The clinical features and prognosis of patients with mucinous breast carcinoma compared with those with infiltrating ductal carcinoma: a population-based study

Abstract

Abstract Background At present, the characteristics of mucinous breast carcinoma (MBC) and the factors affecting its prognosis are controversial. We compared the clinical features of MBC with those of infiltrating ductal carcinoma (IDC) and summarized the relevant prognostic factors. Methods The Surveillance, Epidemiology, and End Results (SEER) database includes information on 10,593 patients diagnosed with MBC between 2004 and 2016. Chi-square tests and analyses were used to analyze differences in variables between the MBC and IDC groups. Univariate and multivariate Cox proportional hazards models were used to assess the relative impacts of risk factors on cancer-specific survival (CSS) in patients. Kaplan-Meier survival curves were constructed to assess cancer-specific mortality and were compared using the log-rank test. Results From 2004 to 2016, 10,593 people were diagnosed with MBC, and 402,797 were diagnosed with IDC. Patients with MBC had significantly higher 5−/10-year CSS rates (96.4%/93.4%) than those with IDC (89%/83.8%). Compared with IDC patients, MBC patients had less lymph node metastasis, an earlier stage, a higher rate of hormone receptor positivity and a lower expression rate of HER2. Univariate and multivariate analyses showed that age ≥ 60 years old (HR = 1.574, 95%CI: 1.238–2.001, P < 0.001), singled status (HR = 1.676, 95%CI: 1.330–2.112, P < 0.001) and advanced TNM/SEER stage were independent prognostic risk factors for MBC. In addition, positive estrogen receptor (HR = 0.577, 95%CI: 0.334–0.997, P = 0.049), positive progesterone receptor (HR = 0.740, 95%CI: 0.552–0.992, P = 0.044), surgical treatment (HR = 0.395, 95%CI: 0.288–0.542, P < 0.001) and radiotherapy (HR = 0.589, 95%CI: 0.459–0.756, P < 0.001) were identified as protective factors. Conclusion Compared with IDC, MBC has a better prognosis. For patients with MBC, we identified prognostic factors that can help clinicians better assess patient outcomes and guide individualized treatment.