The task of image interpretation by radiologists involves two concomitant processes the evaluation of morphologic features of a finding, as well as the estimation of its temporal behavior (changes in size, number, shape, density, margin characteristics, etc.). The ability to detect temporal change in breast tissue and suspicious lesions on mammography has a potential to significantly improve the specificity of breast cancer detection and is therefore of great clinical value. However, the existing commercial and research computer-assisted detection (CAD) techniques (both traditional radiomic and deep learning (DL)-based) focus on interpretation of screening mammography at a single time point and cannot efficiently track temporal changes. This lowers the specificity of these tools and limits their clinical utility. 

To address this unmet need, we aim to develop computational approaches for longitudinal mammography registration, interpretation, and automatic image labeling using text-based radiology reports to enable efficient and comprehensive imaging data analysis.

Modern clinical and translational research in the context of precision medicine requires analysis of large volumes of imaging data. Within this project, we will develop computational tools to maximize the utilization of existing clinical data for retrospective research purposes and decrease the manpower needed for image labeling. Furthermore, the image processing pipeline developed in this proposed study will be subsequently used for integrated imaging and blood biomarker analysis in the MERIT cohort (n > 7,300), to enable further breast cancer prevention and early detection research.

This project is currently ongoing on and is funded by the Joint Center for Computational Oncology in the 2023-2024 Pilot Project Program.