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Which prediction models best identify additional axillary disease after a positive sentinel node biopsy for breast cancer?

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Abstract

To determine which web-based model best identifies women at low risk of further axillary disease after a positive sentinel lymph node (SLN+) biopsy. 673 women with T1-2cN0M0 SNB+ breast cancer who underwent completion axillary dissection (AxD) were identified. A subgroup not eligible to avoid AxD as part of the Z0011 study was defined (Z0011 exclusion group). Predicted risk of further axillary disease was generated using seven web-based models. “Low risk” was defined as a ≤10% risk of further axillary disease. False negative (“low risk” prediction but AxD+) rates (FNRs), area under the receiver operating characteristic curve (AUC), and Brier score were determined for each model. 6 of 7 models identified “low risk” patients but FNRs ranged from 14 to 30%. The Stanford and Memorial Sloan-Kettering (MSKCC) models had the best FNRs. FNRs were lower with SLN micrometastasis (7–15%) and higher in the Z0011 exclusion group (21–41%). All models under-predicted further nodal disease in low risk patients and over-predicted in higher-risk patients. The Stanford and MSKCC models were able to identify women with SLN micrometastasis with a ≤10% FNR. Models were not able to accurately identify low risk women from a cohort that would have been excluded from Z0011.

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Acknowledgments

The authors thank Dr. Stefanie Jeffrey, Stanford School of Medicine, for providing model estimates and coefficients used to assess the Stanford nomogram for this validation.

Funding

This study was supported by a grant from the Canadian Breast Cancer Foundation-BC/Yukon Chapter.

Conflicts of interest

The authors declare no conflicts of interest related to this work.

Author information

Authors and Affiliations

  1. British Columbia Cancer Agency—Vancouver Island Centre, Radiation Therapy Program, 2410 Lee Avenue, Victoria, BC, V8R 6V5, Canada

    Tanya S. Berrang, Pauline T. Truong & Ivo A. Olivotto

  2. University of British Columbia, Victoria, BC, Canada

    Tanya S. Berrang, Pauline T. Truong, Allen H. Hayashi & Ivo A. Olivotto

  3. Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada

    Mary Lesperance

  4. Department of Microbiology, University of Victoria, Victoria, BC, Canada

    Caroline Walter

  5. Department of Surgery, Vancouver Island Health Authority, Victoria, BC, Canada

    Allen H. Hayashi

Authors
  1. Tanya S. Berrang
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Correspondence to Tanya S. Berrang.

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Berrang, T.S., Lesperance, M., Truong, P.T. et al. Which prediction models best identify additional axillary disease after a positive sentinel node biopsy for breast cancer?. Breast Cancer Res Treat 133, 695–702 (2012). https://doi.org/10.1007/s10549-012-1991-y

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  • DOI: https://doi.org/10.1007/s10549-012-1991-y

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