Construction and Validation of a Prognostic Model for the Assessment of Postoperative Overall Survival of Patients with Metaplastic Breast Cancer: Based on a Retrospective Large Data Analysis and Chinese Multicenter Study

Abstract Purpose: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods: Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans. Abstract Background: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods： Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans.