Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer. (January 2018)

• Record Type: Journal Article
• Authors: Duffy, Stephen W.; Morrish, Oliver W.E.; Allgood, Prue C.; Black, Richard; Gillan, Maureen G.C.; Willsher, Paula; Cooke, Julie; Duncan, Karen A.; Michell, Michael J.; Dobson, Hilary M.; Maroni, Roberta; Lim, Yit Y.; Purushothaman, Hema N.; Suaris, Tamara; Astley, Susan M.; Young, Kenneth C.; Tucker, Lorraine; Gilbert, Fiona J.
• Is Part Of: European journal of cancer. Volume 88(2018)
• Page Start: 48
• Page End: 56
• Publication Date: 2018-01
• Subjects: Breast cancer screening -- Breast cancer risk -- Mammographic density -- Automated volumetric method -- Quantra -- Volpara -- Digital breast tomosynthesis -- Fibroglandular volume; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2017.10.022
• Languages: English
• ISSNs: 0959-8049

Abstract

Abstract: Background: Mammographic density has been shown to be a strong independent predictor of breast cancer and a causative factor in reducing the sensitivity of mammography. There remain questions as to the use of mammographic density information in the context of screening and risk management, and of the association with cancer in populations known to be at increased risk of breast cancer. Aim: To assess the association of breast density with presence of cancer by measuring mammographic density visually as a percentage, and with two automated volumetric methods, Quantra™ and VolparaDensity™. Methods: The TOMosynthesis with digital MammographY (TOMMY) study of digital breast tomosynthesis in the Breast Screening Programme of the National Health Service (NHS) of the United Kingdom (UK) included 6020 breast screening assessment cases (of whom 1158 had breast cancer) and 1040 screened women with a family history of breast cancer (of whom two had breast cancer). We assessed the association of each measure with breast cancer risk in these populations at enhanced risk, using logistic regression adjusted for age and total breast volume as a surrogate for body mass index (BMI). Results: All density measures showed a positive association with presence of cancer and all declined with age. The strongest effect was seen with Volpara absolute density, with a significant 3% (95% CI 1–5%) increase in risk per 10 cm 3 of dense tissue. The effect of Volpara volumetric density on risk was stronger for large and grade 3 tumours. Conclusions: Automated absolute breast density is a predictor of breast cancer risk in populations at enhanced risk due to either positive mammographic findings or family history. In the screening context, density could be a trigger for more intensive imaging. Highlights: Three different breast density measures compared: visual, Quantra and Volpara. All density measures showed a positive association with presence of cancer. The strongest effect was seen with Volpara absolute density measure. A stronger effect of Volpara density on risk was observed for large and grade 3 cancers.