Dr. Jennifer Sauler, a diagnostic radiologist and assistant professor of radiology at UMass Chan Medical School, is investigating the use of artificial intelligence-driven risk prediction models to enhance early breast cancer detection, particularly for women with dense breasts. Her work aims to personalize screening recommendations beyond current 'one-size-fits-all' guidelines. Dr. Sauler will present her findings at a 'Science for Living' event on Thursday, February 13, 2025, at UMass Chan.
The research focuses on an AI tool that calculates an individual woman's personalized risk score for breast cancer by analyzing demographics, family history, and mammography features. This risk score is then used to guide recommendations for supplemental screening methods, such as ultrasound or MRI, which can identify cancers earlier than mammography alone, especially in dense breast tissue. This approach addresses the challenge of missed cancers in women with dense breasts, which can obscure tumors on mammograms.
The ultimate goal of this research is to advance personalized breast cancer screening, leading to earlier cancer detection, fewer unnecessary biopsies, and improved patient outcomes. Dr. Sauler's efforts are supported by a grant from the American Cancer Society. Additionally, research fellow Dr. Lauren Hennessy is conducting related work, also supported by the Friends of the Cancer Center and the American Cancer Society, and will present her own findings on Thursday, March 13, 2025, further contributing to this field at UMass Chan.
