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Comparative estimation of percentage breast tissue density for digital mammography, digital breast tomosynthesis, and magnetic resonance imaging

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Abstract

Given the increasingly important role of breast density as an independent risk factor for breast cancer, and the variable breast imaging tests that potentially provide measures for density. We compared breast tissue density on digital mammography (FFDM), digital breast tomosynthesis (DBT), and magnetic resonance imaging (MRI) using semi-automated automated software. These three imaging modalities have not been previously directly compared for estimating breast tissue density. Following informed consent from all participating women, FFDM, DBT, and MRI were performed. Breast percentage density was calculated with semi-automated software, and compared, for all three imaging modalities. 48 patients (mean age, 41 years; range, 35–67 years) underwent FFDM, DBT, and MRI. Percent FFDM, DBT, and MRI breast density measures showed a positive linear correlation, (r = 0.95 for MRI and DBT, P < 0.0001; r = 0.97, P < 0.0001 for FFDM and DBT; r = 0.87 for FFDM and MRI). Linear regression analysis related to MRI and DBT had a high r 2 = 0.89 (95 % CI = 0.88–0.99, P < 0.001). FFDM overestimated breast density in 15.1 % in comparison to DBT and in 16.2 % in comparison to MRI, or conversely each of DBT and MRI underestimated density (relative to FFDM) by 15.1 or 16.2 %, respectively. Differences in percentage breast density between FFDM and DBT, and between FFDM and MRI, were highly significant (P < 0.0001). Differences in percentage breast density between DBT and MRI were not significant (P > 0.05). Breast density measures using FFDM, DBT, or MRI were generally well-correlated, although differences were noted between estimates when comparing FFDM and DBT, and for estimates comparing FFDM and MRI. No signficant differences in percentage density were observed when comparing DBT and MRI. Our work highlight that differences between FFDM, DBT, and MRI should be considered when measuring percentage breast density.

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References

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

    Article  PubMed  Google Scholar 

  2. Houssami N, Kerlikowske K (2012) The impact of breast density on breast cancer risk and breast screening. Curr Breast Cancer Rep. doi:10.1007/s12609-012-0070-z

  3. Khazen M, Warren RM, Boggis CR, Bryant EC, Reed S, Warsi I, Pointon LJ, Kwan-Lim GE, Thompson D, Eeles R, Easton D, Evans DG, Leach MO, Collaborators in the United Kingdom Medical Research Council Magnetic Resonance Imaging in Breast Screening (MARIBS) Study (2008) A pilot study of compositional analysis of the breast and estimation of breast mammographic density using three-dimensional T1-weighted magnetic resonance imaging. Cancer Epidemiol Biomarkers Prev 17:2268–2274

    Article  PubMed  Google Scholar 

  4. Byng JW, Yaffe MJ, Jong RA et al (1998) Analysis of mammographic density and breast cancer risk from digitized mammograms. Radiographics 18:1587–1598

    PubMed  CAS  Google Scholar 

  5. Tagliafico A, Calabrese M, Tagliafico G, Resmini E, Martinoli C, Rebora A, Colao A, Pivonello R, Ferone D (2011) Increased mammographic breast density in acromegaly: quantitative and qualitative assessment. Eur J Endocrinol 164:335–340

    Article  PubMed  CAS  Google Scholar 

  6. Tagliafico A, Tagliafico G, Tosto S, Chiesa F, Martinoli C, Derchi LE, Calabrese M (2009) Mammographic density estimation: comparison among BI-RADS categories, a semi-automated software and a fully automated one. Breast 18:35–40

    Article  PubMed  Google Scholar 

  7. Tagliafico A, Tagliafico G, Astengo D, Cavagnetto F, Rosasco R, Rescinito G, Monetti F, Calabrese M (2012) Mammographic density estimation: one-to-one comparison of digital mammography and digital breast tomosynthesis using fully automated software. Eur Radiol 22:1265–1270

    Article  PubMed  Google Scholar 

  8. Poon CS, Bronskill MJ, Henkelman RM, Boyd NF (1992) Quantitative magnetic resonance imaging parameters and their relationship to mammographic pattern. J Natl Cancer Inst 84:777–781

    Article  PubMed  CAS  Google Scholar 

  9. Graham SJ, Bronskill MJ, Byng JW, Yaffe MJ, Boyd NF (1996) Quantitative correlation of breast tissue parameters using magnetic resonance and X-ray mammography. Br J Cancer 73:162–168

    Article  PubMed  CAS  Google Scholar 

  10. Lee NA, Rusinek H, Weinreb J et al (1997) Fatty and fibroglandular tissue volumes in the breasts of women 20–83 years old: comparison of X-ray mammography and computer-assisted MR imaging. AJR Am J Roentgenol 168:501–506

