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Updated: May 9, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Modelo de predicción de recurrencia tardía a distancia en mujeres premenopáusicas con cáncer de mama
Dong Seung Shin1, Janghee Lee2, Eunhye Kang3
1Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Republic of Korea.
Background:
Late distant recurrence (DR) remains a significant challenge in estrogen receptor (ER)-positive/Human Epidermal Growth Factor Receptor 2 (HER2)-negative breast cancer, especially in premenopausal patients. This study aimed to develop a machine-learning model predicting late DR risk in premenopausal patients and to assess the clinical benefit of extended endocrine therapy (ET) according to risk stratification.
Methods:
This retrospective multicenter study included patients aged ≤45 years with ER-positive/HER2-negative breast cancer who underwent surgery between January 2000 and December 2011. This study was designed as a landmark analysis, with the effective baseline set at 5 years after surgery. Eligible patients had five to 10 years of follow-up and received adjuvant ET for at least two years. The primary outcome was late DR, defined as distant metastasis occurring between five and 10 years after surgery.
Results:
Among 1701 included patients (median age, 41 years), late DR occurred in 108 patients (6.3%). A machine-learning model using eight clinicopathologic variables demonstrated strong predictive performance (AUC = 0.78). Patients classified as high-risk by the model exhibited significantly worse late DMFS compared to low-risk patients (HR,7.36; 95% CI,4.43-12.20; P<0.001). Among high-risk patients, those who received extended ET had significantly improved late DMFS compared to those who did not (HR,0.32; 95% CI,0.18-0.55; P < 0.001). In low-risk patients, extended ET was not associated with a statistically significant benefit (HR,0.45; 95% CI,0.16-1.22; P = 0.081).
Conclusion:
The machine-learning model effectively stratified patients into distinct DR risk groups and highlighted the benefit of extended ET in high-risk patients. This model supports tailored decision-making regarding extended ET in premenopausal patients with ER-positive/HER2-negative breast cancer.
