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Measurement and Clinical Use of Breast Density

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8539))

Abstract

Breast density is loosely defined as the amount of fibroglandular tissue in the breast compared to the total amount of breast tissue. In this review paper we consider the three ways of describing breast density as seen on a mammogram: pattern-based, area-based and volumetric-based and explain the rationale for each along with detailing the various ways of estimating each of them (visual, semi-automated, and fully automated). We also consider the use of other imaging modalities of estimating breast density, including CT. Clinically, breast density has now moved from being a controversial, even derided subject to one which is widely accepted with an expanding number of clinical uses. It is proven that a woman’s breast density is a strong predictor of the failure of mammographic screening to detect breast cancer and thus can be used to indicate where alternate modalities might be considered. It is proven that breast density is a strong predictor of the risk of developing breast cancer and thus can be used to start to consider tailored screening programs. We review the current widely known clinical uses along with the lesser known uses, such as assessing the benefits of chemoprevention and generating more accurate radiation dose estimates. Breast density is becoming an increasingly important clinical tool; there is an increasing need for accurate and consistent density measures along with an understanding of how the various measures compare.

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Ng, KH., Lau, S. (2014). Measurement and Clinical Use of Breast Density. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-07887-8_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07886-1

  • Online ISBN: 978-3-319-07887-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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