title: A simple index to predict prognosis independent of axillary node information in breast cancer.
abstract: BACKGROUND
  Since the course of breast cancer is often unpredictable, we wished to develop a model using characteristics of the primary tumour alone to predict prognosis.
  
  
  METHODS
  Several tumour features were determined, and after a median follow-up duration of 65 months, multivariate analysis identified tumour size and grade, oestrogen receptor concentration, axillary lymph node metastasis and tumour cell proliferation fraction (MIB-1 count) as being independently associated with increases in risk for both relapse and death from breast cancer. A prognostic model was constructed using tumour size and grade, oestrogen receptor concentration and MIB-1 count only. A score of 1 for each was given to tumour size > 20 mm, tumour grade 2 or 3, oestrogen receptor concentration < 10 fmol/mg cytosol protein and MIB-1 count > 9%. Five groups established by assigning a combined score of 0, 1, 2, 3 or 4 for each patient were analysed for their associations with disease-free and overall survivals.
  
  
  RESULTS
  This preliminary model predicted 5-year survival rates of 97, 91, 85, 68 and 50% for the five groups. The model was further simplified by excluding tumour grade from the analysis. The revised model identified four risk groups with predicted 5-year survival rates of 91, 86, 66 and 52%. This model, the Adelaide prognostic index, was also able to identify four risk groups in both node-negative and node-positive patients.
  
  
  CONCLUSIONS
  The Adelaide prognostic index can be used to predict prognosis even in the absence of axillary lymph node information.
authors.affiliation: Department of Haematology, Flinders University School of Medicine, Bedford Park, South Australia, Australia.
authors.forename: R
authors.initials: R
authors.lastname: Seshadri
authors.forename: D J
authors.initials: DJ
authors.lastname: Horsfall
authors.forename: K
authors.initials: K
authors.lastname: McCaul
authors.forename: A S
authors.initials: AS
authors.lastname: Leong
mesh_terms: D034961:Antigens, Nuclear; D001365:Axilla; D014408:Biomarkers, Tumor / Q000378:metabolism; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism; D018572:Disease-Free Survival; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D019394:Ki-67 Antigen; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D009687:Nuclear Proteins / Q000378:metabolism; D011379:Prognosis; D011960:Receptors, Estrogen / Q000378:metabolism; D012307:Risk Factors; D015996:Survival Rate
chemical_list: D034961:Antigens, Nuclear; D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen; D009687:Nuclear Proteins; D011960:Receptors, Estrogen