4.5 Article

International multicenter tool to predict the risk of four or more tumor-positive axillary lymph nodes in breast cancer patients with sentinel node macrometastases

Journal

BREAST CANCER RESEARCH AND TREATMENT
Volume 138, Issue 3, Pages 817-827

Publisher

SPRINGER
DOI: 10.1007/s10549-013-2468-3

Keywords

Breast cancer; Sentinel node biopsy; Axillary lymph node dissection; Tumor staging

Categories

Funding

  1. Helsinki University Central Hospital Research Fund
  2. Academy of Finland
  3. Orion-Farmos Research Foundation
  4. Emil Aaltonen's Foundation

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Recently, many centers have omitted routine axillary lymph node dissection (ALND) after metastatic sentinel node biopsy in breast cancer due to a growing body of literature. However, existing guidelines of adjuvant treatment planning are strongly based on axillary nodal stage. In this study, we aim to develop a novel international multicenter predictive tool to estimate a patient-specific risk of having four or more tumor-positive axillary lymph nodes (ALN) in patients with macrometastatic sentinel node(s) (SN). A series of 675 patients with macrometastatic SN and completion ALND from five European centers were analyzed by logistic regression analysis. A multivariate predictive model was created and validated internally by 367 additional patients and then externally by 760 additional patients from eight different centers. All statistical tests were two-sided. Prevalence of four or more tumor-positive ALN in each center's series (P = 0.010), number of metastatic SNs (P < 0.0001), number of negative SNs (P = 0.003), histological size of the primary tumor (P = 0.020), and extra-capsular extension of SN metastasis (P < 0.0001) were included in the predictive model. The model's area under the receiver operating characteristics curve was 0.766 in the internal validation and 0.774 in external validation. Our novel international multicenter-based predictive tool reliably estimates the risk of four or more axillary metastases after identifying macrometastatic SN(s) in breast cancer. Our tool performs well in internal and external validation, but needs to be further validated in each center before application to clinical use.

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