title: Predicting nonsentinel lymph node metastasis using lymphoscintigraphy in patients with breast cancer.
abstract: Several models for predicting the likelihood of nonsentinel lymph node (NSLN) metastasis using histopathologic parameters in sentinel-positive breast cancer patients have been proposed. In this study, we established a new model that uses sentinel lymphoscintigraphic findings and histopathologic parameters as covariates and assessed its predictive performance.
  
  
  METHODS
  The analysis included breast cancer patients (n = 301 women) who underwent sentinel lymphoscintigraphy (SLS) using (99m)Tc-labeled human serum albumin, had sentinel lymph node biopsy results positive for metastasis, and subsequently underwent complete axillary lymph node dissection. First, we devised a grading system relating SLS patterns to the risk of NSLN metastasis positivity. Second, we developed a multivariate logistic regression model for the prediction of NSLN metastasis using the SLS pattern and histopathologic parameters as covariates and compared its performance with that of the extensively validated Memorial Sloan-Kettering Cancer Center model using receiver-operating-characteristic curve analysis.
  
  
  RESULTS
  The SLS visual grade was strongly correlated with the presence of NSLN metastases. A well-calibrated prediction model for NSLN metastasis was constructed using SLS grade and histopathologic findings. The mean area under the curve of our model was 0.812, which is significantly greater than that of the Memorial Sloan-Kettering Cancer Center model (P < 0.001). A nomogram was drawn to facilitate the application of our model.
  
  
  CONCLUSIONS
  SLS can aid in predicting NSLN metastasis in patients with breast cancer. Our model performed better than did established prediction models.
authors.affiliation: Department of Nuclear Medicine and Center for Breast Cancer, Goyang, Korea.
authors.forename: Hyo Sang
authors.initials: HS
authors.lastname: Lee
authors.forename: Seok Won
authors.initials: SW
authors.lastname: Kim
authors.forename: Byoung-Hee
authors.initials: BH
authors.lastname: Kim
authors.forename: So-Youn
authors.initials: SY
authors.lastname: Jung
authors.forename: Seeyoun
authors.initials: S
authors.lastname: Lee
authors.forename: Tae-Sung
authors.initials: TS
authors.lastname: Kim
authors.forename: Youngmi
authors.initials: Y
authors.lastname: Kwon
authors.forename: Eun Sook
authors.initials: ES
authors.lastname: Lee
authors.forename: Han-Sung
authors.initials: HS
authors.lastname: Kang
authors.forename: Seok-ki
authors.initials: SK
authors.lastname: Kim
mesh_terms: D000704:Analysis of Variance; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008207:Lymphatic Metastasis; D061305:Lymphoscintigraphy; D008875:Middle Aged; D060787:Neoplasm Grading; D015203:Reproducibility of Results; D012189:Retrospective Studies