Technologies
This technology is a deep learning-based software tool that automatically estimates volumetric breast density directly from standard 3D digital breast tomosynthesis (DBT) images without requiring raw data.
Breast density is a major independent risk factor for breast cancer, but current methods to estimate volumetric breast density (VBD) rely on raw two-dimensional mammography data. Clinical centers rarely store this raw data due to storage constraints, archiving only the processed "for presentation" images. Consequently, large-scale automated density assessment is limited, and opportunities for personalized risk stratification are missed because current tools cannot operate on the standard archived images.
This technology, tomoLIBRA, is a deep learning model designed to calculate volumetric breast density (VBD) directly from 3D reconstructed digital breast tomosynthesis (DBT) images, which are routinely archived in Picture Archiving and Communication Systems (PACS). The software performs 3D segmentation of breast tissue into background, fatty, and dense tissue. By eliminating the dependence on raw projection data, the tool enables retrospective analysis of existing imaging archives and prospective integration into clinical screening workflows. Density measures derived from the software can support personalized breast cancer risk assessment and clinical decision-making.
The model has been validated on 1,080 DBT screening exams and tested in an independent case-control study of 834 patients, demonstrating significant association between density measurements and breast cancer diagnosis.
IR CU26015
Licensing Contact: Joan Martinez