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      Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study

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          Abstract

          Background

          Full-field digital mammography, which is gradually being introduced in most clinical and screening settings, produces two types of images: raw and processed. However, the extent to which mammographic density measurements, and their ability to predict breast cancer risk, vary according to type of image is not fully known.

          Methods

          We compared the performance of the semi-automated Cumulus method on digital raw, “analogue-like” raw and processed images, and the performance of a recently developed method - Laboratory for Breast Radiodensity Assessment (LIBRA) - on digital raw and processed images, in a case-control study (414 patients (cases) and 684 controls) by evaluating the extent to which their measurements were associated with breast cancer risk factors, and by comparing their ability to predict breast cancer risk.

          Results

          Valid Cumulus and LIBRA measurements were obtained from all available images, but the resulting distributions differed according to the method and type of image used. Both Cumulus and LIBRA percent density were inversely associated with age, body mass index (BMI), parity and postmenopausal status, regardless of type of image used. Cumulus percent density was strongly associated with breast cancer risk, but with the magnitude of the association slightly stronger for processed (risk increase per one SD increase in percent density (95 % CI): 1.55 (1.29, 1.85)) and “analogue-like” raw (1.52 (1.28, 1.80)) than for raw (1.35 (1.14, 1.60)) images. LIBRA percent density produced weaker associations with risk, albeit stronger for processed (1.32 (1.08, 1.61)) than raw images (1.17 (0.99, 1.37)). The percent density values yielded by the various density assessment/type of image combinations had similar ability to discriminate between patients and controls (area under the receiving operating curve values for percent density, age, BMI, parity and menopausal status combined ranged from 0.61 and 0.64).

          Conclusions

          The findings showed that Cumulus can be used to measure density on all types of digital images. They also indicate that LIBRA may provide a valid fully automated alternative to the more labour-intensive Cumulus. However, the same digital image type and assessment method should be used when examining mammographic density across populations, or longitudinal changes in density within a single population.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13058-016-0756-7) contains supplementary material, which is available to authorized users.

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          Most cited references13

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          Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers.

          Screening mammography is the best method to reduce mortality from breast cancer, yet some breast cancers cannot be detected by mammography. Cancers diagnosed after a negative mammogram are known as interval cancers. This study investigated whether mammographic breast density is related to the risk of interval cancer. Subjects were selected from women participating in mammographic screening from 1988 through 1993 in a large health maintenance organization based in Seattle, WA. Women were eligible for the study if they had been diagnosed with a first primary invasive breast cancer within 24 months of a screening mammogram and before a subsequent one. Interval cancer case subjects (n = 149) were women whose breast cancer occurred after a negative or benign mammographic assessment. Screen-detected control subjects (n = 388) were diagnosed after a positive screening mammogram. One radiologist, who was blinded to cancer status, assessed breast density by use of the American College of Radiology Breast Imaging Reporting and Data System. Mammographic sensitivity (i.e., the ability of mammography to detect a cancer) was 80% among women with predominantly fatty breasts but just 30% in women with extremely dense breasts. The odds ratio (OR) for interval cancer among women with extremely dense breasts was 6.14 (95% confidence interval [CI] = 1.95-19.4), compared with women with extremely fatty breasts, after adjustment for age at index mammogram, menopausal status, use of hormone replacement therapy, and body mass index. When only those interval cancer cases confirmed by retrospective review of index mammograms were considered, the OR increased to 9.47 (95% CI = 2.78-32.3). Mammographic breast density appears to be a major risk factor for interval cancer.
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            Mammographic density, breast cancer risk and risk prediction

            In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.
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              Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods

              Introduction Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM). Methods The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables. Results Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65). Conclusions Fully-automated methods are valid alternatives to the labour-intensive “gold standard” Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments. Electronic supplementary material The online version of this article (doi:10.1186/s13058-014-0439-1) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Marta.Busana@lshtm.ac.uk
                A.J.Eng@massey.ac.nz
                Rachel.Denholm@lshtm.ac.uk
                Mitchell.Dowsett@icr.ac.uk
                s.vinnicombe@dundee.ac.uk
                stevenallen@nhs.net
                +44 (0)20 7927 2113 , Isabel.Silva@lshtm.ac.uk
                Journal
                Breast Cancer Res
                Breast Cancer Res
                Breast Cancer Research : BCR
                BioMed Central (London )
                1465-5411
                1465-542X
                26 September 2016
                26 September 2016
                2016
                : 18
                : 96
                Affiliations
                [1 ]Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
                [2 ]Centre for Public Health Research, Massey University, Wellington, New Zealand
                [3 ]Academic Biochemistry, Royal Marsden Hospital, London, UK
                [4 ]Cancer Research, Ninewells Hospital Medical School, University of Dundee, Dundee, UK
                [5 ]Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK
                Author information
                http://orcid.org/0000-0002-6596-8798
                Article
                756
                10.1186/s13058-016-0756-7
                5037867
                27670914
                fa89fb00-0ff6-420c-a2cb-c322832b8404
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 24 February 2016
                : 8 September 2016
                Funding
                Funded by: Da Costa Foundation
                Award ID: No grant number assigned
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000289, Cancer Research UK;
                Award ID: C405/A14565
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Oncology & Radiotherapy
                digital mammography,mammographic density,breast density,breast cancer,image acquisition

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