Seeing into the future: Machine learning for personalized cancer screening

Seminar | March 15 | 4-5 p.m. | 1011 Evans Hall

Speaker/Performer:  Adam Yala, UC Berkeley and UCSF

Sponsor:  Department of Statistics

For multiple diseases, early detection significantly improves patient outcomes. This motivates considerable investments in population-wide screening programs, such as mammography for breast cancer and low-dose CT for lung cancer. To be effective and economically viable, these programs must find the right balance between early detection and overscreening. This capacity builds on two complementary technologies: (1) the ability to accurately assess patient risk at a given time point and (2) the ability to design screening regimens based on this risk. Moreover, these tools must obtain consistent performance across diverse populations and adapt to new clinical requirements while learning from limited datasets. In this talk, I’ll discuss approaches to address these challenges in both areas, namely cancer risk assessment from imaging and personalized screening policy design. I’ve demonstrated that these clinical models offer significant improvements over the current standard of care across globally diverse patient populations, and our image-based tools now underlie prospective trials.

Event Contact:  zhivotovskiy@berkeley.edu, 5102292370

Access Coordinator:  Nikita Zhivotovskiy,  zhivotovskiy@berkeley.edu,  510-229-2370