Quantitative Analysis for Breast Density Estimation in Low Dose Chest CT Scans

  • Woo Kyung Moon
  • Chung-Ming Lo
  • Jin Mo Goo
  • Min Sun Bae
  • Jung Min Chang
  • Chiun-Sheng Huang
  • Jeon-Hor Chen
  • Violeta Ivanova
  • Ruey-Feng Chang
Research Article

Abstract

A computational method was developed for the measurement of breast density using chest computed tomography (CT) images and the correlation between that and mammographic density. Sixty-nine asymptomatic Asian women (138 breasts) were studied. With the marked lung area and pectoralis muscle line in a template slice, demons algorithm was applied to the consecutive CT slices for automatically generating the defined breast area. The breast area was then analyzed using fuzzy c-mean clustering to separate fibroglandular tissue from fat tissues. The fibroglandular clusters obtained from all CT slices were summed then divided by the summation of the total breast area to calculate the percent density for CT. The results were compared with the density estimated from mammographic images. For CT breast density, the coefficient of variations of intraoperator and interoperator measurement were 3.00 % (0.59 %–8.52 %) and 3.09 % (0.20 %–6.98 %), respectively. Breast density measured from CT (22 ± 0.6 %) was lower than that of mammography (34 ± 1.9 %) with Pearson correlation coefficient of r = 0.88. The results suggested that breast density measured from chest CT images correlated well with that from mammography. Reproducible 3D information on breast density can be obtained with the proposed CT-based quantification methods.

Keywords

Breast density CT Mammography Fuzzy c-mean Image registration 

Notes

Acknowledgments

The authors thank the National Science Council (NSC 99-2221-E-002-136-MY3), Ministry of Economic Affairs (100-EC-17-A-19-S1-164) of the Republic of China and National Taiwan University (101R890863) for the funding support. This work was supported by the Industrial Strategic Technology Development Program (10042581) funded by the Ministry of Knowledge Economy (MKE, Korea) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-01010846).

