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Is mammographic density a marker of breast cancer phenotypes?

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Abstract

Purpose

To investigate the association between mammographic density (MD) phenotypes and both clinicopathologic features of breast cancer (BC) and tumor location.

Methods

MD was measured for 297 BC-affected females using qualitative (visual method) and quantitative (fully automated area-based method) approaches. Radiologists’ description, visible external markers, and surgical scar were used to establish the location of tumors. Binary logistic regression models were used to assess the association between MD phenotypes and BC clinicopathologic features.

Results

Categorical and numerical MD measures showed no association with clinicopathologic features of BC (p > 0.05). Participants with higher BI-RADS scores [(51–75% glandular) and (> 75% glandular)] (p < 0.001), and percent density (PD) categories [PD (21–49%) and PD ≥ 50%] (p = 0.01) were more likely to have tumors emanating from dense areas. Additionally, tumors were commonly found in dense regions of the breast among patients with higher medians of PD (p = 0.001), dense area (DA) (p = 0.02), and lower medians of non-dense area (NDA) (p < 0.001). Adjusted logistic regression models showed that high BI-RADS density (> 75% glandular) has an almost fivefold increased odds of tumors developing within dense areas (OR 4.99, 95% CI 0.93–25.9; p = 0.05. PD (OR 1.02, 95% CI 1–1.03, p = 0.002) and NDA (OR 0.99, 95% CI 0.991–0.997, p < 0.001) had very small effect on tumor location. Compared to tumors within non-dense areas, tumors in dense areas tended to exhibit human epidermal growth factor receptor 2 positive (p = 0.05) and carcinoma in situ (p = 0.01) characteristics.

Conclusion

MD shows no significant association with clinicopathologic features of BC. However, BC was more likely to originate from dense tissue, with tumors in dense regions having human epidermal growth receptor 2 positive and carcinoma in situ characteristics.

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References:s

  1. Boyd NF et al (2007) Mammographic density and the risk and detection of breast cancer. N Engl J Med 356(3):227–236

    CAS  PubMed  Google Scholar 

  2. Boyd NF et al (2011) Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res 13(6):223

    PubMed  PubMed Central  Google Scholar 

  3. Harris HR et al (2011) Body size across the life course, mammographic density, and risk of breast cancer. Am J Epidemiol 174(8):909–918

    PubMed  PubMed Central  Google Scholar 

  4. Boyd NF et al (2006) Mammographic density as a surrogate marker for the effects of hormone therapy on risk of breast cancer. Cancer Epidemiol Biomark Prev 15(5):961–966

    CAS  Google Scholar 

  5. Rice MS et al (2016) Mammographic density and breast cancer risk: a mediation analysis. Breast Cancer Res 18(1):94

    PubMed  PubMed Central  Google Scholar 

  6. Azam S et al (2018) Hormone replacement therapy, mammographic density, and breast cancer risk: a cohort study. Cancer Causes Control 29(6):495–505

    PubMed  PubMed Central  Google Scholar 

  7. 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

    Google Scholar 

  8. Boyd NF et al (2002) Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med 347(12):886–894

    PubMed  Google Scholar 

  9. Vachon CM et al (2007) Mammographic breast density as a general marker of breast cancer risk. Cancer Epidemiol Biomark Prev 16(1):43–49

    Google Scholar 

  10. Maskarinec G et al (2006) A longitudinal investigation of mammographic density: the multiethnic cohort. Cancer Epidemiol Biomark Prev 15(4):732–739

    Google Scholar 

  11. Roubidoux MA et al (2004) Invasive cancers detected after breast cancer screening yielded a negative result: relationship of mammographic density to tumor prognostic factors. Radiology 230(1):42–48

    PubMed  Google Scholar 

  12. Arpino G et al (2004) Infiltrating lobular carcinoma of the breast: tumor characteristics and clinical outcome. Breast cancer research : BCR 6(3):R149–R156

    PubMed  Google Scholar 

  13. Carey LA et al (2006) Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295(21):2492–2502

    CAS  PubMed  Google Scholar 

  14. Phipps AI et al (2010) Risk factors for ductal, lobular, and mixed ductal-lobular breast cancer in a screening population. Cancer Epidemiol Biomark Prev 19(6):1643–1654

    Google Scholar 

  15. Kanbayti IH et al (2019) Are mammographic density phenotypes associated with breast cancer treatment response and clinical outcomes? A systematic review and meta-analysis. The Breast 47:62–76

