Skip to main content

Advertisement

SpringerLink
Log in

Quantification of water and lipid density with dual-energy mammography: validation in postmortem breasts

  • Breast
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Breast cancer is the most common cancer in women and the second leading cause of cancer death. It is well known that breast density is an important risk factor for breast cancer and also can be used to personalize screening and for assessment of treatment response. Breast density has previously been correlated to volumetric water density. The purpose of this study is to validate the accuracy and precision of dual-energy mammography in measuring water density in postmortem breasts.

Methods

Twenty pairs of postmortem breasts were imaged using dual-energy mammography with energy-sensitive photon-counting detectors. Chemical analysis was used as the reference standard to assess the accuracy of dual-energy mammography in measuring volumetric water and lipid density. Images from different views and contralateral breasts were used to assess estimate of precision for water and lipid volumetric density measurements.

Results

The measured volumetric water and lipid density from dual-energy mammography and chemical analysis were in good agreement, where the standard errors of estimates (SEE) of both were calculated to be 2.1%. Volumetric water and lipid density measurements from different views were also in good agreement, with a SEE of 1.3% and 1.1%, respectively.

Conclusions

The results indicate that dual-energy mammography can be used to accurately measure volumetric water and lipid density in breast tissue. Accurate quantification of volumetric water density is expected to enhance its utility as a risk factor for breast cancer and for assessment of response to therapy.

Key Points

• Dual-energy mammography can be used to accurately measure water and lipid volumetric density in breast tissue.

• Improved quantification of volumetric water density is expected to enhance its utility for assessment of response to therapy and as a risk factor for breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

ASIC:

Application-specific integrated circuits

BI-RADS:

Breast Imaging Reporting and Data System

CC:

Craniocaudal

CT:

Computed tomography

kVp:

Kilovolt peak

mAs:

Milliamp Second

MLO:

Mediolateral–oblique

MRI:

Magnetic resonance imaging

SEE:

Standard error of estimate

References

  1. (2006) World Heatlh Organization Fact Sheet N297: cancer

  2. McCormack VA, dos Santos SI (2006) Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 15:1159–1169

    Article  Google Scholar 

  3. Boyd NF, Martin LJ, Bronskill M, Yaffe MJ, Duric N, Minkin S (2010) Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst 102:1224–1237

    Article  Google Scholar 

  4. Boyd NF, Guo H, Martin LJ et al (2007) Mammographic density and the risk and detection of breast cancer. N Engl J Med 356:227–236

    Article  CAS  Google Scholar 

  5. Sprague BL, Conant EF, Onega T et al (2016) Variation in mammographic breast density assessments among radiologists in clinical practice: a multicenter observational study. Ann Intern Med 165:457–464

    Article  Google Scholar 

  6. Ekpo EU, Ujong UP, Mello-Thoms C, McEntee MF (2016) Assessment of interradiologist agreement regarding mammographic breast density classification using the fifth edition of the BI-RADS Atlas. AJR Am J Roentgenol 206:1119–1123

    Article  Google Scholar 

  7. Spayne MC, Gard CC, Skelly J, Miglioretti DL, Vacek PM, Geller BM (2012) Reproducibility of BI-RADS breast density measures among community radiologists: a prospective cohort study. Breast J 18:326–333

    Article  Google Scholar 

  8. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ (1994) The quantitative-analysis of mammographic densities. Phys Med Biol 39:1629–1638

    Article  CAS  Google Scholar 

  9. Sivaramakrishna R, Obuchowski NA, Chilcote WA, Powell KA (2001) Automatic segmentation of mammographic density. Acad Radiol 8:250–256

    Article  CAS  Google Scholar 

  10. Carton AK, Li JJ, Chen S, Conant E, Maidment ADA (2006) Optimization of contrast-enhanced digital breast tomosynthesis. In: Astley SM, Brady M, Rose C, Zwiggelaar R, (eds) Digital mammography, proceedings. (Lecture Notes in Computer Science), pp 183–189

  11. Kaufhold J, Thomas JA, Eberhard JW, Galbo CE, Trotter DE (2002) A calibration approach to glandular tissue composition estimation in digital mammography. Med Phys 29:1867–1880

    Article  CAS  Google Scholar 

  12. Pawluczyk O, Augustine BJ, Yaffe MJ et al (2003) A volumetric method for estimation of breast density on digitized screen-film mammograms. Med Phys 30:352–364

