Massachusetts Institute of Technology
Professor, Electrical Engineering and Computer Science
School of Engineering Distinguished Professor for AI and Health
Faculty Co-Leader, Jameel Clinic-MIT Initiative in Machine Learning and Health
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. Barzilay, Yala, 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. Developing MIRAI involved utilizing a large dataset of mammographic information from Massachusetts General Hospital (MGH). It was tested across diverse patient data accumulated from seven hospitals in the United States and other countries. In this retrospective testing, the model was able to reliably identify high-risk patients, outperforming existing methods by a large margin. 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.
Drs. Barzilay and Yala’s research thus far suggests that MIRAI-based screening can offer broad, cost-effective, individualized, and equitable improvements in breast cancer risk prediction compared to costlier methods such as MRI. Their current work will be conducted at UMass Memorial Hospital (UMH) which serves a racially and socio-economically diverse group of patients—this will enable testing of MIRAI across these different subgroups. And it will be the first large prospective trial of MIRAI-based risk assessment technology in a diverse clinical setting.
The team will continue to recruit participants and anticipates a total patient accrual of 20,000. Ultimately, the results of the study could lead to redefining breast cancer screening guidelines and yield novel clinical protocols for managing high-risk patients.
Regina Barzilay, PhD is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science as well as a member of the Computer Science and Artificial Intelligence Laboratory at MIT. She is the recipient of various awards, including the National Science Foundation Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship, and several Best Paper Awards from the North American Chapter of the Association of Computational Linguistics (ACL). In 2017, she received a MacArthur fellowship, an ACL fellowship, and an Association for the Advancement of Artificial Intelligence fellowship. In 2021, she was awarded the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, the AACC Wallace H. Coulter Lectureship Award, and the UNESCO/Netexplo Award. In 2022, Dr. Barzilay was elected to the American Academy of Arts and Sciences. She received her undergraduate degree from Ben-Gurion University of the Negev, Israel, a PhD in Computer Science from Columbia University, and spent a year as a postdoctoral fellow at Cornell University.
Her research interests are in natural language processing and applications of deep learning to chemistry and oncology. She is a member of the Learning to Cure Initiative at MIT which utilizes data collected from millions of cancer patients—their pathology slides, imaging, and other tests—to address many open questions in oncology. Jointly with the MGH collaborators, the team is developing algorithms that can learn from this data to improve models of disease progression, prevent over-treatment, and potentially home in on a cure.
The AI Screening Project supported by Zeta Tau Alpha Foundation
University of California, Berkeley
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