    PubMed  Google Scholar 

  16. Yamashita H et al (2004) Coexistence of HER2 over-expression and p53 protein accumulation is a strong prognostic molecular marker in breast cancer. Breast Cancer Res 6(1):R24–30

    CAS  PubMed  Google Scholar 

  17. Chung SR et al (2019) Prognostic factors predicting recurrence in invasive breast cancer: An analysis of radiological and clinicopathological factors. Asian J Surg 42(5):613–620

    PubMed  Google Scholar 

  18. Truong PT et al (2005) Lymphovascular invasion is associated with reduced locoregional control and survival in women with node-negative breast cancer treated with mastectomy and systemic therapy. J Am Coll Surg 200(6):912–921

    PubMed  Google Scholar 

  19. Song WJ et al (2012) The risk factors influencing between the early and late recurrence in systemic recurrent breast cancer. J Breast Cancer 15(2):218–223

    PubMed  PubMed Central  Google Scholar 

  20. Dunnwald LK, Rossing MA, Li CI (2007) Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients. Breast Cancer Res 9(1):R6

    PubMed  PubMed Central  Google Scholar 

  21. Mosly D et al (2018) Predictive markers of endocrine response in breast cancer. World journal of experimental medicine 8(1):1–7

    PubMed  PubMed Central  Google Scholar 

  22. Park CC et al (2009) High mammographic breast density is independent predictor of local but not distant recurrence after lumpectomy and radiotherapy for invasive breast cancer. Int J Radiat Oncol Biol Phys 73(1):75–79

    PubMed  Google Scholar 

  23. Elsamany S et al (2014) Prognostic value of mammographic breast density in patients with metastatic breast cancer. Med Oncol 31(8):96

    PubMed  Google Scholar 

  24. Boyd N et al (2002) A longitudinal study of the effects of menopause on mammographic features. Cancer Epidemiol Biomark Prev 11(10 Pt 1):1048–1053

    Google Scholar 

  25. Azam S et al (2019) Determinants of mammographic density change. JNCI Cancer Spectr 3(1):pkz004

    PubMed  PubMed Central  Google Scholar 

  26. Vachon CM et al (2013) Mammographic breast density response to aromatase inhibition. Clinical cancer research : an official journal of the American Association for Cancer Research 19(8):2144–2153

    CAS  Google Scholar 

  27. Ekpo EU et al (2016) Relationship between breast density and selective estrogen-receptor modulators, aromatase inhibitors, physical activity, and diet: a systematic review. Integr Cancer Ther 15(2):127–144

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Mullooly M, Gierach GL (2019) The potential for mammographic breast density change as a biosensor of adjuvant tamoxifen therapy adherence and response. JNCI Cancer Spectrum 2(4):72

    Google Scholar 

  29. Cuzick J et al (2004) Tamoxifen and breast density in women at increased risk of breast cancer. J Natl Cancer Inst 96(8):621–628

    CAS  PubMed  Google Scholar 

  30. Holm J et al (2015) Risk factors and tumor characteristics of interval cancers by mammographic density. J Clin Oncol 33(9):1030–1037

    PubMed  Google Scholar 

  31. Yaghjyan L et al (2011) Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to tumor characteristics. J Natl Cancer Inst 103(15):1179–1189

    PubMed  PubMed Central  Google Scholar 

  32. Ding J et al (2010) Mammographic density, estrogen receptor status and other breast cancer tumor characteristics. Breast J 16(3):279–289

    PubMed  Google Scholar 

  33. Eriksson L et al (2012) The influence of mammographic density on breast tumor characteristics. Breast Cancer Res Treat 134(2):859–866

    PubMed  Google Scholar 

  34. Heusinger K et al (2012) Association of mammographic density with hormone receptors in invasive breast cancers: results from a case-only study. Int J Cancer 131(11):2643–2649

    CAS  PubMed  Google Scholar 

  35. Shawky MS et al (2019) A review of the influence of mammographic density on breast cancer clinical and pathological phenotype. Breast Cancer Res Treat 177(2):251–276

    PubMed  Google Scholar 

  36. Hack CC et al (2013) Mammographic density and prediction of nodal status in breast cancer patients. Geburtshilfe Frauenheilkd 73(2):136–141

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Ghosh K et al (2008) Association of mammographic density with the pathology of subsequent breast cancer among postmenopausal women. Cancer Epidemiol Biomarkers Prev 17(4):872–879

