A high-quality model for predicting the prognosis of breast neuroendocrine carcinoma to help clinicians decide on appropriate treatment methods: A population-based analysis

BACKGROUND: Breast neuroendocrine carcinoma (NEC) is a rare malignancy with unclear treatment options and prognoses. This study aimed to construct a high-quality model to predict overall survival (OS) and breast cancer-specific survival (BCSS) and help clinicians choose appropriate breast NEC treatments. PATIENTS AND METHODS: A total of 378 patients with breast NEC and 349,736 patients with breast invasive ductal carcinoma (IDC) were enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2018. Propensity score matching (PSM) was performed to balance the clinical baseline. Prognostic factors determined by multivariate Cox analysis were included in the nomogram. C-index and calibration curves were used to verify the performance of the nomogram. RESULTS: Nomograms were constructed for the breast NEC and breast IDC groups after PSM. The C–index of the nomograms ranged from 0.834 to 0.880 in the internal validation and 0.818–0.876 in the external validation, indicating that the nomogram had good discrimination. The risk stratification system showed that patients with breast NEC had worse prognoses than those with breast IDC in the low-risk and intermediate-risk groups but had a similar prognosis that those in the high-risk group. Moreover, patients with breast NEC may have a better prognosis when undergoing surgery plus chemotherapy than when undergoing surgery alone or chemotherapy alone. CONCLUSIONS: We established nomograms with a risk stratification system to predict OS and BCSS in patients with breast NEC. This model could help clinicians evaluate prognosis and provide individualized treatment recommendations for patients with breast NEC.