians to prioritize high-risk patients through early identification, thereby optimizing surveillance protocols and therapeutic strategies to safeguard patients' quality of life.
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