title: Korean risk assessment model for breast cancer risk prediction.
abstract: OBJECTIVE
  We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk.
  
  
  METHODS
  Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort.
  
  
  RESULTS
  The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (p<0.001 and <0.001, respectively). The observed incidence of breast cancer in the two cohorts was similar to the expected incidence from the KoBCRAT (KMCC, p = 0.880; NCC, p = 0.878). The AUC using the KoBCRAT was 0.61 for the KMCC and 0.89 for the NCC cohort.
  
  
  CONCLUSIONS
  Our findings suggest that the KoBCRAT is a better tool for predicting the risk of breast cancer in Korean women, especially urban women.
authors.affiliation: National Cancer Control Institute, National Cancer Center, Goyang, Korea.
authors.forename: Boyoung
authors.initials: B
authors.lastname: Park
authors.forename: Seung Hyun
authors.initials: SH
authors.lastname: Ma
authors.forename: Aesun
authors.initials: A
authors.lastname: Shin
authors.forename: Myung-Chul
authors.initials: MC
authors.lastname: Chang
authors.forename: Ji-Yeob
authors.initials: JY
authors.lastname: Choi
authors.forename: Sungwan
authors.initials: S
authors.lastname: Kim
authors.forename: Wonshik
authors.initials: W
authors.lastname: Han
authors.forename: Dong-Young
authors.initials: DY
authors.lastname: Noh
authors.forename: Sei-Hyun
authors.initials: SH
authors.lastname: Ahn
authors.forename: Daehee
authors.initials: D
authors.lastname: Kang
authors.forename: Keun-Young
authors.initials: KY
authors.lastname: Yoo
authors.forename: Sue K
authors.initials: SK
authors.lastname: Park
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000369:Aged, 80 and over; D044466:Asian People / Q000706:statistics & numerical data; D001943:Breast Neoplasms / Q000453:epidemiology* / Q000208:ethnology; D016022:Case-Control Studies; D015331:Cohort Studies; D005260:Female; D006801:Humans; D015994:Incidence; D008875:Middle Aged; D015233:Models, Statistical; D056910:Republic of Korea / Q000453:epidemiology; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data*; D012307:Risk Factors; D055815:Young Adult