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      Comparison of percent density from raw and processed full-field digital mammography data


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          Mammographic density has been established as a strong risk factor for breast cancer, primarily using digitized film mammograms. Full-field digital mammography (FFDM) is replacing film mammography, has different properties than film, and provides both raw and processed clinical display representation images. We evaluated and compared FFDM raw and processed breast density measures and their associations with breast cancer.


          A case-control study of 180 cases and 180 controls matched by age, postmenopausal hormone use, and screening history was conducted. Mammograms were acquired from a General Electric Senographe 2000D FFDM unit. Percent density (PD) was assessed for each FFDM representation using the operator-assisted Cumulus method. Reproducibility within image type ( n = 80) was assessed using Lin's concordance correlation coefficient (r c). Correlation of PD between image representations ( n = 360) was evaluated using Pearson's correlation coefficient (r) on the continuous measures and the weighted kappa statistic (κ) for quartiles. Conditional logistic regression was used to estimate odds ratios (ORs) for the PD and breast cancer associations for both image representations with 95% confidence intervals. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminatory accuracy.


          Percent density from the two representations provided similar intra-reader reproducibility (r c= 0.92 for raw and r c= 0.87 for processed images) and was correlated (r = 0.82 and κ = 0.64). When controlling for body mass index, the associations of quartiles of PD with breast cancer and discriminatory accuracy were similar for the raw (OR: 1.0 (ref.), 2.6 (1.2 to 5.4), 3.1 (1.4 to 6.8), 4.7 (2.1 to 10.6); AUC = 0.63) and processed representations (OR: 1.0 (ref.), 2.2 (1.1 to 4.1), 2.2 (1.1 to 4.4), 3.1 (1.5 to 6.6); AUC = 0.64).


          Percent density measured with an operator-assisted method from raw and processed FFDM images is reproducible and correlated. Both percent density measures provide similar associations with breast cancer.

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          Most cited references 10

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          Body size, mammographic density, and breast cancer risk.

          Greater weight and body mass index (BMI) are negatively correlated with mammographic density, a strong risk factor for breast cancer, and are associated with an increased risk of breast cancer in postmenopausal women, but with a reduced risk in premenopausal women. We have examined the associations of body size and mammographic density on breast cancer risk. We examined the associations of body size and the percentage of mammographic density at baseline with subsequent risk of breast cancer among 1,114 matched case-control pairs identified from three screening programs. The effect of each factor on risk of breast cancer was examined before and after adjustment for the other, using logistic regression. In all subjects, before adjustment for mammographic density, breast cancer risk in the highest quintile of BMI, compared with the lowest, was 1.04 [95% confidence interval (CI), 0.8-1.4]. BMI was associated positively with breast cancer risk in postmenopausal women, and negatively in premenopausal women. After adjustment for density, the risk associated with BMI in all subjects increased to 1.60 (95% CI, 1.2-2.2), and was positive in both menopausal groups. Adjustment for BMI increased breast cancer risk in women with 75% or greater density, compared with 0%, increased from 4.25 (95% CI, 1.6-11.1) to 5.86 (95% CI, 2.2-15.6). BMI and mammographic density are independent risk factors for breast cancer, and likely to operate through different pathways. The strong negative correlated between them will lead to underestimation of the effects on risk of either pathway if confounding is not controlled.
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            Mammographic density. Measurement of mammographic density

             Martin Yaffe (2008)
            Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations.
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              Full breast digital mammography with an amorphous silicon-based flat panel detector: physical characteristics of a clinical prototype.

              The physical characteristics of a clinical prototype amorphous silicon-based flat panel imager for full-breast digital mammography have been investigated. The imager employs a thin thallium doped CsI scintillator on an amorphous silicon matrix of detector elements with a pixel pitch of 100 microm. Objective criteria such as modulation transfer function (MTF), noise power spectrum, detective quantum efficiency (DQE), and noise equivalent quanta were employed for this evaluation. The presampling MTF was found to be 0.73, 0.42, and 0.28 at 2, 4, and 5 cycles/mm, respectively. The measured DQE of the current prototype utilizing a 28 kVp, Mo-Mo spectrum beam hardened with 4.5 cm Lucite is approximately 55% at close to zero spatial frequency at an exposure of 32.8 mR, and decreases to approximately 40% at a low exposure of 1.3 mR. Detector element nonuniformity and electronic gain variations were not significant after appropriate calibration and software corrections. The response of the imager was linear and did not exhibit signal saturation under tested exposure conditions.

                Author and article information

                Breast Cancer Res
                Breast Cancer Res
                Breast Cancer Research : BCR
                BioMed Central
                4 January 2013
                : 15
                : 1
                : R1
                [1 ]Mayo Clinic College of Medicine, Department of Health Sciences Research, 200 First Street SW, Rochester, MN 55905, USA
                [2 ]H. Lee Moffitt Cancer Center & Research Institute, Division of Population Sciences, 12902 Magnolia Drive, Tampa, FL 33612, USA
                Copyright ©2013 Vachon et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Research Article

                Oncology & Radiotherapy


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