Computerized Assessment of Tissue Composition on Digitized Mammograms

Chang, Yuan‐Hsiang; Wang, Xiaohui; Hardesty, Lara A.; Chang, Thomas S.; Poller, William R.; Good, Walter F.; Gur, David

doi:10.1016/s1076-6332(03)80459-2

Cited by 47 publications

(54 citation statements)

References 21 publications

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“…Automated measurement of breast density is being developed (23)(24)(25)(26) and will greatly improve the ability to assess breast density changes over time in large populations though these methods will need to be validated and become more widely available. Breast density could then be assessed prospectively in a reproducible fashion on large populations.…”

Section: Discussionmentioning

confidence: 99%

Increasing Mammographic Breast Density in Response to Hormone Placement Therapy and Breast Cancer Risk

Harvey¹

2004

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Section: Discussionmentioning

confidence: 99%

Increasing Mammographic Breast Density in Response to Hormone Placement Therapy and Breast Cancer Risk

Harvey¹

2004

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“…A number of automated or semi-automated methods have been proposed to identify mammographic density using either a threshold based method, such as those described above, or fractal analysis or other texturebased techniques (78)(79)(80)(81)(82)(83)(84)(85)(86). However, so far none of these methods have become widely used.…”

Section: Automated Methodsmentioning

confidence: 99%

Untitled

Ursin¹,

Qureshi²

2009

Nor J Epidemiol

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We describe mammographic density and how it is associated with breast cancer risk, what mammographic density represents biologically, as well as evidence that it is associated with breast cancer risk factors and is modifiable. Mammographic density has a large unused potential in mammographic screening programs. Currently mammographic density is being used as a biomarker or surrogate endpoint for breast cancer risk in a number of studies, and we discuss the rationale for doing this, as well as the challenges involved. A major challenge is the need for an automated method that can yield an even more precise estimate of the dense areas in the breast. Currently the most widely used methods are various computer-assisted methods. These are reader intensive, but so far the methods that yield the highest estimates for breast cancer risk. Once a robust automated method for assessing mammographic density or breast density is developed, this measure will probably become even more widely used, not just in epidemiology, but also in screening programs and in clinical practice.

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“…Different image features and machine learning based classifiers have been investigated and compared. Studies have reported higher correlation in assessing mammographic density between averaged visual and automated assessments (e.g., r= 0.87 [20]). However, mammograms are based on two-dimensional projection images.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2017

JABB

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Breast density is a well-recognized important breast cancer risk factor. However, accurately assessing breast density is difficult. Although the mammographic density is the most popular method to assess breast density in current clinical practice, it is not accurate and reliable due to the overlap of breast tissues and inter-reader variability. In order to more accurately assess breast density, we proposed a new non-invasive assessment technology based on the measurement of dielectric impedance spectrums of the breast regions. The objective of this study is to test the feasibility of this new technology through a unique phantom study. The phantom design is based on mixing two types of agar saline materials with two different levels of electrical conductivity corresponding to fat and fibro-glandular tissue conductivities. In the phantom design considerations, we assembled four breast phantoms to mimic four breast density categories defined by the Breast Imaging Reporting and Data System. The testing results showed that as the increase of the simulated "fibro-glandular" tissues inside the phantom, the measured electrical impedance values monotonically decreased. The testing results were consistent and reproducible at different positions between detection probes and breast phantom surface when considering and calibrating the systematic errors. Thus, this study demonstrated the feasibility of developing and applying a new dielectric impedance measurement technology and device to assess breast density, which is low-cost, non-invasive, non-hazard and easy-to-use. If successful in future tests, this new method has potential to assist better assessing breast cancer risk for developing an optimal personalized breast cancer screening paradigm.

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Computerized Assessment of Tissue Composition on Digitized Mammograms

Cited by 47 publications

References 21 publications

Increasing Mammographic Breast Density in Response to Hormone Placement Therapy and Breast Cancer Risk

Increasing Mammographic Breast Density in Response to Hormone Placement Therapy and Breast Cancer Risk

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