Abstract #1121

A Parsimonious Assessment of Breast Density Classes from Quantitative, AI-based FGT Volume Segmentations

Pablo F. Damasceno^1,2, Tatiana Kelil^1,2, Rutwik Shah^1,2, Bruno Astuto Arouche Nunes^1,2, Jason Crane^1,2, and Sharmila Majumdar^1,2

¹Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²Center for Intelligent Imaging, University of California San Francisco, San Francisco, CA, United States

Given its potential importance as a biomarker of breast cancer risk, a reliable and objective quantitative measurement of Fibroglandular tissue (FGT) with limited intra or inter-rater variability will be invaluable in clinical practice. Currently, the amount of FGT in breast MRIs is reported via a 4-level qualitative system. We investigate the relationship between these classes and the amount of FGT, obtained via deep-learning segmentations. We find that the distribution of FGT in these classes deviates significantly from quartiles, but more uniform distributions can be achieved by emulating the radiologist’s workflow during clinical reporting.

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