Risk factors and survival prediction of young breast cancer patients with liver metastases: a population-based study

BACKGROUND: The risk and prognosis of young breast cancer (YBC) with liver metastases (YBCLM) remain unclear. Thus, this study aimed to determine the risk and prognostic factors in these patients and construct predictive nomogram models. METHODS: This population-based retrospective study was conducted using data of YBCLM patients from the Surveillance, Epidemiology, and End Results database between 2010 and 2019. Multivariate logistic and Cox regression analyses were used to identify independent risk and prognostic factors, which were used to construct the diagnostic and prognostic nomograms. The concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the performances of the established nomogram models. Propensity score matching (PSM) analysis was used to balance the baseline characteristics between the YBCLM patients and non-young patients with BCLM when comparing overall survival (OS) and cancer-specific survival (CSS). RESULTS: A total of 18,275 YBC were identified, of whom 400 had LM. T stage, N stage, molecular subtypes, and bone, lung, and brain metastases were independent risk factors for LM developing in YBC. The established diagnostic nomogram showed that bone metastases contributed the most risk of LM developing, with a C-index of 0.895 (95% confidence interval 0.877–0.913) for this nomogram model. YBCLM had better survival than non-young patients with BCLM in unmatched and matched cohorts after propensity score matching analysis. The multivariate Cox analysis demonstrated that molecular subtypes, surgery and bone, lung, and brain metastases were independently associated with OS and CSS, chemotherapy was an independent prognostic factor for OS, and marital status and T stage were independent prognostic factors for CSS. The C-indices for the OS- and CSS-specific nomograms were 0.728 (0.69–0.766) and 0.74 (0.696–0.778), respectively. The ROC analysis indicated that these models had excellent discriminatory power. The calibration curve also showed that the observed results were consistent with the predicted results. DCA showed that the developed nomogram models would be effective in clinical practice. CONCLUSION: The present study determined the risk and prognostic factors of YBCLM and further developed nomograms that can be used to effectively identify high-risk patients and predict survival outcomes.