Models for Reducing the Risk of Breast Cancer Related Mortality
Models for Reducing the Risk of Breast Cancer Related Mortality
20.02.2018 11:00
YER : AB4 4001 Meeting Room
​​Models for Reducing the Risk of Breast Cancer Related Mortality ​

20 February 2018, Tuesday
11.00- 12.00
AB4 4001 Meeting Room
Industrial Engineering

Mehmet Ali Ergun
Mehmet Ergun recently completed his Ph.D. at the Department of Industrial and Systems Engineering at the University of Wisconsin – Madison. He received a BS degree in Computer Engineering from Bogazici University and an MS degree in Computer Science from Masdar Institute of Science and Technology in Abu Dhabi, UAE. His research interests include healthcare analytics, stochastic modeling, and optimization, optimal policymaking in healthcare and reinforcement learning. His dissertation focused on determining the optimal risk-reduction policies for patients with a high risk of breast cancer. He was also a major contributor to the National Cancer Institute-funded Cancer Intervention and Surveillance Modeling Network (CISNET) project that was used to develop the breast cancer screening guidelines in the U.S.

Breast cancer is the most common non-skin cancer and a leading cause of death for women. Depending on the personal and hereditary factors, each woman has a different risk of developing a breast cancer and the focus of recent studies has been on identifying the high-risk patients and reducing the mortality due to breast cancer. For high-risk women, breast cancer mortality can be prevented by 1) regular screening with latest imaging techniques such as mammography and MRI; 2) preventative treatments such as hormonal therapy (i.e. Tamoxifen, Raloxifene, Exemestane); or 3) a risk reduction surgery (i.e. Bilateral Prophylactic Mastectomy). In this talk, I will describe several models used for developing breast cancer prevention strategies to (1) optimize risk reduction strategies for individual women at high risk of breast cancer and (2) set national screening guidelines for the U.S. female population via collaborative modeling within National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network.