Abstract A29: Mathematical models are not the be-all and end-all for breast cancer risk assessment

Conclusions: Our institutional high-risk database includes women who are at high risk based on well-established risk factors for developing breast cancer (FH, BRCA mutations, AH, LCIS). Current mathematical models including the Gail and Tyrer-Cuzick models did not capture the increased risk of breast cancer in 8% of our population. While the models are helpful, in clinical practice they are not necessarily the be-all and end-all. Using heuristic risk factors is more time efficient and comprehensive risk assessment allows the clinicians and patients to better understand risk. Identifying patients as high risk and enrolling them in a high-risk database and program allow us to capture long term follow up, recommend surveillance for early detection, and better understand the effectiveness of different risk reduction and management strategies for this population.Citation Format: Freya Schnabel, Jennifer Chun, Shira Schwartz, Amber Guth, Deborah Axelrod, Richard Shapiro, Karen Hiotis, Julia Smith. Mathematical models are not the be-all and end-all for breast cancer risk assessment. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A29.

Source: Cancer Epidemiology Biomarkers and Prevention - April 30, 2017 Category: Cancer & Oncology Authors: Schnabel, F., Chun, J., Schwartz, S., Guth, A., Axelrod, D., Shapiro, R., Hiotis, K., Smith, J. Tags: Improving Cancer Risk Prediction for Prevention and Early Detection: Poster Presentations - Proffered Abstracts Source Type: research