Construction and validation of prognostic nomogram for metaplastic breast cancer

In this study, we aimed to develop nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with metaplastic breast carcinoma (MBC). Data of patients diagnosed with MBC from 1973 to 2015 were collected from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox analyses were performed to identify independent prognostic factors for OS and CSS of MBC patients. The obtained prognostic variables were combined to construct nomogram models for predicting OS and CSS in patients with MBC. Model performance was evaluated using concordance index (C-index) and calibration plots. Data from 1125 patients were collected and divided into a training (750) and a validation (375) cohort. The multivariate Cox model identified age, TNM stage, tumor size, and radiotherapy as independent covariates associated with OS and CSS. The nomogram constructed based on these covariates demon-strated excellent accuracy in estimating 3-, and 5-year OS and CSS, with a C-index of 0.769 (95% confidence interval [CI], 0.731-0.808) for OS and 0.761 (95% CI, 0.713-0.809) for CSS in the training cohort. In the validation cohort, the nomogram-predicted C-index was 0.738 (95% CI, 0.676-0.800) for OS and 0.747 (95% CI, 0.667-0.827) for CSS. All calibration curves exhibited good consistency between predicted and actual survival. The nomogram models established in this study may enhance the accuracy of prognosis prediction and therefore may improve indi-vidualized assessment of survival risks and enable constructive therapeutic suggestions.

Automated summary

This study aimed to develop nomogram models for predicting overall survival and cancer-specific survival of patients with metaplastic breast carcinoma (MBC). The study collected data from 1973 to 2015 and used Cox analyses and calibration plots to evaluate model performance. The results showed that the nomogram models had good accuracy in predicting patient survival.