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    McCormack, V. A., and Silva, I. D. S., Breast density and parenchymal patterns as markers of breast cancer risk: A meta-analysis. Cancer Epidemiol. Biomarkers 15(6):1159–1169, 2006. doi: 10.1158/1055-9965-Epi-06-0034.CrossRefGoogle Scholar
  2. 2.
    Boyd, N. F., Guo, H., Martin, L. J., Sun, L. M., Stone, J., Fishell, E., Jong, R. A., Hislop, G., Chiarelli, A., Minkin, S., and Yaffe, M. J., Mammographic density and the risk and detection of breast cancer. N. Engl. J. Med. 356(3):227–236, 2007.CrossRefGoogle Scholar
  3. 3.
    van Duijnhoven, F. J. B., Peeters, P. H. M., Warren, R. M. L., Bingham, S. A., van Noord, P. A. H., Monninkhof, E. M., Grobbee, D. E., and van Gils, C. H., Postmenopausal hormone therapy and changes in mammographic density. J. Clin. Oncol. 25(11):1323–1328, 2007. doi: 10.1200/Jco.2005.04.7332.CrossRefGoogle Scholar
  4. 4.
    Cuzick, J., Warwick, J., Pinney, E., Duffy, S. W., Cawthorn, S., Howell, A., Forbes, J. F., and Warren, R. M. L., Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: A nested case-control study. J. Natl. Cancer Inst. 103(9):744–752, 2011. doi: 10.1093/Jnci/Djr079.CrossRefGoogle Scholar
  5. 5.
    American College of Radiology, Breast Imaging Reporting and Data System, 4th edition. American College of Radiology, Reston, 2003.Google Scholar
  6. 6.
    Harvey, J. A., and Bovbjerg, V. E., Quantitative assessment of mammographic breast density: Relationship with breast cancer risk. Radiology 230(1):29–41, 2004. doi: 10.1148/radiol.2301020870.CrossRefGoogle Scholar
  7. 7.
    Heine, J. J., Cao, K., Rollison, D. E., Tiffenberg, G., and Thomas, J. A., A quantitative description of the percentage of breast density measurement using full-field digital mammography. Acad. Radiol. 18(5):556–564, 2011. doi: 10.1016/j.acra.2010.12.015.CrossRefGoogle Scholar
  8. 8.
    Nie, K., Chen, J.-H., Chan, S., Chau, M.-K. I., Hon, J. Y., Bahri, S., Tseng, T., Nalcioglu, O., and Su, M.-Y., Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med. Phys. 35:5253, 2008.CrossRefGoogle Scholar
  9. 9.
    Boyd, N., Martin, L., Chavez, S., Gunasekara, A., Salleh, A., Melnichouk, O., Yaffe, M., Friedenreich, C., Minkin, S., and Bronskill, M., Breast-tissue composition and other risk factors for breast cancer in young women: A cross-sectional study. Lancet Oncol. 10(6):569–580, 2009. doi: 10.1016/S1470-2045(09)70078-6.CrossRefGoogle Scholar
  10. 10.
    Moon, W. K., Shen, Y. W., Huang, C. S., Luo, S. C., Kuzucan, A., Chen, J. H., and Chang, R. F., Comparative study of density analysis using automated whole breast ultrasound and MRI. Med. Phys. 38(1):382–389, 2011. doi: 10.1118/1.3523617.CrossRefGoogle Scholar
  11. 11.
    Nie, K., Chen, J. H., Chan, S., Chau, M. K. I., Yu, H. J., Bahri, S., Tseng, T., Nalcioglu, O., and Su, M. Y., Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med. Phys. 35(12):5253–5262, 2008. doi: 10.1118/1.3002306.CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Bellomi, M., Gatti, G., and Ping, X., Accuracy of computed tomography perfusion in assessing metastatic involvement of enlarged axillary lymph nodes in patients with breast cancer. Breast Cancer Res. 9(4):R40, 2007. doi: 10.1186/Bcr1738.CrossRefGoogle Scholar
  13. 13.
    Isaacs, R. J., Ford, J. M., Allan, S. G., Forgenson, G. V., and Gallagher, S., Role of computed-tomography in the staging of primary breast-cancer. Br. J. Surg. 80(9):1137–1137, 1993.CrossRefGoogle Scholar
  14. 14.
    Prionas, N. D., Lindfors, K. K., Ray, S., Huang, S. Y., Beckett, L. A., Monsky, W. L., and Boone, J. M., Contrast-enhanced dedicated breast CT: Initial clinical experience. Radiology 256(3):714–723, 2010. doi: 10.1148/radiol.10092311.CrossRefGoogle Scholar
  15. 15.
    Lindfors, K. K., Boone, J. M., Newell, M. S., and D’Orsi, C. J., Dedicated breast computed tomography: The optimal cross-sectional imaging solution? Radiol. Clin. N. Am. 48(5):1043, 2010. doi: 10.1016/j.rcl.2010.06.001.CrossRefGoogle Scholar
  16. 16.
    Aberle, D. R., Adams, A. M., Berg, C. D., Black, W. C., Clapp, J. D., Fagerstrom, R. M., Gareen, I. F., Gatsonis, C., Marcus, P. M., Sicks, J. D., and Team NLSTR, Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365(5):395–409, 2011. doi: 10.1056/Nejmoa1102873.CrossRefGoogle Scholar
  17. 17.
