Translational cancer research · 2026
A LASSO-based nomogram for predicting breast cancer-specific survival in elderly patients after surgery: a large retrospective cohort study.
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Wang Ziqiang, Zhang Haojie, Li Qianqian
Аннотация
BACKGROUND: Accurate prognostic assessment for elderly patients with breast cancer remains challenging, as the conventional tumor-node-metastasis (TNM) staging system is limited in capturing individual heterogeneity. This study aimed to develop and internally validate a least absolute shrinkage and selection operator (LASSO)-Cox-based nomogram for predicting breast cancer-specific survival (BCSS) in elderly patients.
METHODS: A total of 69,137 eligible patients diagnosed between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (70%) and validation (30%) cohorts. LASSO regression was applied for variable selection, followed by multivariable Cox proportional hazards modeling to construct a nomogram. Model performance was evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare predictive accuracy with the TNM staging system.
RESULTS: 11 variables were identified by LASSO selection and entered into the multivariable Cox model, including surgery, age, tumor grade, TNM stage, radiotherapy, hormone receptor status, primary site and marital status. The nomogram demonstrated acceptable discrimination (training C-index: 0.807) and satisfactory calibration. DCA suggested a higher net clinical benefit of the nomogram compared with TNM staging across a range of threshold probabilities.
CONCLUSIONS: This LASSO-Cox-based nomogram provides a prognostic model for estimating BCSS in elderly patients and may assist postoperative risk stratification in elderly patients.
METHODS: A total of 69,137 eligible patients diagnosed between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (70%) and validation (30%) cohorts. LASSO regression was applied for variable selection, followed by multivariable Cox proportional hazards modeling to construct a nomogram. Model performance was evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare predictive accuracy with the TNM staging system.
RESULTS: 11 variables were identified by LASSO selection and entered into the multivariable Cox model, including surgery, age, tumor grade, TNM stage, radiotherapy, hormone receptor status, primary site and marital status. The nomogram demonstrated acceptable discrimination (training C-index: 0.807) and satisfactory calibration. DCA suggested a higher net clinical benefit of the nomogram compared with TNM staging across a range of threshold probabilities.
CONCLUSIONS: This LASSO-Cox-based nomogram provides a prognostic model for estimating BCSS in elderly patients and may assist postoperative risk stratification in elderly patients.