Survival analysis and establishment of prognostic nomograms for primary breast signet ring cell carcinoma - Chang Cai; Tongshun Liu; Jing Zhao; Kedong Zhang (Scientific Reports)
Abstract Primary breast signet ring cell carcinoma (BSRCC) is an extremely rare malignancy with poor prognosis. Few consensus exists regarding the prognostic factors and treatment modalities. This study aimed to develop a nomogram model to predict survival probability and guide clinical treatment for BSRCC patients. Clinicopathological data of BSRCC patients were retrieved from SEER database. Univariate and multivariate Cox regression analyses were performed to screen and identify prognostic factors. Kaplan–Meier method was used to describe the survival curve for each prognostic factor. Additionally, these factors were incorporated to construct nomograms for predicting overall survival (OS) and disease-specific survival (DSS) of BSRCC patients. The nomograms were internally validated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). A total of 362 eligible BSRCC patients were included in this study. Multivariate Cox analysis demonstrated that age at diagnosis, T stage, N stage, and surgery were identified as independent prognostic factors for OS, while grade, T stage, N stage, ER status, and surgery were independent DSS-related factors. Our study elucidated that surgery is the effective treatment for BSRCC, while postoperative radiotherapy does not confer additional benefit to patients. Nomograms were established to predict OS and DSS probability by incorporating independent prognostic factors among BSRCC patients. The nomograms were subsequently validated using ROC curves, calibration curves, and DCA to display the robust prognostic capability. Robust nomograms for OS and DSS of BSRCC patients were established, facilitating precise personalized risk assessment and appropriate treatment regimens in clinical practice.