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Which nomogram is best for predicting non-sentinel lymph node metastasis in breast cancer patients? A meta-analysis

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An Erratum to this article was published on 17 April 2013

Abstract

To present a systemic review and meta-analysis to evaluate the nomograms developed to predict non-sentinel lymph node (NSLN) metastasis in breast cancer patients. We focused on the six nomograms (Cambridge, MSKCC, Mayo, MDA, Tenon, and Stanford) that are the most widely validated. The AUCs were converted to odds ratios for the meta-analysis. In total, the Cambridge, Mayo, MDA, MSKCC, Stanford, and Tenon models were validated in 2,156, 2,431, 843, 8,143, 3,700, and 3,648 patients, respectively. The pooled AUCs for the Cambridge, MDA, MSKCC, Mayo, Tenon, and Stanford models were 0.721, 0.706, 0.715, 0.728, 0.720, and 0.688, respectively. Subgroup analysis revealed that in populations with a higher micrometastasis rate in the SLNs, the Tenon and Stanford models had a significantly higher predictive accuracy. A meta-regression analysis revealed that the SLN micrometastasis rate, but not the NSLN-positivity rate, was associated with improved predictive accuracy in the Tenon and Stanford models. The performance of the MSKCC and Cambridge models was not influenced by these two factors. All of these prediction models perform better than random chance. The Stanford model seems to be relatively inferior to the other models. The accuracy of the Tenon and Stanford models is influenced by the tumor burden in the SLNs.

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Acknowledgments

We thank Dr. Zhaopeng Cai for extensive discussion of this research. This study was supported by the National Natural Science Foundation of China (Grant 81172524/H1622).

Conflict of interest

The authors have no conflict of interest to disclose.

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Authors and Affiliations

  1. Department of Breast Surgery, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China

    Liling Zhu, Liang Jin, Shunrong Li, Kai Chen, Weijuan Jia, Quanyuan Shan, Erwei Song & Fengxi Su

  2. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

    Stephen Walter

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  1. Liling Zhu
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  7. Stephen Walter
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Correspondence to Erwei Song or Fengxi Su.

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Liling Zhu, Liang Jin, and Shunrong Li contributed equally to this study.

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Zhu, L., Jin, L., Li, S. et al. Which nomogram is best for predicting non-sentinel lymph node metastasis in breast cancer patients? A meta-analysis. Breast Cancer Res Treat 137, 783–795 (2013). https://doi.org/10.1007/s10549-012-2360-6

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