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

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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.

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References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  5. American College of Radiology, Breast Imaging Reporting and Data System, 4th edition. American College of Radiology, Reston, 2003.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  21. Thirion, J. P., Image matching as a diffusion process: An analogy with Maxwell’s demons. Med. Image Anal. 2(3):243–260, 1998.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  28. Field, A. P., Discovering Statistics Using SPSS, 3rd edition. SAGE Publications, Los Angeles, 2009.

    Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

Download references

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.

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Authors and Affiliations

  1. Department of Radiology, Seoul National University Hospital, Seoul, South Korea

    Woo Kyung Moon, Jin Mo Goo, Min Sun Bae & Jung Min Chang

  2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 10617, Republic of China

    Chung-Ming Lo, Violeta Ivanova & Ruey-Feng Chang

  3. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan

    Chiun-Sheng Huang

  4. Center for Functional Onco-Imaging and Department of Radiological Science, University of California Irvine, Irvine, CA, USA

    Jeon-Hor Chen

  5. Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan

    Jeon-Hor Chen

  6. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

    Ruey-Feng Chang

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  1. Woo Kyung Moon
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Correspondence to Ruey-Feng Chang.

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Moon, W.K., Lo, CM., Goo, J.M. et al. Quantitative Analysis for Breast Density Estimation in Low Dose Chest CT Scans. J Med Syst 38, 21 (2014). https://doi.org/10.1007/s10916-014-0021-5

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