    Megumi Kuchiki, T. H., and Fukao, A., Assessment of breast cancer risk based on mammary gland volume measured with CT. Breast Cancer Basic Clin. Res. 4:57–64, 2010.CrossRefGoogle Scholar
  18. 18.
    Juneja, P., Harris, E. J., Kirby, A. M., and Evans, P. M., Adaptive breast radiation therapy using modeling of tissue mechanics: A breast tissue segmentation study. Int. J. Radiat. Oncol. Biol. Phys. 84:e419–e425, 2012.CrossRefGoogle Scholar
  19. 19.
    Yang, X. F., Wu, S. Y., Sechopoulos, I., and Fei, B. W., Cupping artifact correction and automated classification for high-resolution dedicated breast CT images Xiaofeng Yang and Shengyong Wu. Med. Phys. 39(10):6397–6406, 2012. doi: 10.1118/1.4754654.CrossRefGoogle Scholar
  20. 20.
    Hou, J. D., Guerrero, M., Chen, W. J., and D’Souza, W. D., Deformable planning CT to cone-beam CT image registration in head-and-neck cancer. Med. Phys. 38(4):2088–2094, 2011. doi: 10.1118/1.3554647.CrossRefGoogle Scholar
  21. 21.
    Thirion, J. P., Image matching as a diffusion process: An analogy with Maxwell’s demons. Med. Image Anal. 2(3):243–260, 1998.CrossRefGoogle Scholar
  22. 22.
    Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., and Hawkes, D. J., Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imaging 18(8):712–721, 1999.CrossRefGoogle Scholar
  23. 23.
    Lin, M. Q., Chen, J. H., Mehta, R. S., Bahri, S., Chan, S. W., Nalcioglu, O., and Su, M. Y., Spatial shrinkage/expansion patterns between breast density measured in two MRI scans evaluated by non-rigid registration. Phys. Med. Biol. 56(18):5865–5875, 2011. doi: 10.1088/0031-9155/56/18/006.CrossRefGoogle Scholar
  24. 24.
    Wang, H., Dong, L., O’Daniel, J., Mohan, R., Garden, A. S., Ang, K. K., Kuban, D. A., Bonnen, M., Chang, J. Y., and Cheung, R., Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy. Phys. Med. Biol. 50(12):2887–2905, 2005. doi: 10.1088/0031-9155/50/12/011.CrossRefGoogle Scholar
  25. 25.
    Van den Bulcke, J., Boone, M., Van Acker, J., and Van Hoorebeke, L., Three-dimensional X-ray imaging and analysis of fungi on and in wood. Microsc. Microanal. 15(5):395–402, 2009. doi: 10.1017/S1431927609990419.CrossRefGoogle Scholar
  26. 26.
    Keator, D. B., Fallon, J. H., Lakatos, A., Fowlkes, C. C., Potkin, S. G., and Ihler, A., Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification. Hum. Brain Mapp. 2012. doi: 10.1002/hbm.22149.Google Scholar
  27. 27.
    Liao, C. C., Xiao, F. R., Wong, J. M., and Chiang, I. J., Automatic recognition of midline shift on brain CT images. Comput. Biol. Med. 40(3):331–339, 2010. doi: 10.1016/j.compbiomed.2010.01.004.CrossRefGoogle Scholar
  28. 28.
    Field, A. P., Discovering Statistics Using SPSS, 3rd edition. SAGE Publications, Los Angeles, 2009.Google Scholar
  29. 29.
    Joachim, N., Rochtchina, E., Tan, A. G., Hong, T., Mitchell, P., and Wang, J. J., Right and left correlation of retinal vessel caliber measurements in anisometropic children: Effect of refractive error. Invest. Ophthalmol. Vis. Sci. 53(9):5227–5230, 2012. doi: 10.1167/Iovs.12-9422.CrossRefGoogle Scholar
  30. 30.
    Uyamker, B., Rajapakshe, R., Gordon, P., and Silver, S., Quest for a “gold standard” for breast density evaluation. Med. Phys. 36(9):4305–4305, 2009.Google Scholar
  31. 31.
    Highnam, R., Brady, M., Yaffe, M., Karssemeijer, N., and Harvey, J., Robust Breast Composition Measures – Volpara. Paper presented at the International Workshop on Digital Mammography: Girona, Spain, 2010.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Woo Kyung Moon
    • 1
  • Chung-Ming Lo
    • 2
  • Jin Mo Goo
    • 1
  • Min Sun Bae
    • 1
  • Jung Min Chang
    • 1
  • Chiun-Sheng Huang
    • 3
  • Jeon-Hor Chen
    • 4
    • 5
  • Violeta Ivanova
    • 2
  • Ruey-Feng Chang
    • 2
    • 6
  1. 1.Department of RadiologySeoul National University HospitalSeoulSouth Korea
  2. 2.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiRepublic of China
  3. 3.Department of SurgeryNational Taiwan University Hospital and National Taiwan University College of MedicineTaipeiTaiwan
  4. 4.Center for Functional Onco-Imaging and Department of Radiological ScienceUniversity of California IrvineIrvineUSA
  5. 5.Department of RadiologyE-Da Hospital and I-Shou UniversityKaohsiungTaiwan
  6. 6.Graduate Institute of Biomedical Electronics and BioinformaticsNational Taiwan UniversityTaipeiTaiwan

Personalised recommendations