Breast Cancer Research and Treatment

, Volume 137, Issue 3, pp 783–795 | Cite as

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

  • Liling Zhu
  • Liang Jin
  • Shunrong Li
  • Kai Chen
  • Weijuan Jia
  • Quanyuan Shan
  • Stephen Walter
  • Erwei Song
  • Fengxi Su
Clinical Trial

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.

Keywords

Meta-analysis Non-sentinel lymph node Nomogram Prediction 

Supplementary material

10549_2012_2360_MOESM1_ESM.xlsx (27 kb)
Supplementary material 1 (XLSX 28 kb)
10549_2012_2360_MOESM2_ESM.docx (5.4 mb)
Supplementary material 2 (DOCX 5576 kb)

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Liling Zhu
    • 1
  • Liang Jin
    • 1
  • Shunrong Li
    • 1
  • Kai Chen
    • 1
  • Weijuan Jia
    • 1
  • Quanyuan Shan
    • 1
  • Stephen Walter
    • 2
  • Erwei Song
    • 1
  • Fengxi Su
    • 1
  1. 1.Department of Breast Surgery, Breast Tumor Center, Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhouChina
  2. 2.Department of Clinical Epidemiology and BiostatisticsMcMaster UniversityHamiltonCanada

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