Predicting survival in women with breast cancer and brain metastasis

A nomogram outperforms current survival prediction models

Authors

  • Nicholas F. Marko MD,

    Corresponding author
    1. Department of Neurosurgery, Cleveland Clinic, Cleveland, Ohio
    • Nicholas F. Marko, Cancer Research UK Cambridge Research Institute and Department of Applied Mathematics and Theoretical Physics, Cambridge University, United Kingdom

      Robert J. Weil, Brain Tumor and Neuro-Oncology Center, The Neurological Institute, Cleveland Clinic, Mail Desk: ND4-40 LRI, 9500 Euclid Avenue, Cleveland, OH 44195

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    • Telephone: +44 1223 404231

  • Zhiyuan Xu MD,

    1. Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, Ohio
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  • Tianming Gao MS,

    1. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio
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  • Michael W. Kattan PhD,

    1. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio
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  • Robert J. Weil MD

    Corresponding author
    1. Department of Neurosurgery, Cleveland Clinic, Cleveland, Ohio
    2. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio
    • Nicholas F. Marko, Cancer Research UK Cambridge Research Institute and Department of Applied Mathematics and Theoretical Physics, Cambridge University, United Kingdom

      Robert J. Weil, Brain Tumor and Neuro-Oncology Center, The Neurological Institute, Cleveland Clinic, Mail Desk: ND4-40 LRI, 9500 Euclid Avenue, Cleveland, OH 44195

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Abstract

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.

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