【摘 要】
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BACKGROUND:Brain metastasis (BM) is common in patients with breast cancer.Predicting patient survival is critical for the clinical management of breast cancer brain metastasis (BCBM).The present study
【机 构】
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Department of Radiation Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beiji
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BACKGROUND:Brain metastasis (BM) is common in patients with breast cancer.Predicting patient survival is critical for the clinical management of breast cancer brain metastasis (BCBM).The present study was designed to develop and evaluate a prognostic model to facilitate decision-making for patients with newly diagnosed brain metastasis from breast cancer.METHODS:Based on the clinical data of BCBM patients treated in our hospital from 2002 to 2014, a nomogram was developed to predict survival using proportional hazards regression analysis.The model was validated internally by bootstrapping, and the concordance index (c-index) was calculated.We used a calibration curve and c-index to evaluate discriminatory and predictive ability, in order to compare the nomogram with widely used models,including recursive partitioning analysis (RPA), graded prognostic assessment (GPA), and breast-graded prognostic assessment (Breast-GPA).RESULTS:A total of 411 BCBM patients were included in the development of this predictive model.Median overall survival was 14.1 months.Statistically significant predictors for patient survival included biologic subtype,Karnofsky performance score (KPS), leptomeningeal metastasis, extracranial metastasis, the number of brain metastases, and disease-free survival (DFS).A nomogram for predicting 1-and 2-year overall survival was constructed, which exhibited good accuracy in predicting overall survival with a concordance index of 0.735.This model outperformed RPA, GPA and Breast-GPA, based on the comparisons of the c-indexes.CONCLUSION:The nomogram constructed based on a multiple factor analysis could more accurately predict the individual survival probability of patients with BCBM, compared with existing models.
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