Cumulus Breast Density
Collaborators: Stanford Epidemiology
Assessing breast cancer risk using mammography often includes evaluating breast density, but this has traditionally been studied using older film-based images. Today, most imaging is digital, and the images used in clinical care are processed for display. These processing steps can change how the image looks, raising questions about whether density measurements from these images are still reliable.
In this work, processed digital mammograms were used to measure breast density and evaluate its relationship with breast cancer risk. Density was assessed using Cumulus, a widely used tool for quantifying dense tissue on mammograms. The results showed that higher density remained strongly associated with increased risk, consistent with prior findings from film-based imaging. This supports the idea that routinely acquired clinical images can still provide meaningful and reproducible measurements.
Publication Link: PubMed
Figure A: Representative full-field digital mammography image prior to processing. This original image reflects the native acquisition resolution (70 micron pixel size) before any downsampling or filtering is applied.
The 3DQ Lab contributed by supporting the scale needed for this type of analysis during the early adoption of digital mammography, with work spanning 2012 to 2014. A dataset of over 30,000 mammograms was processed using a standardized thresholding approach to extract density-related information. In parallel, database connectivity and a structured data workflow were developed to support consistent data handling across large volumes of imaging.
This matters for clinical workflows because it confirms that the images already used in routine care can support meaningful risk assessment. There is no need for additional image types or special data access, making it easier to use existing imaging data for both patient care and large-scale research.
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