Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a 4- to 6-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from 2-dimensional (2-D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3-D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3-D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N= 2 to 7 replicates in 6 women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N=95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (I) 0.76–0.86, 0.8–1.1, and 0.87–0.92 for %-gland tissue, (II) 0.72–0.82, 0.64–0.96, and 0.77–0.91, for glandular volume, (III) 0.87–0.98, 0.94–1.07, and 0.89–0.99, for fat volume, and (IV) 0.89–0.98, 0.94–1.00, and 0.89–0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI vs. mammography, and between each MRI vs. MATH data than between each MRI vs. HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.