    PubMed  CAS  Google Scholar 

  11. Klifa C, Carballido-Gamio J, Wilmes L et al (2004) Quantification of breast tissue index from MR data using fuzzy clustering. Conf Proc IEEE Eng Med Biol Soc 3:1667–1670

    PubMed  CAS  Google Scholar 

  12. Wei J, Chan HP, Helvie MA et al (2004) Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images. Med Phys 31:933–942

    Article  PubMed  Google Scholar 

  13. Reeder SB, McKenzie CA, Pineda AR, Yu H, Shimakawa A, Brau AC, Hargreaves BA, Gold GE, Brittain JH (2007) Water-fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging 25:644–652

    Article  PubMed  Google Scholar 

  14. Clendenen TV, Kim S, Moy L, Wan L, Rusinek H, Stanczyk FZ, Pike MC, Zeleniuch-Jacquotte A (2013) Magnetic resonance imaging (MRI) of hormone-induced breast changes in young premenopausal women. Magn Reson Imaging 31(1):1–9. doi:10.1016/j.mri.2012.06.022

    Article  PubMed  CAS  Google Scholar 

  15. Sardanelli F, Boetes C, Borisch B et al (2010) Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer 46:1296–1316

    Article  PubMed  Google Scholar 

  16. Jones J, Hunter D (1995) Consensus methods for medical and health services research. BMJ 311:376–380

    Article  PubMed  CAS  Google Scholar 

  17. Landis JR, Kock GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    Article  PubMed  CAS  Google Scholar 

  18. Bland JM, Altman DG (1997) Statistics notes: cronbach’s alpha. BMJ 314:572

    Article  PubMed  CAS  Google Scholar 

  19. Harvey JA, Bovbjerg VE (2004) Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology 230:29–41

    Article  PubMed  Google Scholar 

  20. Boyd NF, Guo H, Martin LJ et al (2007) Mammographic density and the risk and detection of breast cancer. N Engl J Med 356:227–232

    Article  PubMed  CAS  Google Scholar 

  21. Varghese JS, Thompson DJ, Michailidou K, Lindstr√∂m S, Turnbull C, Brown J, Leyland J, Warren RM, Luben RN, Loos RJ, Wareham NJ, Rommens J, Paterson AD, Martin LJ, Vachon CM, Scott CG, Atkinson EJ, Couch FJ, Apicella C, Southey MC, Stone J, Li J, Eriksson L, Czene K, Boyd NF, Hall P, Hopper JL, Tamimi RM, MODE Consortium, Rahman N, Easton DF (2012) Mammographic breast density and breast cancer: evidence of a shared genetic basis. Cancer Res 72:1478–1484

    Article  PubMed  CAS  Google Scholar 

  22. Thompson DJ, Leach MO, Kwan-Lim G, Gayther SA, Ramus SJ, Warsi I, Lennard F, Khazen M, Bryant E, Reed S, Boggis CR, Evans DG, Eeles RA, Easton DF, Warren RM (2009) Assessing the usefulness of a novel MRI-based breast density estimation algorithm in a cohort of women at high genetic risk of breast cancer: the UK MARIBS study. Breast Cancer Res 11(6):R80

    Article  PubMed  Google Scholar 

  23. Jordan CD, Daniel BL, Koch KM, Yu H, Conolly S, Hargreaves BA (2012) Subject-specific models of susceptibility-induced B(0) field variations in breast MRI. J Magn Reson Imaging. doi:10.1002/jmri.23762

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Authors and Affiliations

  1. Institute of Anatomy, Department of Experimental Medicine, University of Genoa, Largo Rosanna Benzi 8, 16132, Genoa, Italy

    Alberto Tagliafico

  2. Dipartimento di Chimica e Chimica Industriale (DCCI), University of Genoa, via Dodecaneso 31, 16146, Genoa, Italy

    Giulio Tagliafico

  3. Department of Radiology, University of Genoa, Largo Rosanna Benzi 8, 16138, Genoa, Italy

    Davide Astengo & Sonia Airaldi

  4. Department of Diagnostic Senology Ist Istituto Nazionale per la Ricerca sul Cancro, IRCCS Azienda Ospedaliera Universitaria San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy

    Massimo Calabrese

  5. Screening and Test Evaluation Program (STEP), School of Public Health, Sydney Medical School, University of Sydney, Sydney, Australia

    Nehmat Houssami

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  1. Alberto Tagliafico
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  2. Giulio Tagliafico
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  3. Davide Astengo
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  4. Sonia Airaldi
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  5. Massimo Calabrese
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  6. Nehmat Houssami
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Correspondence to Alberto Tagliafico.

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Tagliafico, A., Tagliafico, G., Astengo, D. et al. Comparative estimation of percentage breast tissue density for digital mammography, digital breast tomosynthesis, and magnetic resonance imaging. Breast Cancer Res Treat 138, 311–317 (2013). https://doi.org/10.1007/s10549-013-2419-z

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  • DOI: https://doi.org/10.1007/s10549-013-2419-z

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