Journal of Digital Imaging

, Volume 25, Issue 5, pp 591–598 | Cite as

Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets

  • Hui LiEmail author
  • Maryellen L. Giger
  • Li Lan
  • Jeremy Bancroft Brown
  • Aoife MacMahon
  • Mary Mussman
  • Olufunmilayo I. Olopade
  • Charlene Sennett


The purpose of this study was to demonstrate the robustness of our prior computerized texture analysis method for breast cancer risk assessment, which was developed initially on a limited dataset of screen-film mammograms. This current study investigated the robustness by (1) evaluating on a large clinical dataset, (2) using full-field digital mammograms (FFDM) as opposed to screen-film mammography, and (3) incorporating analyses over two types of high-risk patient sets, as well as patients at low risk for breast cancer. The evaluation included the analyses on the parenchymal patterns of women at high risk of developing of breast cancer, including both BRCA1/2 gene mutation carriers and unilateral cancer patients, and of women at low risk of developing breast cancer. A total of 456 cases, including 53 women with BRCA1/2 gene mutations, 75 women with unilateral cancer, and 328 low-risk women, were retrospectively collected under an institutional review board approved protocol. Regions-of-interest (ROIs), were manually selected from the central breast region immediately behind the nipple. These ROIs were subsequently used in computerized feature extraction to characterize the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of the computerized texture features in the task of distinguishing between high-risk and low-risk subjects. In a round robin evaluation on the FFDM dataset with Bayesian artificial neural network analysis, AUC values of 0.82 (95% confidence interval [0.75, 0.88]) and 0.73 (95% confidence interval [0.67, 0.78]) were obtained between BRCA1/2 gene mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results from computerized texture analysis on digital mammograms demonstrated that high-risk and low-risk women have different mammographic parenchymal patterns. On this large clinical dataset, we validated our methods for quantitative analyses of mammographic patterns on FFDM, statistically demonstrating again that women at high risk tend to have dense breasts with coarse and low-contrast texture patterns.


Computerized texture analysis Breast cancer risk assessment Mammographic parenchymal patterns Full-field digital mammograms Quantitative imaging analysis 



This research was supported in part by the University of Chicago Breast SPORE P50-CA125183, DOE grant DE-FG02-08ER6478, NIH S10 RR021039, and P30 CA14599. M. L. Giger is a stockholder in R2 Technology/Hologic and shareholder in Quantitative Insights, and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. It is the University of Chicago Conflict of Interest Policy that investigators disclose publicly actual or potential significant financial interest that would reasonably appear to be directly and significantly affected by the research activities.


