Construction and validation of nomogram for the cancer-specific mortality for HER2-positive breast cancer patients.

Abstract

The cancer-specific mortality (CSM) of patients with human epidermal growth factor receptor 2 positive (HER2+) breast cancer remains dismal and varies widely from person to person. Therefore, we aim to construct a nomogram to predict CSM in HER2+ breast cancer using data from the surveillance, epidemiology, and end results (SEER) database. The clinicopathological data of patients diagnosed with HER2+ breast cancer from 2000 to 2019 were selected from the SEER database. Independent prognostic factors for CSM of patients were identified by competing risk model. Subsequently, we constructed a new predicting nomogram. Calibration curves, receiver operating characteristic curve, and decision curve were used to evaluate the efficiency of the nomogram. A total of 45,362 breast cancer patients in the SEER database were selected for study and randomly separated into training (n = 31,753) and validation (n = 13,609) cohorts. Univariate and multivariate analysis showed that age, race, tumor grade, clinical stage, T stage, surgery status, radiotherapy, chemotherapy, and regional nodes examined were independent risk factors for CSM of HER2+ breast cancer patients. Receiver operating characteristic curves for the prediction nomogram of the CSM for breast cancer patients indicated that the 1-, 3- and 5-year AUCs were 0.874, 0.843, and 0.820 in the training cohort and 0.861, 0.845, and 0.825 in the validation cohort, respectively. The c-index was 0.817 and 0.821 in training cohort and validation cohort, respectively. Moreover, a good agreement was seen between the observed outcome and the predicted probabilities in the calibration curves of the nomogram in training cohort and validation cohort. Further decision curve analysis demonstrated good clinical utilities of the nomogram in training cohort and validation cohort. The nomogram shows good accuracy and reliability in predicting the CSM of breast cancer patients, and it could provide some theoretical support for clinicians to make decisions.