Ying Zhao1,2, Ping Xiong1, Lauren E McCullough3,4, Erline E Miller3, Hui Li1, Yuan Huang1,5, Min Zhao1, Meng-Jie Wang1, Min Kang6, Qiong Wang1,7, Jia-Yuan Li1. 1. 1 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, People's Republic of China . 2. 5 Department of Discipline Construction, West China Hospital, Sichuan University , Chengdu, People's Republic of China . 3. 2 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina. 4. 3 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia . 5. 4 London School of Hygiene and Tropical Medicine, London, United Kingdom . 6. 6 The Comprehensive Guidance Center of Women's Health, Women's and Children's Hospital of Sichuan Province, Chengdu, People's Republic of China . 7. 7 School of Public Health, Sun Yat-Sen University, Guangzhou, People's Republic of China .
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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%). CONCLUSION: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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%). CONCLUSION: 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.
Entities:
Keywords:
artificial neural network; breast cancer; mammography; risk assessment; screening