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Updated: Jul 24, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting rapid relapse in triple-negative breast cancer patients
Tao Ma1,2,3,4,5, Xin-Yu Liu1,2,3,4,5, Shuang-Long Cai6
1The Third Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Background:
Triple-negative breast cancer (TNBC) accounts for disproportionately poor outcomes in breast cancer, driven by a subset of rapid-relapse TNBC (rrTNBC) with marked chemoresistance, rapid metastatic spread, and poor survival. This study aimed to develop and validate a nomogram based on clinicopathological characteristics to predict rapid relapse in TNBC patients treated with neoadjuvant chemotherapy (NAC) first.
Methods:
The clinicopathological data of 504 TNBC patients treated with NAC first in Tianjin Medical University Cancer Hospital were analyzed retrospectively, with 109 rapid relapsed patients, and 395 non-rapid relapsed patients, respectively. Based on clinicopathologic characteristics, and follow-up data were analyzed. The independent predictors of clinicopathological characteristics were identified by logistic regression analysis and then used to build a nomogram. The concordance index (C-index), the area under the curve (AUC) of receiver operating characteristic (ROC), and calibration plots were used to evaluate the performance of the model.
Results:
Univariate and multivariate logistic regression analyses showed that age at diagnosis (age≥50 years, OR = 0.325,95% CI:0.137-0.771), Nodal staging (N3 staging, OR = 13.669,95% CI:3.693-50.592),sTIL expression levels (sTIL intermediate expression, OR = 0.272,95% CI:0.109-0.678; sTIL high expression, OR = 0.169,95% CI:0.048-0.594), and NAC response (ORR, OR = 0.059,95% CI:0.024-0.143) were independent predictors of rapid relapse in TNBC patients treated with NAC firstly. Among these independent predictors, age ≥ 50 years, sTIL intermediate expression, sTIL high expression, and ORR in NAC were independent protective factors for rapid relapse in TNBC NAC patients. N3 staging was an independent risk factor for rapid relapse in TNBC NAC patients. The ROC curve, calibration curve, and decision curve analysis were used to validate the model. The C-Index of the training sets and validation sets were 0.938 and 0.910, respectively. The Brier scores of the training sets and validation sets were 0.076 and 0.097, respectively.
Conclusion:
This study developed and verified a nomogram for predicting rapid relapse in TNBC NAC patients, and the predictive model had high discrimination and accuracy.