  1. 1.
    Siegel R, Ward E, Brawley O, Jemal A: Cancer Statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin 61:212–236, 2011PubMedCrossRefGoogle Scholar
  2. 2.
    American College of Radiology: Breast Imaging Reporting and Data System (BI-RADS), 4th edition. American College of Radiology, Reston, Va, 2003Google Scholar
  3. 3.
    Wolfe JN: Breast patterns as an index of risk for developing breast cancer. Am J Roentgenol 126:1130–1139, 1976Google Scholar
  4. 4.
    Boyd NF, Martin LJ, Stone J, Greenberg, et al: Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention. Curr Oncol Rep 3:314–321, 2001PubMedCrossRefGoogle Scholar
  5. 5.
    Brisson J, Diorio C, Mâsse B: Wolfe’s parenchymal pattern and percentage of the breast with mammographic densities: redundant or complementary classifications? Cancer Epidemiol Biomarkers Prev 12:728–732, 2003PubMedGoogle Scholar
  6. 6.
    Boyd NF, Lockwood GA, Martin LJ, et al: Mammographic densities and breast cancer risk. Breast Dis 10:113–126, 1998PubMedGoogle Scholar
  7. 7.
    Boyd NF, Martin LJ, Stone J, et al: Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention. Cancer Prev 3:314–321, 2001Google Scholar
  8. 8.
    Atkinson C, Warren R, Bingham SA, et al: Mammographic patterns as a predictive biomarker of breast cancer risk: effect of tamoxifen. Cancer Epidemiol Biomarkers Prev 8:863–866, 1999PubMedGoogle Scholar
  9. 9.
    Saftlas AF, Hoover RN, Brinton LA, et al: Mammographic densities and risk of breast cancer. Cancer 67:2833–2838, 1991PubMedCrossRefGoogle Scholar
  10. 10.
    Byrne C, Schairer C, Wolfe JN, et al: Mammographic features and breast cancer risk: Effects with time, age, and menopause status. J Natl Cancer Inst 87:1622–1629, 1995PubMedCrossRefGoogle Scholar
  11. 11.
    Boyd NF, Byng J, Jong R: Quantitative classification of mammographic densities and breast cancer risk: Results from the Canadian National Breast Screening Study. J Natl Cancer Inst 87:670–675, 1995PubMedCrossRefGoogle Scholar
  12. 12.
    Boyd NF, Guo H, Martin LJ, et al: Mammographic density and the risk and detection of breast cancer. N Engl J Med 356:227–236, 2007PubMedCrossRefGoogle Scholar
  13. 13.
    Byng JW, Yaffe MJ, Lockwood GA, et al: Automated analysis of mammographic densities and breast carcinoma risk. Cancer 80:66–74, 1997PubMedCrossRefGoogle Scholar
  14. 14.
    Manduca A, Carston MJ, Heine JJ, et al: Texture features from mammographic images and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 18:837–845, 2009PubMedCrossRefGoogle Scholar
  15. 15.
    Wei J, Chan HP, Wu YT, et al: Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case–control study. Radiology 260:42–49, 2011PubMedCrossRefGoogle Scholar
  16. 16.
    Huo Z, Giger ML, Wolverton DE, et al: Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. Med Phys 27:4–12, 2000PubMedCrossRefGoogle Scholar
  17. 17.
    Huo Z, Giger ML, Olopade OI, et al: Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. Radiology 225:519–526, 2002PubMedCrossRefGoogle Scholar
  18. 18.
    Li H, Giger ML, Olopade OI, et al: Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad Radiol 12:863–873, 2005PubMedCrossRefGoogle Scholar
  19. 19.
    Li H, Giger ML, Olopade OI, et al: Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol 14:513–521, 2007PubMedCrossRefGoogle Scholar
  20. 20.
    Li H, Giger ML, Olopade OI, et al: Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment. J Digit Imaging 21:145–152, 2008PubMedCrossRefGoogle Scholar
  21. 21.
    Metz CE: ROC methodology in radiographic imaging. Invest Radiol 21:720–733, 1986PubMedCrossRefGoogle Scholar
  22. 22.
    Metz CE: Some practical issues of experimental design and data analysis in radiological ROC studies. Invest Radiol 24:234–245, 1989PubMedCrossRefGoogle Scholar
  23. 23.
    Gail MH, Brinton LA, Byar DP, et al: Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81:1879–1886, 1989PubMedCrossRefGoogle Scholar
  24. 24.
    Li H, Giger ML, Huo Z, et al: Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. Med Phys 31:549–555, 2004PubMedCrossRefGoogle Scholar
  25. 25.
    Chen W, Giger ML, Li H, Bick U, Newstead GM: Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 58:562–571, 2007PubMedCrossRefGoogle Scholar
  26. 26.
    Huberty CJ: Applied Discriminant Analysis, John Wiley and Sons, Inc, 1994Google Scholar
  27. 27.
    Lachenbruch PL: Discriminant Analysis. Hafer, London, England, 1975Google Scholar
  28. 28.
    Kupinski MA, Edwards DC, Giger ML, et al: Ideal observer approximation using Bayesian classification neural networks. IEEE Trans Med Imaging 20:886–899, 2001PubMedCrossRefGoogle Scholar
  29. 29.
  30. 30.
    Holm S: A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70, 1979Google Scholar
  31. 31.
    Johnson RA, Wichern DW: Applied multivariate statistical analysis, 3rd edition. Prentice-Hall, Englewood Cliffs, NJ, 1992Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2012

Authors and Affiliations

  • Hui Li
    • 1
    Email author
  • Maryellen L. Giger
    • 1
  • Li Lan
    • 1
  • Jeremy Bancroft Brown
    • 1
  • Aoife MacMahon
    • 1
  • Mary Mussman
    • 1
  • Olufunmilayo I. Olopade
    • 2
  • Charlene Sennett
    • 1
  1. 1.Department of RadiologyThe University of ChicagoChicagoUSA
  2. 2.Department of Medicine and Human GeneticsThe University of ChicagoChicagoUSA

Personalised recommendations