    PubMed  PubMed Central  Google Scholar 

  38. Ekpo EU et al (2015) Breast composition: measurement and clinical use. Radiography 21(4):324–333

    Google Scholar 

  39. Palomares MR et al (2006) Mammographic density correlation with Gail model breast cancer risk estimates and component risk factors. Cancer Epidemiol Biomark Prev 15(7):1324–1330

    Google Scholar 

  40. Destounis S et al (2017) Qualitative versus quantitative mammographic breast density assessment: applications for the US and abroad. Diagnostics 7(2):30

    PubMed Central  Google Scholar 

  41. Sala E et al (2000) Size, node status and grade of breast tumours: association with mammographic parenchymal patterns. Eur Radiol 10(1):157–161

    CAS  PubMed  Google Scholar 

  42. Ziv E et al (2004) Mammographic density and estrogen receptor status of breast cancer. Cancer Epidemiol Biomark Prev 13(12):2090–2095

    CAS  Google Scholar 

  43. Astley SM et al (2018) A comparison of five methods of measuring mammographic density: a case-control study. Breast Cancer Res 20(1):10

    PubMed  PubMed Central  Google Scholar 

  44. Hinck L, Näthke I (2014) Changes in cell and tissue organization in cancer of the breast and colon. Curr Opin Cell Biol 26:87–95

    CAS  PubMed  Google Scholar 

  45. Boyd N et al (2018) The origins of breast cancer associated with mammographic density: a testable biological hypothesis. Breast Cancer Res 20(1):17

    PubMed  PubMed Central  Google Scholar 

  46. Aiello EJ et al (2005) Association between mammographic breast density and breast cancer tumor characteristics. Cancer Epidemiol Biomark Prev 14(3):662–668

    Google Scholar 

  47. Conroy SM et al (2011) Mammographic density and hormone receptor expression in breast cancer: the Multiethnic Cohort Study. Cancer Epidemiol 35(5):448–452

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Patani N, Martin LA, Dowsett M (2013) Biomarkers for the clinical management of breast cancer: international perspective. Int J Cancer 133(1):1–13

    CAS  PubMed  Google Scholar 

  49. Maskarinec G et al (2013) Mammographic density as a predictor of breast cancer survival: the Multiethnic Cohort. Breast Cancer Res 15(1):R7

    PubMed  PubMed Central  Google Scholar 

  50. Keller BM et al (2015) Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography. Breast Cancer Res 17:117

    PubMed  PubMed Central  Google Scholar 

  51. Busana MC et al (2016) Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study. Breast Cancer Res 18(1):96

    PubMed  PubMed Central  Google Scholar 

  52. Byng JW et al (1996) Symmetry of projection in the quantitative analysis of mammographic images. Eur J Cancer Prev 5(5):319–327

    CAS  PubMed  Google Scholar 

  53. Aitken Z et al (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

    Google Scholar 

  54. Maskarinec G et al (2017) Tumor characteristics and family history in relation to mammographic density and breast cancer: The French E3N cohort. Cancer Epidemiol 49:156–160

    PubMed  Google Scholar 

  55. Verheus M et al (2009) Mammographic density and epithelial histopathologic markers. BMC Cancer 9:182

    PubMed  PubMed Central  Google Scholar 

  56. Shaikh AJ et al (2018) Mammographic breast density and breast cancer molecular subtypes: the Kenyan-African aspect. Biomed Res Int 2018:10

    Google Scholar 

  57. Antoni S et al (2013) Is mammographic density differentially associated with breast cancer according to receptor status? A meta-analysis. Breast Cancer Res Treat 137(2):337–347

    PubMed  Google Scholar 

  58. Kerlikowske K et al (2010) Breast cancer risk by breast density, menopause, and postmenopausal hormone therapy use. J Clin Oncol 28(24):3830–3837

    PubMed  PubMed Central  Google Scholar 

  59. Guo YP et al (2001) Growth factors and stromal matrix proteins associated with mammographic densities. Cancer Epidemiol Biomark Prev 10(3):243–248

    CAS  Google Scholar 

  60. Hawes D et al (2006) Dense breast stromal tissue shows greatly increased concentration of breast epithelium but no increase in its proliferative activity. Breast Cancer Res 8(2):R24

    PubMed  PubMed Central  Google Scholar 

  61. Ursin G et al (2005) Greatly increased occurrence of breast cancers in areas of mammographically dense tissue. Breast Cancer Res 7(5):R605–R608

    PubMed  PubMed Central  Google Scholar 

  62. Chan S et al (2017) Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants. BMC Cancer 17(1):274

    PubMed  PubMed Central  Google Scholar 

  63. Pinto Pereira SM et al (2011) Localized fibroglandular tissue as a predictor of future tumor location within the breast. Cancer Epidemiol Biomark Prev 20(8):1718–1725

