Cancer Causes & Control

, Volume 27, Issue 4, pp 481–491 | Cite as

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

  • Marta Cecilia Busana
  • Bianca L. De Stavola
  • Ulla Sovio
  • Jingmei Li
  • Sue Moss
  • Keith Humphreys
  • Isabel dos-Santos-Silva
Original paper



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.


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.


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.


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.


Mammographic density Breast density Breast cancer Pre-menopausal 



Body mass index


Breast cancer




Confidence interval


Inter-quartile range


Mammographic density


Medio-lateral oblique


Percent density


Standard deviation

Supplementary material

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Supplementary material 1 (DOCX 54 kb)
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Supplementary material 2 (DOCX 17 kb)
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Supplementary material 3 (DOCX 13 kb)
10552_2016_722_MOESM4_ESM.docx (17 kb)
Supplementary material 4 (DOCX 17 kb)
10552_2016_722_MOESM5_ESM.docx (17 kb)
Supplementary material 5 (DOCX 16 kb)


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marta Cecilia Busana
    • 1
  • Bianca L. De Stavola
    • 1
  • Ulla Sovio
    • 1
    • 4
  • Jingmei Li
    • 2
  • Sue Moss
    • 3
  • Keith Humphreys
    • 2
  • Isabel dos-Santos-Silva
    • 1
  1. 1.Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonUK
  2. 2.Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
  3. 3.Centre for Cancer Prevention, Wolfson Institute of Preventive MedicineQueen Mary University of LondonLondonUK
  4. 4.Department of Obstetrics and GynaecologyUniversity of CambridgeCambridgeUK
  5. 5.Department of Non-Communicable Disease EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUK

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