title: A head to head comparison of nine tools predicting non-sentinel lymph node status in sentinel node positive breast cancer women.
abstract: OBJECTIVE
  This study was conducted to evaluate the performance of available tools predicting non-sentinel lymph node (non-SLN) status in women with SLN positive breast cancer and to see if they can be safely used in everyday clinical practice.
  
  
  METHODS
  Data of 220 women with breast cancer who underwent a SLN biopsy at the Máxima Medical Centre between 2000-2008 were analysed. Tools evaluated were: the models from Memorial Sloan Kettering Cancer Centre, Stanford, Mayo, Cambridge, Gur, and MOU, and the scores from Saidi, Tenon, and MDA. Model performance was assessed using calibration, discrimination and Nagelkerke's explained variation.
  
  
  RESULTS
  The MSKCC nomogram showed best overall performance with best discrimination (AUC 0.69), second best calibration, and highest explained variation (31%). The 10% low risk threshold led to defining only 22% (38/176) of the women as being low risk while in fact 66% (116/176) were non-SLN negative. The false negative rate was 13% (5/38).
  
  
  CONCLUSIONS
  Current models for predicting non-SLN metastases in SLN positive breast cancer are not yet ready for implementation in general practice. Further research efforts should improve model performance in selecting patients or perhaps find a role in support in the paradigm shift to a "treat none unless" approach.
authors.affiliation: Department of Surgery, Máxima Medical Center, Veldhoven, The Netherlands.
authors.forename: Ingrid
authors.initials: I
authors.lastname: van den Hoven
authors.affiliation: Department of Surgery, Máxima Medical Center, Veldhoven, The Netherlands.
authors.forename: Gert
authors.initials: G
authors.lastname: Kuijt
authors.affiliation: Department of Surgery, Máxima Medical Center, Veldhoven, The Netherlands.|Maastricht University Medical Center, GROW-school for Oncology and Developmental Biology, Maastricht, The Netherlands.
authors.forename: Rudi
authors.initials: R
authors.lastname: Roumen
authors.affiliation: Maastricht University Medical Center, GROW-school for Oncology and Developmental Biology, Maastricht, The Netherlands.|Department of Research, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands.
authors.forename: Adri
authors.initials: A
authors.lastname: Voogd
authors.affiliation: Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.affiliation: Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.
authors.forename: Yvonne
authors.initials: Y
authors.lastname: Vergouwe
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001365:Axilla; D001943:Breast Neoplasms / Q000473:pathology* / Q000601:surgery*; D018270:Carcinoma, Ductal, Breast / Q000473:pathology / Q000601:surgery; D018275:Carcinoma, Lobular / Q000473:pathology / Q000601:surgery; D005260:Female; D006801:Humans; D008197:Lymph Node Excision; D008198:Lymph Nodes / Q000473:pathology* / Q000601:surgery; D008207:Lymphatic Metastasis; D008875:Middle Aged; D060787:Neoplasm Grading; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D049451:Nomograms; D011237:Predictive Value of Tests; D021701:Sentinel Lymph Node Biopsy
keywords: axillary lymph node dissection; breast cancer; nomogram; predictive system; sentinel lymph node biopsy