Prognostic factors for hormone receptor-positive breast cancer with liver metastasis and establishment of novel nomograms for prediction: a SEER-based study.

BACKGROUND

The prognosis of patients with hormone receptor (HR)-positive breast cancer with liver metastasis (BCLM) remains dismal and varies widely from person to person. Thus, we sought to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) in patients with HR-positive BCLM using data from the Surveillance, Epidemiology and End Results (SEER) database.

METHODS

The data of patients with BCLM, who had received HR-positive diagnoses between 2010 and 2016, were collected from the SEER database. A Cox proportional hazards model was used to evaluate and identify the independent risk factors for OS and BCSS. Subsequently, two new nomograms were developed. Finally, the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) results were evaluated.

RESULTS

The data of 1,780 patients diagnosed between 2010 and 2015 were used to build the nomogram models. Using both univariate and multivariate Cox regression analyses, nine variables, including age, marital status, grade, human epidermal growth factor receptor 2 (HER2) status, chemotherapy, surgery, bone metastasis, lung metastasis, and brain metastasis, were found to be significantly associated with OS. Conversely, 10 variables, including age, marital status, T stage, grade, HER2 status, chemotherapy, surgery, bone metastasis, lung metastasis, and brain metastasis, were identified as independent risk factors for BCSS. Using the risk factors listed above, we created 1-, 2-, and 3-year survival nomograms for OS and BCSS, respectively. Subsequently, the data of 312 patients, who had been diagnosed in 2016, were used for the external validation. These results, including the ROC curve, calibration curve, and DCA results, showed that our nomogram had strong predictive power.

CONCLUSIONS

Nomograms can effectively and reliably predict a patient's prognosis and could be useful in clinical decision making. The nomograms had strong discrimination, calibration, and clinical values. More aggressive treatment and closer monitoring should be considered when treating high-risk individuals.