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Updated: Sep 21, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Survival Nomogram for Young Breast Cancer Patients Based on the SEER Database and an External Validation Cohort
Xiao Huang1, Zhou Luo2, Wei Liang3
1Clinical Medical College, Yangzhou University, Yangzhou, Jiangsu Province, China.
Background:
Young breast cancer (YBC) patients are more prone to lymph node metastasis than other age groups. Our study aimed to investigate the predictive value of lymph node ratio (LNR) in YBC patients and create a nomogram to predict overall survival (OS), thus helping clinical diagnosis and treatment.
Methods:
Patients diagnosed with YBC between January 2010 and December 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into a training set and an internal validation set with a ratio of 7:3. An independent cohort from our hospital was used for external validation. Univariate and least absolute shrinkage and selection operator (LASSO) regression were used to identify the significant factors associated with prognosis, which were used to create a nomogram for predicting 3- and 5-year OS.
Results:
We selected seven survival predictors (tumor grade, T-stage, N-stage, LNR, ER status, PR status, HER2 status) for nomogram construction. The C-indexes in the training set, the internal validation set, and the external validation set were 0.775, 0.778 and 0.817, respectively. The nomogram model was well calibrated, and the time-dependent ROC curves verified the superiority of our model for clinical usefulness. In addition, the nomogram classification could more precisely differentiate risk subgroups and improve the discrimination of YBC prognosis.
Conclusions:
LNR is a strong predictor of OS in YBC patients. The novel nomogram based on LNR is a reliable tool to predict survival, which may assist clinicians in identifying high-risk patients and devising individual treatments.
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