AUTHOR=Cui Xiang , Song Deba , Li Xiaoxu 

TITLE=Construction and Validation of Nomograms Predicting Survival in Triple-Negative Breast Cancer Patients of Childbearing Age

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.636549

DOI=10.3389/fonc.2020.636549

ISSN=2234-943X

ABSTRACT=Background: Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer with poorest clinical outcomes, and patients of childbearing age have highest odds of TNBC diagnosis with more demands on maintenance and restoration of physical and psychosocial function. This study aimed to design effective and comprehensive nomograms to better predict survival in these patients.

Methods: TNBC patients aged between 18 and 45 years, diagnosed between 2010 and 2015, were selected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly classified into the training (n=2,296) and validation (n=2,297) cohorts. Cox proportional hazard models and competing-risk models were used to identify prognostic factors in the nomograms for overall survival (OS) and breast cancer-specific survival (BCSS) in the training cohort. Significant factors were integrated to build nomogram, which was validated using multiple tools in both cohorts.

Results: A total of 4,593 TNBC patients of childbearing age were enrolled. Four prognostic factors of both OS and BCSS, and two additional prognostic factors of BCSS were integrated to formulate the nomograms. Calibration curves showed excellent agreement between nomogram-predicted and actual survival data. The areas under the curves (AUCs) in time-dependent receiver operating characteristic (ROC) curves, as well as concordance index (C-index) values, of the nomograms were relatively high for both training and validation cohorts.

Conclusions: Independent prognostic factors were identified, and used to develop nomograms to predict survival in childbearing-age patients with TNBC. These models could assist clinicians in quantifying the risk of overall mortality and breast cancer-specific mortality and selecting appropriate treatment regimens.