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Breast density measured volumetrically in a clinical environment: cross-sectional study with photon counting technology

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Objective

Mammographic breast density (BDen), the ratio of glandular volume (GVol) to breast volume (BVol), is the second most prevalent risk factor for breast cancer (BC). Newly developed photon counting technology allows precise and systematic measurements in clinical practice. Our objective is to see how these parameters change with age in women with and without cancer.

Materials and methods

This retrospective study analyzed results of BDen, GVol, and BVol in 64,182 mammograms performed with photon counting technology on 32,448 consecutive women from April 2014 to December 2015. Only their first study was included. We excluded women with incomplete data or with breast implants.

Results

Mean age of women without BC diagnosed during the study period was 52.1 ± 9.9. BC and was found in 263 women (0.81%). Mean age was 53.0 ± 10.4. BDen, GVol, and BVol were 14%, 24%, and 2% greater in women with BC (P < 0.001 for BDen and GVol and P = 0.02 for BVol). BDen and GVol diminished following similar patterns across age in both groups, with soft slopes before and after a steep drop from 50 to 60, probably due to menopause.

Conclusion

BDen diminishes with age in women with or without BC, but it is generally higher in women with BC. GVol could be a more robust indicator associated with BC risk than BDen. This technology can ease the way to studies of interventions to diminish BDen (or GVol) in the hope of diminishing BC incidence or predict if longitudinal changes are indicative of impending cancer.

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Abbreviations

ACR:

American College of Radiology

BC:

Breast cancer

BDen:

Breast density

BI-RADS:

Breast imaging reporting and data system

BVol:

Breast volume

CC:

Cranio-caudal

GAM:

Generalized Additive Model

GVol:

Glandular volume

MLO:

Medio-lateral oblique

NCCB:

Non-cancer contralateral breast

SD:

Standard deviation

US:

Ultrasound

References

  1. Wolfe JN (1976) Breast patterns as an index of risk for developing breast cancer. AJR Am J Roentgenol 126(6):1130–1137. https://doi.org/10.2214/ajr.126.6.1130

    Article  PubMed  CAS  Google Scholar 

  2. McCormack VA, dos Santos Silva I (2006) Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 15(6):1159–1169. https://doi.org/10.1158/1055-9965.EPI-06-0034

    Article  PubMed  Google Scholar 

  3. Engmann NJ, Golmakani MK, Miglioretti DL, Sprague BL, Kerlikowske K (2017) Breast cancer surveillance consortium. Population-attributable risk proportion of clinical risk factors for breast cancer. JAMA Oncol 3(9):1228–1236. https://doi.org/10.1001/jamaoncol.2016.6326

    Article  PubMed  PubMed Central  Google Scholar 

  4. Twombly R (2007) Dense breasts linked to higher breast cancer risk, but clinicians unsure of application. J Natl Cancer Inst 99(22):1661–1663. https://doi.org/10.1093/jnci/djm243

    Article  PubMed  Google Scholar 

  5. Kopans D (2008) Basic physics and doubts about relationship between mammographically determines tissue density and breast cancer risk. Radiology 246:2. https://doi.org/10.1148/radiol.2461070309

    Article  Google Scholar 

  6. Yaffe MJ (2008) Mammographic density. Measurement of mammographic density. Breast Cancer Res 10(3):209. https://doi.org/10.1186/bcr2102

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ding H, Molloi S (2012) Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study. Phys Med Biol 57(15):4719–4738. https://doi.org/10.1088/0031-9155/57/15/4719

    Article  PubMed  PubMed Central  Google Scholar 

  8. Machida Y, Tozaki M, Yoshida T, Saita A, Yakabe M, Nii K (2014) Feasibility study of a breast density measurement within a direct photon-counting mammography scanner system. Jpn J Radiol 32(9):561–567. https://doi.org/10.1007/s11604-014-0333-x

    Article  PubMed  Google Scholar 

  9. Johansson H, von Tiedemann M, Erhard K, Heese H, Ding H, Molloi S et al (2017) Breast-density measurement using photon-counting spectral mammography. Med Phys 44(7):3579–3593. https://doi.org/10.1002/mp.12279

    Article  PubMed  CAS  Google Scholar 

  10. Erhard K, Kilburn-Toppin F, Willsher P, Moa E, Fredenberg E, Wieberneit N et al (2016) Characterization of cystic lesions by spectral mammography: results of a clinical pilot study. Invest Radiol 51(5):340–347. https://doi.org/10.1097/RLI.0000000000000246

    Article  PubMed  Google Scholar 

  11. Sickles EA, D’Orsi CJ, Bassett LW et al (2013) ACR BI-RADS® mammography. In: ACR BI-RADS® atlas. Breast imaging reporting and data system. American College of Radiology, Reston, VA

  12. Wood SN (2003) Thin-plate regression splines. J R Stat Soc 65(1):95–114. https://doi.org/10.1111/1467-9868.00374

    Article  Google Scholar 

  13. Wood SN (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J Am Stat Assoc 99(467):673–686. https://doi.org/10.1198/016214504000000980

    Article  Google Scholar 

  14. Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc 73(1):3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x

    Article  Google Scholar 

  15. R Development Core Team (2008) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. ISBN 3-900051-07-0. www.R-project.org. Accessed 16 Oct 2017

  16. Wood SN (2016) Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  17. Destounis Stamatia, Arieno Andrea, Morgan Renee, Roberts Christina, Chan Ariane (2017) Qualitative versus quantitative mammographic breast density assessment: applications for the US and Abroad. Diagnostics 7:30. https://doi.org/10.3390/diagnostics7020030

