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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