Skip to main content

Advertisement

SpringerLink
Log in

Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches

  • Original paper
  • Published:
Cancer Causes & Control Aims and scope Submit manuscript

Abstract

Background

Mammographic density (MD) varies throughout a woman’s life. We compared the performance of a fully automated (ImageJ-based) method to the observer-dependent Cumulus approach in the assessment of within-woman changes in MD over time.

Methods

MD was assessed in annual pre-diagnostic films (from age 40 to early 50s) from 313 breast cancer cases and 452 matched controls using Cumulus (left medio-lateral oblique (MLO) readings) and the ImageJ-based method (mean left–right MLO readings). Linear mixed models were used to compare within-woman changes in MD among controls. Associations between individual-specific MD trajectories and breast cancer were examined using conditional logistic regression.

Results

The age-related trajectories predicted by Cumulus and the ImageJ-based method were similar for all MD measures, except that the ImageJ-based method yielded slightly higher (by 2.54 %, 95 % CI 2.07 %, 3.00 %) estimates for percent MD. For both methods, the yearly rate of change in percent MD was twice faster after menopause than before, and higher BMI was associated with lower mean percent MD, but not associated with rate of change. Both methods yielded similar associations of individual-specific MD trajectories with breast cancer risk.

Conclusions

The ImageJ-based method is a valid fully automated alternative to Cumulus for measuring within-woman changes in MD in digitized films. The Age Trial is registered as an International Standard Randomized Controlled Trial, number ISRCTN24647151.

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

Similar content being viewed by others

Abbreviations

BMI:

Body mass index

BC:

Breast cancer

CC:

Cranio-caudal

CI:

Confidence interval

IQR:

Inter-quartile range

MD:

Mammographic density

MLO:

Medio-lateral oblique

PD:

Percent density

SD:

Standard deviation

References

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

    Article  Google Scholar 

  2. Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, Jong RA, Hislop G, Chiarelli A, Minkin S, Yaffe MJ (2007) Mammographic density and the risk and detection of breast cancer. N Engl J Med 356(3):227–236. doi:10.1056/NEJMoa062790

    Article  CAS  PubMed  Google Scholar 

  3. Salminen TM, Saarenmaa IE, Heikkila MM, Hakama M (1998) Risk of breast cancer and changes in mammographic parenchymal patterns over time. Acta Oncol (Stockholm, Sweden) 37(6):547–551

    Article  CAS  Google Scholar 

  4. van Gils CH, Hendriks JH, Holland R, Karssemeijer N, Otten JD, Straatman H, Verbeek AL (1999) Changes in mammographic breast density and concomitant changes in breast cancer risk. Eur J Cancer Prev 8(6):509–515

    Article  PubMed  Google Scholar 

  5. Maskarinec G, Pagano I, Lurie G, Kolonel LN (2006) A longitudinal investigation of mammographic density: the multiethnic cohort. Cancer Epidemiol Biomark Prev 15(4):732–739. doi:10.1158/1055-9965.EPI-05-0798

    Article  Google Scholar 

  6. Work ME, Reimers LL, Quante AS, Crew KD, Whiffen A, Terry MB (2014) Changes in mammographic density over time in breast cancer cases and women at high risk for breast cancer. Int J Cancer 135(7):1740–1744. doi:10.1002/ijc.28825

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Vachon CM, Pankratz VS, Scott CG, Maloney SD, Ghosh K, Brandt KR, Milanese T, Carston MJ, Sellers TA (2007) Longitudinal trends in mammographic percent density and breast cancer risk. Cancer Epidemiol Biomark Prev 16(5):921–928. doi:10.1158/1055-9965.EPI-06-1047

    Article  Google Scholar 

  8. Kerlikowske K, Ichikawa L, Miglioretti DL, Buist DS, Vacek PM, Smith-Bindman R, Yankaskas B, Carney PA, Ballard-Barbash R (2007) Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk. J Natl Cancer Inst 99(5):386–395. doi:10.1093/jnci/djk066

    Article  PubMed  Google Scholar 

  9. Lokate M, Stellato RK, Veldhuis WB, Peeters PH, van Gils CH (2013) Age-related changes in mammographic density and breast cancer risk. Am J Epidemiol 178(1):101–109. doi:10.1093/aje/kws446

    Article  PubMed  Google Scholar 

  10. McCormack VA, Highnam R, Perry N, dos Santos Silva I (2007) Comparison of a new and existing method of mammographic density measurement: intramethod reliability and associations with known risk factors. Cancer Epidemiol Biomark Prev 16(6):1148–1154. doi:10.1158/1055-9965.EPI-07-0085

    Article  Google Scholar 

  11. Li J, Szekely L, Eriksson L, Heddson B, Sundbom A, Czene K, Hall P, Humphreys K (2012) High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer. Breast Cancer Res 14(4):R114. doi:10.1186/bcr3238

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sovio U, Li J, Aitken Z, Humphreys K, Czene K, Moss S, Hall P, McCormack V, Dos-Santos-Silva I (2014) Comparison of fully and semi-automated area-based methods for measuring mammographic density and predicting breast cancer risk. Br J Cancer 110(7):1908–1916. doi:10.1038/bjc.2014.82

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Moss S (1999) A trial to study the effect on breast cancer mortality of annual mammographic screening in women starting at age 40. Trial Steering Group. J Med Screen 6(3):144–148

