Development of prognostic models for HER2-positive metastatic breast cancer in females: a retrospective population-based study

构建HER2阳性转移性乳腺癌女性患者的预后模型:一项基于人群的回顾性研究

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Abstract

BACKGROUND: This study aimed to construct, evaluate, and validate nomograms for breast cancer-specific survival (BCSS) and overall survival (OS) prediction in patients with HER2- overexpressing (HER2+) metastatic breast cancer (MBC). METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to select female patients diagnosed with HER2 + MBC between 2010 and 2015. These patients were distributed into training and validation groups (7:3 ratio). Variables were screened using univariate and multivariate Cox regression analyses, and BCSS and OS nomograms were constructed to determine one-, three-, and five-year survival probabilities. The nomograms were evaluated and validated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Stratification was evaluated using Kaplan-Meier curves and log-rank tests based on optimal total score cut-off values. We published web-based versions of these nomograms for clinical use. RESULTS: A total of 2,151 eligible patients were randomized into training (n = 1,505) and validation (n = 646) groups. Independent prognostic factors of BCSS and OS included: age; marital status; race; oestrogen receptor status; surgery; chemotherapy; and bone, brain, liver, and lung metastases. The C-indices for the BCSS and OS training groups were 0.707 and 0.702, respectively. The ROC, calibration, and decision curves demonstrated the strength of the nomograms. According to cut-off values, patients were categorized into low-, intermediate-, and high-risk groups, with significant differences in survival outcomes between them. CONCLUSION: We constructed predictive nomograms and stratified risk to assess the prognosis of patients with HER2 + MBC, which could help inform therapeutic decisions. TRIAL REGISTRATION: Not applicable.

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