Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images.
Previous studies have found that mammographic breast density is highly correlated with breast cancer risk. Therefore, mammographic breast density may be considered as an important risk factor in studies of breast cancer treatments. In this paper, we evaluated the accuracy of using mammograms for estimating breast density by analyzing the correlation between the percent mammographic dense area and the percent glandular tissue volume as estimated from MR images. A dataset of 67 cases having MR images (coronal 3-D SPGR T1-weighted pre-contrast) and corresponding 4-view mammograms was used in this study. Mammographic breast density was estimated by an experienced radiologist and an automated image analysis tool, Mammography Density ESTimator (MDEST) developed previously in our laboratory. For the estimation of the percent volume of fibroglandular tissue in breast MR images, a semiautomatic method was developed to segment the fibroglandular tissue from each slice. The tissue volume was calculated by integration over all slices containing the breast. Interobserver variation was measured for 3 different readers. It was found that the correlation between every two of the three readers for segmentation of MR volumetric fibroglandular tissue was 0.99. The correlations between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by an experienced radiologist were both 0.91. The correlation between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by MDEST was 0.91 and 0.89, respectively. The root-mean-square (rms) residual ranged from 5.4% to 6.3%. The mean bias ranged from 3% to 6%. The high correlation indicates that changes in mammographic density may be a useful indicator of changes in fibroglandular tissue volume in the breast.
Wei
Jun
J
Department of Radiology, University of Michigan, Ann Arbor, Ann Arbor, Michigan 49109, USA. jvwei@umich.edu
Chan
Heang-Ping
HP
Helvie
Mark A
MA
Roubidoux
Marilyn A
MA
Sahiner
Berkman
B
Hadjiiski
Lubomir M
LM
Zhou
Chuan
C
Paquerault
Sophie
S
Chenevert
Thomas
T
Goodsitt
Mitchell M
MM
D015502:Absorptiometry, Photon / Q000379:methods*; D019412:Anatomy, Cross-Sectional / Q000379:methods; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D003238:Connective Tissue / Q000000981:diagnostic imaging / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique