title: PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.
abstract: BACKGROUND
  The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK.
  
  
  METHODS
  Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation.
  
  
  RESULTS
  Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75).
  
  
  CONCLUSIONS
  We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.
authors.affiliation: Cambridge Breast Unit, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 2QQ, UK. gordon.wishart@addenbrookes.nhs.uk
authors.forename: Gordon C
authors.initials: GC
authors.lastname: Wishart
authors.forename: Elizabeth M
authors.initials: EM
authors.lastname: Azzato
authors.forename: David C
authors.initials: DC
authors.lastname: Greenberg
authors.forename: Jem
authors.initials: J
authors.lastname: Rashbass
authors.forename: Olive
authors.initials: O
authors.lastname: Kearins
authors.forename: Gill
authors.initials: G
authors.lastname: Lawrence
authors.forename: Carlos
authors.initials: C
authors.lastname: Caldas
authors.forename: Paul D P
authors.initials: PD
authors.lastname: Pharoah
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000453:epidemiology / Q000401:mortality* / Q000601:surgery; D005260:Female; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009361:Neoplasm Invasiveness; D011379:Prognosis; D011960:Receptors, Estrogen / Q000032:analysis; D012042:Registries; D018426:SEER Program; D006113:United Kingdom
chemical_list: D011960:Receptors, Estrogen