A new method for quantitative analysis of mammographic density.
Women with mammographic percent density >50% have a approximately three-fold increased risk of developing breast cancer, potentially making them screening candidates for breast MRI scanning. The purpose of this work is to introduce a new method to quantify mammographic percent density (MPD), and to compare the results with the current standard of care for breast density assessment. Craniocaudal (CC) and mediolateral oblique (MLO) mammograms for 104 patients were digitized and analyzed using an interactive computer-assisted segmentation routine implemented for two purposes: (1) to segment the breast area from background and radiographic markers, and (2) to segment dense from fatty portions of the breast. Our technique was evaluated by comparing the results to qualitative estimates determined by a certified breast radiologist using the BI-RADS Categorical Assessment (1 (fatty) to 4 (dense) scale). Statistically significant correlations (two-tailed, p < 0.01) were observed between calculated MPD and BI-RADS for both CC (Spearman rho = 0.67) and MLO views (Spearman rho = 0.71). For the CC view, statistically significant differences were revealed between the mean MPD for each BI-RADS category except between fatty (BI-RADS 1) and scattered (BI-RADS 2). Finally, for the MLO views, statistically significant differences in the mean MPD between all BI-RADS categories were observed. Comparing the CC and MLO views revealed a strong positive correlation (Pearson r = 0.8) in calculated MPD. In addition, an evaluation of the reproducibility of our segmentation demonstrated the average standard deviation of MPD for a subsample of eight patients, measured five times, was 1.9% (range: 0.03%-9.9%). Eliminating one misassignment reduced the average standard deviation to 0.75% (range: 0.03%-3.16%). Further analysis of approximately 10% of the patient sample revealed strong agreement (ICC = 0.80-0.85) in the reliability of MPD estimates for both mammographic views. Overall, these results demonstrate the feasibility of utilizing our approach for quantitative breast density segmentation, which may be useful for detecting small changes in MPD introduced through chemoprevention, diet, or other interventions.
Glide-Hurst
Carri K
CK
Karmanos Cancer Institute and Wayne State University, 110 East Warren, Hudson-Webber Cancer Research Center, Room 7040, Detroit, Michigan 48201, USA. glidec@karmanos.org
Duric
Neb
N
Littrup
Peter
P
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001940:Breast / Q000473:pathology*; D003201:Computers; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software