University of California, Berkeley Berkeley, California
Assistant Professor, Computational Precision Health Joint Assistant Professor, University of California, San Francisco
Developing targeted screening strategies to improve breast cancer risk prediction.
Risk prediction models can inform the use of targeted screening strategies to achieve earlier detection of breast cancer. To improve existing risk prediction models, Drs. Yala, Barzilay and their colleagues have developed a mammography-based deep learning model called MIRAI. This model was designed to predict an individual’s risk of developing breast cancer by analyzing multiple mammography timepoints and leveraging potentially missing risk factor information. In addition, MIRAI has the potential to produce breast cancer risk predictions that are consistent across mammography machines. The goal of the current study is to test MIRAI in a prospective study, where patients predicted to be high-risk by this model are followed by MRI screening to assess its accuracy. In this way, this comprehensive study stands to redefine guidelines, generate personalized screening regimes and, ultimately, yield novel clinical protocols for optimizing breast cancer management for high-risk patients.
Drs. Yala and Barzilay’s project began in the summer of 2023 at UMass Memorial Hospital (UMH) which serves a racially and socio-economically diverse group of patients. It is the first large prospective trial of MIRAI-based risk assessment technology in a diverse clinical setting. To date, they have accrued over 7,400 patients and identified 372 high-risk candidates based on MIRAI results. Initial analysis showed that MIRAI has a cancer detection rate of 33 percent while other reports have described a cancer detection rate of 6.4 percent using breast density for prediction.
The team will continue to recruit participants and anticipates a total patient accrual of 20,000. They expect to identify an additional 1,500 high-risk patients by the end of 2024 and will follow them by MRI to confirm MIRAI’s predictive ability. In the next few years, Drs. Yala and Barzilay plan to add a new intervention and a new site to their trial. Specifically, they will test the impact of leveraging MIRAI to select patients for Contrast-Enhanced Mammography (CEM), a promising low-cost alternative to MRI. This additional trial arm will allow a significant expansion of AI-driven screening for better risk prediction, identifying truly high-risk patients who can benefit from additional diagnostic technologies.
Adam Yala, PhD is an assistant professor of Computational Precision Health and Electrical Engineering and Computer Sciences at UC Berkeley and UCSF. His research focuses on developing machine learning methods for precision health and translating them to clinical care. His previous research has contributed to three areas: 1) predicting future cancer risk, 2) designing personalized screening policies, and 3) learning encoding schemes for private data sharing. Dr. Yala’s tools have been deployed at multiple health systems around the world and his research has been featured in the Washington Post, New York Times, Boston Globe, and Wired. He obtained his PhD in Computer Science from MIT, and subsequently became a member of MIT’s Jameel Clinic and Computer Science & Artificial Intelligence Laboratory.
2022
The AI Screening Project supported by Zeta Tau Alpha Foundation
Massachusetts Institute of Technology Cambridge, Massachusetts
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