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European Radiology

, Volume 28, Issue 11, pp 4717–4724 | Cite as

Parenchymal pattern in women with dense breasts. Variation with age and impact on screening outcomes: observations from a UK screening programme

  • Laura Ward
  • S. Heller
  • S. Hudson
  • L. Wilkinson
Breast

Abstract

Objectives

To assess patterns of parenchymal tissue on mammography in women with dense breasts and to determine how this varies with age and affects recall to assessment and cancer diagnosis.

Method

Breast density data was obtained in women attending routine mammographic screening from April 2013 to March 2015 using automated breast density assessment software. Women with the densest breasts were selected for visual interpretation of parenchymal pattern (PP). One hundred non-assessed women, aged 50, 55, 60, 65 and 69-71 years (total = 500), provided controls. Cases included women recalled for assessment (mastectomy or implants excluded) (total = 280). Mammograms reviewed by ten readers and PP classified as: (1) very smooth; (2) mainly smooth; (3) mixed; (4) mainly nodular; (5) very nodular. The ratio of women in each category at each age and screening outcomes were compared by Pearson’s chi-squared test.

Results

Reader agreement for scoring PP was good (intraclass correlation = 0.6302). Proportions of women in each PP category were similar at all ages for controls (p = 0.147) and cases (p = 0.657). The ratio of PP categories did not vary significantly with age in those who underwent biopsy (p = 0.484). Thirty-four cancers were diagnosed. There was a significant correlation between a diagnosis of cancer and nodular PP compared to not nodular PP (p = 0.043).

Conclusions

The ratio of smooth to nodular pattern in women with the densest breasts did not vary with age. The PP of the breast tissue did not affect likelihood of recall to assessment or biopsy. There was a significant relationship between a nodular parenchymal pattern and diagnosis of cancer.

Key Points

This paper shows that there is good agreement between mammogram readers when classifying mammographic PP on a five-point scale from very smooth to very nodular.

In non-assessed women with the densest breasts, there is no significant change in the proportions of smooth to nodular patterns with increasing age.

The likelihood of recall for further assessment or biopsy at assessment is not related to PP in women with highest breast density.

When recalled for further assessment, significantly more women are diagnosed with cancer in the group with nodular PP on mammography when compared with smooth and mixed patterns.

Keywords

Breast cancer Mammography Breast neoplasms Breast density Cancer screening 

Abbreviations

ICC

Interclass correlation

NBSS

National Breast Screening System

PP

Parenchymal pattern

VGd

Volpara Grade d

Notes

Acknowledgements

These findings were presented at ECR 2016.

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr Louise Wilkinson.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was not required for this study because local governance approval was obtained from the research and development office as the observations were of routinely collected data.

Ethical approval

Institutional Review Board approval was not required because local governance approval was obtained from the research and development office as the observations of routinely collected data without the need for ethics approval.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Heller et al. [12].

Methodology

• retrospective

• observational

• performed at one institution

References

  1. 1.
    Boyd F, Martin LJ, Yaffe MJ et al (2011) Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res 13:223Google Scholar
  2. 2.
    van Gils CH, Hendriks JH, Otten JD, Holland R, Verbeek AL (2000) Parity and mammographic breast density in relation to breast cancer risk: indication of interaction. Eur J Cancer Prev 9:105–111CrossRefGoogle Scholar
  3. 3.
    Boyd NF, Byng JW, Jong RA et al (1995) Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst 87:670–675CrossRefGoogle Scholar
  4. 4.
    Chiu SYH, Duffy S, Yen AMF, Tabár L, Smith RA, Chen HH (2010) Effect of baseline breast density on breast cancer incidence, stage, mortality, and screening parameters: 25-year follow-up of a Swedish mammographic screening. Cancer Epidemiol Biomarkers Prev 19:1219–1228CrossRefGoogle Scholar
  5. 5.
    Gierach GL, Ichikawa L, Kerlikowske K et al (2012) Relationship between mammographic density and breast cancer death in the Breast Cancer Surveillance Consortium. J Natl Cancer Inst 104:1218–1227CrossRefGoogle Scholar
  6. 6.
    Wolfe JN (1976) Breast patterns as an index of risk for developing breast cancer. AJR Am J Roentgenol 126:1130–1137CrossRefGoogle Scholar
  7. 7.
    Winkel RR, von Euler-Chelpin M, Nielsen M et al (2015) Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study: impact on relative risk of breast cancer. BMC Cancer 15:274Google Scholar
  8. 8.
    Gram IT, Funkhouser E, Tabár L (1997) The Tabár classification of mammographic parenchymal patterns. Eur J Radiol 24:131–136CrossRefGoogle Scholar
  9. 9.
    Jakes RW, Duffy SW, Ng FC, Gao F, Ng EH (2000) Mammographic parenchymal patterns and risk of breast cancer at and after a prevalence screen in Singaporean women. Int J Epidemiol 29:11–19CrossRefGoogle Scholar
  10. 10.
    Cicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assessment 6:284–290CrossRefGoogle Scholar
  11. 11.
    R Core Team (2015) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria Available at: http://www.R-project.org/ Google Scholar
  12. 12.
    Heller SL, Hudson S, Wilkinson LS (2015) Breast density across a regional screening population: effects of age, ethnicity and deprivation. Br J Radiol 88(1055):20150242CrossRefGoogle Scholar
  13. 13.
    Nayak L, Miyake KK, Leung JWT et al (2016) Impact of breast density legislation on breast cancer risk assessment and supplemental screening: a survey of 110 radiology facilities. Breast J 22:493–500CrossRefGoogle Scholar
  14. 14.
    Bernardi D, Macaskill P, Pellegrini M et al (2016) Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study. Lancet Oncol 17:1105–1116CrossRefGoogle Scholar
  15. 15.
    Skaane P, Bandos AI, Gullien R et al (2013) Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology 267:47–56CrossRefGoogle Scholar
  16. 16.
    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 15:1159–1169CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologySt George’s University Hospitals NHS Foundation TrustLondonUK
  2. 2.New York University School of MedicineNew YorkUSA

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