    Google Scholar 

  64. Pettersson A et al (2014) Mammographic density phenotypes and risk of breast cancer: a meta-analysis. JNCI 106(5):dju78

    Google Scholar 

  65. Greendale GA et al (2007) Serum prolactin levels are positively associated with mammographic density in postmenopausal women. Breast Cancer Res Treat 105(3):337–346

    CAS  PubMed  Google Scholar 

  66. Xu C, Langenheim JF, Chen WY (2012) Stromal-epithelial interactions modulate cross-talk between prolactin receptor and HER2/Neu in breast cancer. Breast Cancer Res Treat 134(1):157–169

    CAS  PubMed  Google Scholar 

  67. Boyd NF 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(9):670–675

    CAS  PubMed  Google Scholar 

  68. Kurebayashi J (2001) Biological and clinical significance of HER2 overexpression in breast cancer. Breast Cancer 8(1):45–51

    CAS  PubMed  Google Scholar 

  69. Rakha EA, Ellis IO (2010) Lobular breast carcinoma and its variants. Semin Diagn Pathol 27(1):49–61

    PubMed  Google Scholar 

  70. Habel LA et al (2004) Mammographic density and breast cancer after ductal carcinoma in situ. J Natl Cancer Inst 96(19):1467–1472

    PubMed  PubMed Central  Google Scholar 

  71. Bani MR et al (2009) Factors correlating with reexcision after breast-conserving therapy. Eur J Surg Oncol 35(1):32–37

    CAS  PubMed  Google Scholar 

  72. Song SE et al (2017) MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study. Acta Radiol 58(7):792–799

    PubMed  Google Scholar 

  73. Yamada T et al (2010) Radiologic-pathologic correlation of ductal carcinoma in situ. RadioGraphics 30(5):1183–1198

    PubMed  Google Scholar 

  74. Morra L et al (2015) Breast cancer: computer-aided detection with digital breast tomosynthesis. Radiology 277(1):56–63

    PubMed  Google Scholar 

  75. Baker JA et al (2003) Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD Systems for detecting architectural distortion. Am J Roentgenol 181(4):1083–1088

    Google Scholar 

  76. Broeders MJ et al (2003) Use of previous screening mammograms to identify features indicating cases that would have a possible gain in prognosis following earlier detection. Eur J Cancer 39(12):1770–1775

    CAS  PubMed  Google Scholar 

  77. Cuzick J et al (2011) Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case-control study. J Natl Cancer Inst 103(9):744–752

    CAS  PubMed  Google Scholar 

  78. Brisson J et al (2000) Tamoxifen and mammographic breast densities. Cancer Epidemiol Biomark Prev 9(9):911–915

    CAS  Google Scholar 

  79. Chen JH et al (2011) Reduction of breast density following tamoxifen treatment evaluated by 3-D MRI: preliminary study. Magn Reson Imaging 29(1):91–98

    CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Diagnostic Radiography Technology Department, Faculty of Applied Medical Sciences, King Abdul-Aziz University, Jeddah, Saudi Arabia

    Ibrahem H. Kanbayti

  2. Medical Image Optimisation and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

    Ibrahem H. Kanbayti, William I. D. Rae, Mark F. McEntee & Ernest U. Ekpo

  3. Department of Medicine Roinn na Sláinte, UG 12 Áras Watson | Brookfield Health Sciences, Cork, T12 AK54, Ireland

    Mark F. McEntee

  4. Orange Radiology, Laboratories and Research Centre, Calabar, Nigeria

    Ernest U. Ekpo

  5. King Saud Bin Abdulaziz University for Health Science-National Guard Health Affairs, Jeddah, Saudi Arabia

    Meteb Al-Foheidi

  6. Radiology Department, King Abdulaziz University Hospital, Jeddah, Saudi Arabia

    Sawsan Ashour & Smeera A. Turson

  7. Faculty of Health Science, University of Sydney, Cumberland Campus C42|75 East Street, Lidcombe, NSW, 2141, Australia

    Ibrahem H. Kanbayti

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  1. Ibrahem H. Kanbayti
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Kanbayti, I.H., Rae, W.I.D., McEntee, M.F. et al. Is mammographic density a marker of breast cancer phenotypes?. Cancer Causes Control 31, 749–765 (2020). https://doi.org/10.1007/s10552-020-01316-x

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