title: Prediction of axillary lymph node involvement of women with invasive breast carcinoma: a multivariate analysis.
abstract: BACKGROUND
  The increasing use of systemic therapy for women with lymph node negative breast carcinoma and earlier stage of disease at mammographic detection raises questions regarding the need for routine axillary lymph node dissection. Predictive modeling for lymph node involvement may be one way to reduce the need for axillary lymph node dissection and its morbidity.
  
  
  METHODS
  A multivariate analysis of 12 factors predictive of axillary lymph node involvement was conducted in a population-based cohort of 4312 women with invasive breast carcinoma diagnosed between January 1, 1993 and December 31, 1996.
  
  
  RESULTS
  Clinical palpability, lymphatic or vascular invasion, lesion size, margin status, histology, and patient age were independent predictors of axillary lymph node involvement. The model correctly identified lymph node status in 76.6% of cases. Model accuracy and fit were equally high when applied to randomly selected halves of the study subjects. Approximately 32.0% of the patients in the study sample (1363/4312) were identified as having an extremely high (91%; n = 1102) or low (10%; n = 261) risk of lymph node involvement. In a second analysis, a clinically useable, three-variable model identified a very low risk group of patients (n = 147) with a 4.8% risk of lymph node metastasis and a high risk group of patients (n = 1008) with a 74.2% risk of lymph node metastasis. Greater than 90% of subjects in the high risk group received adjuvant systemic therapy even if they were lymph node negative pathologically.
  
  
  CONCLUSIONS
  A clinically useable, three-variable model employing tumor and lymph node palpability, size, and lymphatic or vascular invasion can identify women with invasive breast carcinoma in whom axillary lymph node dissection is very unlikely to alter recommendations regarding adjuvant systemic therapy.
authors.affiliation: Breast Cancer Outcomes Unit, British Columbia Cancer Agency, Vancouver, Canada.
authors.forename: I A
authors.initials: IA
authors.lastname: Olivotto
authors.forename: J S
authors.initials: JS
authors.lastname: Jackson
authors.forename: D
authors.initials: D
authors.lastname: Mates
authors.forename: S
authors.initials: S
authors.lastname: Andersen
authors.forename: W
authors.initials: W
authors.lastname: Davidson
authors.forename: C J
authors.initials: CJ
authors.lastname: Bryce
authors.forename: J
authors.initials: J
authors.lastname: Ragaz
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000704:Analysis of Variance; D001365:Axilla; D001943:Breast Neoplasms / Q000473:pathology* / Q000628:therapy; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis / Q000473:pathology; D008875:Middle Aged; D008962:Models, Theoretical; D015999:Multivariate Analysis; D012044:Regression Analysis