Development and Validation of Nomograms for Predicting Overall and Breast Cancer-Specific Survival in Young Women with Breast Cancer: A Population-Based Study

Overview

Journal Transl Oncol

Specialty Oncology

Date 2018 Sep 7

PMID 30189361

Citations 10

Authors

Yue Gong

Peng Ji

Wei Sun

Yi-Zhou Jiang

Xin Hu

Zhi-Ming Shao

Affiliations

Soon will be listed here.

Abstract

Introduction: The objective of current study was to develop and validate comprehensive nomograms for predicting the survival of young women with breast cancer.

Methods: Women aged <40 years diagnosed with invasive breast cancer between 1990 and 2010 were selected from the Surveillance, Epidemiology, and End Results database and randomly divided into training (n = 12,465) and validation (n = 12,424) cohorts. A competing-risks model was used to estimate the probability of breast cancer-specific survival (BCSS). We identified and integrated significant prognostic factors for overall survival (OS) and BCSS to construct nomograms. The performance of the nomograms was assessed with respect to calibration, discrimination, and risk group stratification.

Results: The entire cohort comprised 24,889 patients. The 5- and 10-year probabilities of breast cancer-specific mortality were 11.6% and 20.5%, respectively. Eight independent prognostic factors for both OS and BCSS were identified and integrated for the construction of the nomograms. The calibration curves showed optimal agreement between the predicted and observed probabilities. The C-indexes of the nomograms in the training cohort were higher than those of the TNM staging system for predicting OS (0.724 vs 0.694; P < .001) and BCSS (0.733 vs 0.702; P < .001). Additionally, significant differences in survival were observed in patients stratified into different risk groups within respective TNM categories.

Conclusions: We developed and validated novel nomograms that can accurately predict OS and BCSS in young women with breast cancer. These nomograms may help clinicians in making decisions on an individualized basis.

Citing Articles

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