A team of researchers at Stony Brook University is developing a new method to analyze breast cancer imaging by incorporating mathematical modeling and deep learning. Led by Chao Chen, associate professor, and Prateek Prasanna, assistant professor in the Department of Biomedical Informatics, the approach aims to improve disease diagnosis and create treatment plans specific to biomarker imaging and modeling findings.
The research focuses on understanding breast tissue architecture and its changes over time. Breast tissue is composed of various cell types, including epithelial and adipose cells, which influence tumor pathogenesis. High breast density can be a risk factor for breast cancer, but the complexity and changing architecture of breast tissue often make subtle changes difficult to detect with standard imaging.
To address these challenges, Chen and Prasanna are developing "TopoQuant," a suite of informatics tools for analyzing breast tissue images. Built on advanced mathematical modeling and machine learning, TopoQuant will be used in collaboration with Stony Brook Medicine clinicians to uncover intricate changes in tissue architecture during cancer pathogenesis, disease progression, and radiation treatment.
The project is supported by a four-year $1.2 million grant from the National Cancer Institute (NCI), running through August 2028. Both researchers are affiliated with the Stony Brook Cancer Center’s Imaging, Biomarker Discovery, and Engineering Sciences Research Division.
"This research will offer new insights into how structural changes in breast tissue can influence cancer screening and treatment outcomes," said Chen. "Topology is the area of mathematics that studies structures. By incorporating topology with deep learning in a seamless fashion, we can develop novel algorithms to capture structural changes in ways that were previously difficult with traditional techniques such as textural radiomics."
Existing machine learning-driven tools used by cancer imaging researchers lack the capacity to interpret or explain findings comprehensively. However, TopoQuant aims to provide clinicians with quantitative evidence of changes in breast tissue architecture related to cancer risk and treatment response.
In preliminary findings published in 2021, the team demonstrated the efficacy of their approach using one of their informatics tools to predict a patient’s response to neoadjuvant chemotherapy for breast cancer. The results suggested differential topological behavior of breast tissue between patients who responded favorably to therapy and those who did not.
"Our prediction models will be unique in that they do not rely on traditional post-hoc interpretation but ensure interpretability by design," explained Prasanna. "The research is intended not only to benefit breast cancer diagnosis and treatment but also has broader applications in fields like neuroscience."
Other collaborators from RSOM include Alexander Stessin from the Department of Radiation Oncology; Wei Zhao from the Department of Radiology; and Haibin Ling from the Department of Computer Science within CEAS.
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