Establishment and verification of a nomogram for predicting survival in patients with triple-positive breast cancer.

Background: Triple-positive breast cancer (TPBC) is a specific type of breast cancer characterized by the positive expression of estrogen receptor (ER)/progesterone receptor (PR)/human epidermal growth factor receptor 2 (HER-2). In recent years, the research on breast cancer has been increasing year by year, but there are few studies on TPBC, especially the lack of analysis with large sample size. In this study, sufficient samples were provided through the SEER database, explore the factors affecting the prognosis of TPBC, and construct a prediction model, in order to assess the individual survival of patients, and help clinicians accurately identify high-risk patients and develop personalized treatment plans. Methods: Patients pathologically diagnosed with TPBC were recruited from Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation groups (7:3 ratio). Univariate analysis was used to analyze the related factors affecting the prognosis of TPBC patients in the modeling group, and then multivariate Cox proportional hazards model was used to analyze the significant factors to screen out the independent risk factors affecting the 3- and 5-year overall survival (OS) rate and construct the prediction model. Using the concordance index (C-index) and calibration curve were performed to evaluate the predictive ability of the model. Results: The results of the Cox risk-scale model showed that race, age, marital status, tumor grade, tumor, node, metastasis stage, surgical treatment, chemotherapy, and radiotherapy affected the prognosis of TPBC patients (P<0.05) in the training group, and the factors were used to construct a nomogram. The internal and external validation of the nomogram chart indicated that the C-index of the training group was 0.85 [95% confidence interval (CI): 0.836, 0.863] and that of the verification group was 0.833 (95% CI: 0.807, 0.858). The calibration curves of the 2 groups showed that the OS predicted by the model was consistent with the actual survival of the patients. Conclusions: The prediction model accurately predicted the prognosis of and identified high-risk TPBC patients.