    Article  CAS  PubMed  Google Scholar 

  14. Moss SM, Cuckle H, Evans A, Johns L, Waller M, Bobrow L (2006) Effect of mammographic screening from age 40 years on breast cancer mortality at 10 years’ follow-up: a randomised controlled trial. Lancet 368(9552):2053–2060. doi:10.1016/S0140-6736(06)69834-6

    Article  PubMed  Google Scholar 

  15. Rabe-Hesketh S, Skrondal A (eds) (2012) Multilevel and longitudinal modeling using stata, Volume I: continuous responses. 3rd edn. Stata Press, College Station, TX

  16. McCormack VA, Perry NM, Vinnicombe SJ, dos Santos Silva I (2010) Changes and tracking of mammographic density in relation to Pike’s model of breast tissue ageing: a UK longitudinal study. Int J Cancer 127(2):452–461. doi:10.1002/ijc.25053

    CAS  PubMed  Google Scholar 

  17. Boyd N, Martin L, Chavez S, Gunasekara A, Salleh A, Melnichouk O, Yaffe M, Friedenreich C, Minkin S, Bronskill M (2009) Breast-tissue composition and other risk factors for breast cancer in young women: a cross-sectional study. Lancet Oncol 10(6):569–580. doi:10.1016/S1470-2045(09)70078-6

    Article  PubMed  Google Scholar 

  18. Kelemen LE, Pankratz VS, Sellers TA, Brandt KR, Wang A, Janney C, Fredericksen ZS, Cerhan JR, Vachon CM (2008) Age-specific trends in mammographic density: the Minnesota Breast Cancer Family Study. Am J Epidemiol 167(9):1027–1036. doi:10.1093/aje/kwn063

    Article  PubMed  Google Scholar 

  19. Boyd N, Martin L, Stone J, Little L, Minkin S, Yaffe M (2002) A longitudinal study of the effects of menopause on mammographic features. Cancer Epidemiol Biomark Prev 11(10 Pt 1):1048–1053

    Google Scholar 

  20. Shepherd JA, Kerlikowske K, Ma L, Duewer F, Fan B, Wang J, Malkov S, Vittinghoff E, Cummings SR (2011) Volume of mammographic density and risk of breast cancer. Cancer Epidemiol Biomark Prev 20(7):1473–1482. doi:10.1158/1055-9965.EPI-10-1150

    Article  Google Scholar 

  21. Pawluczyk O, Augustine BJ, Yaffe MJ, Rico D, Yang J, Mawdsley GE, Boyd NF (2003) A volumetric method for estimation of breast density on digitized screen-film mammograms. Med Phys 30(3):352–364

    Article  PubMed  Google Scholar 

  22. Heine JJ, Carston MJ, Scott CG, Brandt KR, Wu FF, Pankratz VS, Sellers TA, Vachon CM (2008) An automated approach for estimation of breast density. Cancer Epidemiol Biomark Prev 17(11):3090–3097. doi:10.1158/1055-9965.EPI-08-0170

    Article  Google Scholar 

  23. Aitken Z, McCormack VA, Highnam RP, Martin L, Gunasekara A, Melnichouk O, Mawdsley G, Peressotti C, Yaffe M, Boyd NF, dos Santos Silva I (2010) Screen-film mammographic density and breast cancer risk: a comparison of the volumetric standard mammogram form and the interactive threshold measurement methods. Cancer Epidemiol Biomark Prev 19(2):418–428. doi:10.1158/1055-9965.EPI-09-1059

    Article  Google Scholar 

  24. Kallenberg MG, Lokate M, van Gils CH, Karssemeijer N (2011) Automatic breast density segmentation: an integration of different approaches. Phys Med Biol 56(9):2715–2729. doi:10.1088/0031-9155/56/9/005

    Article  PubMed  Google Scholar 

  25. Heine JJ, Scott CG, Sellers TA, Brandt KR, Serie DJ, Wu FF, Morton MJ, Schueler BA, Couch FJ, Olson JE, Pankratz VS, Vachon CM (2012) A novel automated mammographic density measure and breast cancer risk. J Natl Cancer Inst 104(13):1028–1037. doi:10.1093/jnci/djs254

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We thank the participating National Health Services Breast Screening Programme (NHSBSP) centres for their help with the retrieval of the mammographic films for the study participants. This study was funded by project Grants from Breast Cancer Campaign (2007MayPR23) and Cancer Research UK (G186/11 and C405/A14565). The funding bodies had no role in the design of the study; in the collection, analysis, and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. The development of ImageJ was supported by the second Joint Council Office (JCO) Career Development Grant (13302EG065). Jingmei Li is a UNESCO-L’Oréal International Fellow.

Author information

Author notes

  1. Ulla Sovio

    Present address: Department of Obstetrics and Gynaecology, University of Cambridge, Box 223, Level 2, The Rosie Hospital, Robinson Way, Cambridge, CB2 0SW, UK

Authors and Affiliations

  1. Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK

    Marta Cecilia Busana, Bianca L. De Stavola, Ulla Sovio & Isabel dos-Santos-Silva

  2. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, 171 77, Stockholm, Sweden

    Jingmei Li & Keith Humphreys

  3. Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK

    Sue Moss

Authors
  1. Marta Cecilia Busana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bianca L. De Stavola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ulla Sovio
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingmei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sue Moss
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Keith Humphreys
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Isabel dos-Santos-Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Isabel dos-Santos-Silva.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Busana, M.C., De Stavola, B.L., Sovio, U. et al. Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches. Cancer Causes Control 27, 481–491 (2016). https://doi.org/10.1007/s10552-016-0722-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10552-016-0722-9

Keywords

Search

Navigation