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Predicting survival in women with breast cancer and brain metastasis
Author(s) -
Marko Nicholas F.,
Xu Zhiyuan,
Gao Tianming,
Kattan Michael W.,
Weil Robert J.
Publication year - 2011
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/cncr.26716
Subject(s) - nomogram , medicine , proportional hazards model , breast cancer , oncology , recursive partitioning , concordance , population , survival analysis , bootstrapping (finance) , relative survival , cancer , cancer registry , econometrics , environmental health , economics
BACKGROUND: Brain metastases (BMs) are a common occurrence in patients with breast cancer, and accurately predicting survival in these patients is critical to appropriate management. A survival nomogram for breast cancer patients with BM was constructed, and its performance is compared to current predictive models of survival. METHODS: A Cox proportional hazards regression with a nomogram representation was used to model survival in a population of 261 women with breast cancer and BMs treated from 1999 to 2008. The model was validated internally by 10‐fold cross‐validation and bootstrapping, and concordance (c) indices were calculated. The predictive performance of the nomogram described here is compared to current prognostic models, including recursive partitioning analysis, graded prognostic assessment, and diagnosis‐specific graded prognostic assessment. RESULTS: The c‐index for the model described here was 0.67. It outperformed recursive partitioning analysis, graded prognostic assessment, and diagnosis‐specific graded prognostic assessment, based on c‐index comparisons. CONCLUSIONS: The nomogram described here outperformed current strategies for survival prediction in breast cancer patients with BMs. Two additional advantages of this nomogram are its ability to predict individualized, 1‐, 3‐, and 5‐year survival for novel patients and its straightforward representations of the relative effects of each of 9 covariates on neurologic survival. This represents a potentially valuable alternative to current models of survival prediction in this patient population. Cancer 2012. © 2011 American Cancer Society.