    Article  PubMed Central  Google Scholar 

  18. Burton A, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M et al (2017) Mammographic density and ageing: a collaborativepooled analysis of cross-sectional data from 22 countries worldwide. PLoS Med 14(6):e1002335. https://doi.org/10.1371/journal.pmed.1002335

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lokate M, Stellato R, Veldhuis W, Peeters P, vanGils CH (2013) Age-related changes in mammographic density and breast cancer risk. Am J Epidemiol 178(1):101–109

    Article  Google Scholar 

  20. Goodwin PJ, Boyd NF (1988) Mammographic parenchymal pattern and breast cancer risk: a critical appraisal of the evidence. Am J Epidemiol 127(6):1097–1108

    Article  CAS  Google Scholar 

  21. McCarthy AM, Keller BM, Pantalone LM, Hsieh MK, Synnestvedt M, Conant EF et al (2016) Racial differences in quantitative measures of area and volumetric breast density. J Natl Cancer Inst. https://doi.org/10.1093/jnci/djw104

    Article  PubMed  PubMed Central  Google Scholar 

  22. Brand JS, Czene K, Shepherd JA, Leifland K, Heddson B, Sundbom A et al (2014) Automated measurement of volumetric mammographic density: a tool for widespread breast cancer risk assessment. Cancer Epidemiol Biomarkers Prev 23(9):1764–1772. https://doi.org/10.1158/1055-9965.EPI-13-1219

    Article  PubMed  Google Scholar 

  23. Kuchiki M, Hosoya T, Fukao A (2010) Assessment of breast cancer risk based on mammary gland volume measured with CT. Breast Cancer (Auckl) 4:57–64. https://doi.org/10.4137/BCBCR.S5248

    Article  Google Scholar 

  24. Shepherd JA, Kerlikowske K, Ma L, Duewer F, Fan B, Wang J et al (2011) Volume of mammographic density and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 20(7):1473–1482. https://doi.org/10.1158/1055-9965.EPI-10-1150

    Article  PubMed  PubMed Central  Google Scholar 

  25. Jansen LA, Backstein RM, Brown MH (2014) Breast size and breast cancer: a systematic review. J Plast Reconstr Aesthet Surg 67(12):1615–1623. https://doi.org/10.1016/j.bjps.2014.10.001

    Article  PubMed  CAS  Google Scholar 

  26. Abdolell M, Tsuruda KM, Brown P, Caines JS, Iles SE (2017) Breast density scales: the metric matters. Br J Radiol 90:20170307. https://doi.org/10.1259/bjr.20170307

    Article  PubMed  PubMed Central  Google Scholar 

  27. Boyd NF et al (2011) Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res 13:223. https://doi.org/10.1186/bcr2942

    Article  PubMed  PubMed Central  Google Scholar 

  28. Brentnall AR, Cohn WF, Knaus WA, Yaffe MJ, Cuzick J, Harvey JA (2019) A Case-control study to add volumetric or clinical mammographic density into the tyrer-cuzick breast cancer risk model. J Breast Imaging 1(2):99–106. https://doi.org/10.1093/jbi/wbz006

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors wish to thank Beatriz Viejo PhD. for writing and editorial assistance in the preparation of this manuscript. This study has been carried out under the auspices of the Càtedra d’Investigació en Obstetrícia i Ginecologia of the Autonomous University of Barcelona, Spain.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Obstetrics, Gynecology and Reproduction, Hospital Universitari Dexeus, Gran Vía Carlos III, 71-75, 08028, Barcelona, Spain

    Jean L. Browne, Lilian Casas, Guillermo Santandreu, Ignacio Rodriguez, Beatriz Navarro & M. Angela Pascual

  2. Department of Pathology, Hospital Universitari Dexeus, Barcelona, Spain

    Francesc Tresserra

Authors
  1. Jean L. Browne
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  2. Lilian Casas
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  3. Guillermo Santandreu
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  4. Ignacio Rodriguez
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  5. Beatriz Navarro
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  6. Francesc Tresserra
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  7. M. Angela Pascual
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Contributions

Study concepts: Jean L. Browne, L Casas, M. Angela Pascual, I. Rodriguez, Santandreu, B. Navarro; F. Tresserra. Study design: Jean L. Browne, L Casas, M. Angela Pascual, I. Rodriguez, Santandreu, B. Navarro; F. Tresserra. Data acquisition: I. Rodriguez; Jean L. Browne. Quality control of data and algorithms: I. Rodriguez. Data analysis and interpretation: Jean L. Browne; I. Rodriguez; M. Angela Pascual. Statistical analysis: I. Rodriguez. Manuscript preparation: Jean L. Browne; M. Angela Pascual; G. Santandreu; B. Navarro; F. Tresserra. Manuscript editing: Jean L. Browne; M. Angela Pascual; G. Santandreu; B. Navarro; L. Casas. Manuscript review: Jean L. Browne; M. Angela Pascual; F. Tresserra.

Corresponding author

Correspondence to Jean L. Browne.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This manuscript complies with the current laws of the country.

Statement of human rights

All procedures performed in the study were in accordance with the ethical standards of the institutional review board IRB (Càtedra d´Investigació en Obstetricia I Ginecologia, Universitat Autònoma de Barcelona, reference number: 191611092) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Statement on the welfare of animals

This article does not contain any studies with animals performed by any of the authors.

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Browne, J.L., Casas, L., Santandreu, G. et al. Breast density measured volumetrically in a clinical environment: cross-sectional study with photon counting technology. Breast Cancer Res Treat 179, 755–762 (2020). https://doi.org/10.1007/s10549-019-05502-7

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  • DOI: https://doi.org/10.1007/s10549-019-05502-7

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