    Article  Google Scholar 

  13. Highnam R, Jeffreys M, McCormack V, Warren R, Smith GD, Brady M (2007) Comparing measurements of breast density. Phys Med Biol 52:5881–5895

    Article  CAS  Google Scholar 

  14. Cardinal HN, Fenster A (1990) An accurate method for direct dual-energy calibration and decomposition. Med Phys 17:327–341

    Article  CAS  Google Scholar 

  15. Molloi S, Ducote JL, Ding H, Feig SA (2014) Postmortem validation of breast density using dual-energy mammography. Med Phys 41:081917

    Article  Google Scholar 

  16. Ducote JL, Molloi S (2010) Quantification of breast density with dual energy mammography: an experimental feasibility study. Med Phys 37:793–801

    Article  Google Scholar 

  17. Ducote JL, Klopfer MJ, Molloi S (2011) Volumetric lean percentage measurement using dual energy mammography. Med Phys 38:4498–4504

    Article  Google Scholar 

  18. Ding HJ, Ducote JL, Molloi S (2012) Breast composition measurement with a cadmium-zinc-telluride based spectral computed tomography system. Med Phys 39:1289–1297

    Article  CAS  Google Scholar 

  19. Johnson T, Ding H, Le HQ, Ducote JL, Molloi S (2013) Breast density quantification with cone-beam CT: a post-mortem study. Phys Med Biol 58:8573–8591

    Article  Google Scholar 

  20. Ding H, Johnson T, Lin M et al (2013) Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study. Med Phys 40:122305

    Article  Google Scholar 

  21. Cho HM, Ding H, Kumar N, Sennung D, Molloi S (2017) Calibration phantoms for accurate water and lipid density quantification using dual energy mammography. Phys Med Biol 62:4589–4603

    Article  CAS  Google Scholar 

  22. Aslund M, Cederstrom B, Lundqvist M, Danielsson M (2006) Scatter rejection in multislit digital mammography. Med Phys 33:933–940

    Article  Google Scholar 

  23. Fredenberg E, Hemmendorff M, Cederstrom B, Aslund M, Danielsson M (2010) Contrast-enhanced spectral mammography with a photon-counting detector. Med Phys 37:2017–2029

    Article  Google Scholar 

  24. Fredenberg E, Lundqvist M, Cederstrom B, Aslund M, Danielsson M (2010) Energy resolution of a photon-counting silicon strip detector. Nucl Instrum Methods Phys Res Sect A 613:156–162

    Article  CAS  Google Scholar 

  25. Johansson H, von Tiedemann M, Erhard K et al (2017) Breast-density measurement using photon-counting spectral mammography. Med Phys 44:3579–3593

    Article  CAS  Google Scholar 

  26. Molloi S, Ding H, Feig S (2015) Breast density evaluation using spectral mammography, radiologist reader assessment, and segmentation techniques: a retrospective study based on left and right breast comparison. Acad Radiol 22:1052–1059

    Article  Google Scholar 

  27. Chow CK, Venzon D, Jones EC, Premkumar A, O'Shaughnessy J, Zujewski J (2000) Effect of tamoxifen on mammographic density. Cancer Epidemiol Biomarkers Prev 9:917–921

    CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Nikita Kumar and Drs. Bahman Sadeghi, Hanna Javan, and Alfonso Lam Ng for their support in data analysis. They would also like to thank Dr. Erik Fredenberg for valuable technical support and acknowledge Philips Medical Systems for providing the MicroDose mammography system for this research.

Funding

This work was supported in part by NIH/NCI grant R01CA13687.

Author information

Authors and Affiliations

  1. Department of Radiological Sciences, University of California, Irvine, Medical Sciences B, 140, Irvine, CA, 92697, USA

    Sabee Molloi, Huanjun Ding, Hyo-Min Cho & Daniel Beidokhti

Authors
  1. Sabee Molloi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huanjun Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyo-Min Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Beidokhti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sabee Molloi.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Sabee Molloi.

Conflict of interest

The authors of this manuscript declare a relationship with Philips Medical Systems. The authors do not have any financial and personal relationships with other people or organizations that could inappropriately influence their work.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Ethical approval

Institutional Review Board approval was not required since the study involved only postmortem breast tissue.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Molloi, S., Ding, H., Cho, HM. et al. Quantification of water and lipid density with dual-energy mammography: validation in postmortem breasts. Eur Radiol 31, 938–946 (2021). https://doi.org/10.1007/s00330-020-07179-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07179-9

Keywords

Search

Navigation