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“Advances in measurement technologies, data science and AI have the potential to fundamentally transform cancer research and care for the benefit of our patients,” says Srinivasan Yegnasubramanian, M.D., Ph.D., professor of oncology at the Sidney Kimmel Comprehensive Cancer Center and director of the inHealth Precision Medicine program at Johns Hopkins Medicine. “To fully realize that potential, we must bring together interdisciplinary teams across many domains. Likewise, developing the large-scale, comprehensive and representative datasets that can fuel this AI-enabled transformation will be facilitated by bringing together the leading cancer centers partnering in this unique alliance.”
Alexis Battle, Ph.D., professor of biomedical engineering, interim co-director of the Data Science and AI Institute and director of the Malone Center for Engineering in Healthcare at the Johns Hopkins Whiting School of Engineering, shares Yegnasubramanian’s enthusiasm. “This alliance has the potential to rapidly accelerate innovation in cancer care using AI. Leveraging data across multiple centers will foster intellectual collaboration, allow us to train more powerful models and, critically, ensure that AI methods are effective for diverse patient populations and treatment settings. Johns Hopkins will bring its deep expertise in AI and technology in the school of engineering to bear on the most pressing challenges in cancer medicine.”Currently, cancer researchers face two challenges to using AI modeling: accessing the computational resources to quickly analyze large volumes of data, and remaining compliant with the regulatory and privacy requirements associated with sharing data from multiple cancer centers.
CAIA will serve an enabling role, providing the computing infrastructure to members of the alliance to process high volumes of cancer data generated during routine cancer care, such as electronic health records, pathology images, medical images and genome sequencing. This data, when paired with AI, could lead to novel insights about tumor biology, treatment resistance and identification of new therapeutic targets.
All of this will be achieved while maintaining data security, privacy and alignment with regulatory and ethical standards. Paired with strict governance, CAIA will use a federated AI learning framework in which each cancer center maintains its independent data, and AI models are sent to the data to produce results. Those results are then aggregated across participating members to uncover insights, all without sharing or exposing any of the raw data.
The Fred Hutch Cancer Center, which spearheaded the formation and initial funding of CAIA, will serve as the alliance’s coordinating center. In addition to Johns Hopkins, participating institutions include Dana-Farber Cancer Institute and Memorial Sloan Kettering Cancer Center.
