title: Comparison of Breast Cancer Risk Predictive Models and Screening Strategies for Chinese Women.
abstract: Previous studies have shown that organized mammographic screening implementation in China may not be cost-effective. Our aim was to develop a valid predictive mathematical model for selecting high-risk groups eligible for mammography examinations (MAMs) and cost-effective strategies for breast cancer screening among Chinese women.
  
  
  
  Between 2009 and 2012, 13,355 eligible women aged 30-65 years were enrolled from the community in Chengdu City. All subjects were administered a valid questionnaire and given MAMs. Using biopsies and 1-year follow up, we compared the accuracy indexes of three predictive models (back-propagation artificial neural network [BP-ANN], logistic regression [LR], and Gail) and four serial screening strategies (BP-ANN→MAM, LR→MAM, Gail→MAM, and MAM alone). We also evaluated the benefits of the four strategies by comparing their incidence-adjusted positive predictive value (PPV). All analyses were conducted with three age-based subgroups: 30-39, 40-49, and 50-65.
  
  
  
  The BP-ANN1, in conjunction with additional continuous risk factor variables, was the best predictive model, with the highest sensitivity (SEN, 76.99%) and specificity (SPE, 54.20%). The BP-ANN1→MAM strategy was best for the 40-49 age group, with the highest adjusted PPV (9.80%) and reasonable SEN (81.82%).
  
  
  
  We found that the BP-ANN model performed the best and was the most accurate for predicting high risk for breast cancer among Chinese women, and the BP-ANN→MAM screening strategy was most effective among the 40-49 age group. However, mammography alone may be a sufficient screening strategy for women aged 50-65.
authors.affiliation: 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China .|5 Department of Discipline Construction, West China Hospital, Sichuan University , Chengdu, People's Republic of China .
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China .
authors.forename: Ping
authors.initials: P
authors.lastname: Xiong
authors.affiliation: 2 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina.|3 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia .
authors.forename: Lauren E
authors.initials: LE
authors.lastname: McCullough
authors.affiliation: 2 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina.
authors.forename: Erline E
authors.initials: EE
authors.lastname: Miller
authors.affiliation: 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China .
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.affiliation: 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China .|4 London School of Hygiene and Tropical Medicine, London, United Kingdom .
authors.forename: Yuan
authors.initials: Y
authors.lastname: Huang
authors.affiliation: 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China .
authors.forename: Min
authors.initials: M
authors.lastname: Zhao
authors.affiliation: 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China .
authors.forename: Meng-Jie
authors.initials: MJ
authors.lastname: Wang
authors.affiliation: 6 The Comprehensive Guidance Center of Women's Health, Women's and Children's Hospital of Sichuan Province, Chengdu, People's Republic of China .
authors.forename: Min
authors.initials: M
authors.lastname: Kang
authors.affiliation: 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China .|7 School of Public Health, Sun Yat-Sen University, Guangzhou, People's Republic of China .
authors.forename: Qiong
authors.initials: Q
authors.lastname: Wang
authors.affiliation: 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China .
authors.forename: Jia-Yuan
authors.initials: JY
authors.lastname: Li
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D015992:Body Mass Index; D001943:Breast Neoplasms / Q000175:diagnosis*; D002681:China; D003362:Cost-Benefit Analysis; D003430:Cross-Sectional Studies; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography / Q000706:statistics & numerical data*; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D008962:Models, Theoretical; D017584:Reproductive History; D012307:Risk Factors
keywords: artificial neural network; breast cancer; mammography; risk assessment; screening