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      Breast cancer detection across dense and non-dense breasts: Markers of diagnostic confidence and efficacy

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          Abstract

          Background

          The impact of radiologists’ characteristics has become a major focus of recent research. However, the markers of diagnostic efficacy and confidence in dense and non-dense breasts are poorly understood.

          Purpose

          This study aims to assess the relationship between radiologists’ characteristics and diagnostic performance across dense and non-dense breasts.

          Materials and methods

          Radiologists specialising in breast imaging ( n = 128) who had 0.5–40 (13±10.6) years of experience reading mammograms were recruited. Participants independently interpreted a test set containing 60 digital mammograms (40 normal and 20 abnormal) with similarly distributed breast densities. Diagnostic performance measures were analysed via Jamovi software (version 1.6.22).

          Results

          In dense breasts, breast-imaging fellowship completion significantly improved specificity ( p = 0.004), location sensitivity ( p = 0.01) and the area under the curve (AUC) of the receiver operating characteristic ( p = 0.03). Only participation in BreastScreen reading significantly improved all performance metrics: specificity ( p = 0.04), sensitivity ( p = 0.005), location sensitivity ( p < 0.001) and AUC ( p < 0.001). Reading > 100 mammograms weekly significantly improved sensitivity ( p = 0.03), location sensitivity ( p = 0.001), and AUC ( p = 0.03).In non-dense breasts, breast fellowship completion significantly improved sensitivity ( p = 0.02), location sensitivity ( p = 0.04) and AUC ( p = 0.002). Participation in BreastScreen reading and reading > 100 mammograms weekly significantly improved only sensitivity ( p = 0.002 and p = 0.003, respectively) and location sensitivity ( p < 0.001 and p < 0.001, respectively).

          Conclusion

          Participating in screening programs, breast fellowships and reading > 100 mammograms weekly are important indicators of the diagnostic performance of radiologists across dense and non-dense breasts. In dense breasts, optimal performance resulted from participation in a breast screening program.

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          Most cited references30

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          Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

          Estimates of the worldwide incidence and mortality from 27 major cancers and for all cancers combined for 2012 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. We review the sources and methods used in compiling the national cancer incidence and mortality estimates, and briefly describe the key results by cancer site and in 20 large "areas" of the world. Overall, there were 14.1 million new cases and 8.2 million deaths in 2012. The most commonly diagnosed cancers were lung (1.82 million), breast (1.67 million), and colorectal (1.36 million); the most common causes of cancer death were lung cancer (1.6 million deaths), liver cancer (745,000 deaths), and stomach cancer (723,000 deaths). © 2014 UICC.
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            Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.

            Mammographic features are associated with breast cancer risk, but estimates of the strength of the association vary markedly between studies, and it is uncertain whether the association is modified by other risk factors. We conducted a systematic review and meta-analysis of publications on mammographic patterns in relation to breast cancer risk. Random effects models were used to combine study-specific relative risks. Aggregate data for > 14,000 cases and 226,000 noncases from 42 studies were included. Associations were consistent in studies conducted in the general population but were highly heterogeneous in symptomatic populations. They were much stronger for percentage density than for Wolfe grade or Breast Imaging Reporting and Data System classification and were 20% to 30% stronger in studies of incident than of prevalent cancer. No differences were observed by age/menopausal status at mammography or by ethnicity. For percentage density measured using prediagnostic mammograms, combined relative risks of incident breast cancer in the general population were 1.79 (95% confidence interval, 1.48-2.16), 2.11 (1.70-2.63), 2.92 (2.49-3.42), and 4.64 (3.64-5.91) for categories 5% to 24%, 25% to 49%, 50% to 74%, and > or = 75% relative to < 5%. This association remained strong after excluding cancers diagnosed in the first-year postmammography. This review explains some of the heterogeneity in associations of breast density with breast cancer risk and shows that, in well-conducted studies, this is one of the strongest risk factors for breast cancer. It also refutes the suggestion that the association is an artifact of masking bias or that it is only present in a restricted age range.
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              Prevalence of mammographically dense breasts in the United States.

              National legislation is under consideration that would require women with mammographically dense breasts to be informed of their breast density and encouraged to discuss supplemental breast cancer screening with their health care providers. The number of US women potentially affected by this legislation is unknown.
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                Author and article information

                Journal
                Acta Radiol Open
                Acta Radiol Open
                sparr
                ARR
                Acta Radiologica Open
                SAGE Publications (Sage UK: London, England )
                2058-4601
                January 2022
                29 January 2022
                : 11
                : 1
                : 20584601211072279
                Affiliations
                [1 ]Medical Image Optimisation and Perception Group, Discipline of Medical Imaging Science, Faculty of Medicine and Health, Ringgold RinggoldID:4334, universityThe University of Sydney; , Sydney, NSW, Australia
                [2 ]Department of Radiological Sciences, Faculty of Applied Medical Sciences, universityKing Khalid University; , Saudi Arabia
                [3 ]Discipline of Diagnostic Radiography, Ringgold RinggoldID:8795, universityUniversity College Cork; , Cork, Ireland
                [4 ]Orange Radiology, universityLaboratories and Research Centre; , Calabar, Nigeria
                Author notes
                [*]Ibrahim Hadadi, Medical Image Optimisation and Perception Group, Faculty of Medicine and Health, The University of Sydney, Susan Wakil Health Building, Camperdown Campus, Western Avenue, Sydney, NSW 2006, Australia. Email: ihad5571@ 123456uni.sydney.edu.au
                Author information
                https://orcid.org/0000-0002-2517-7465
                https://orcid.org/0000-0001-7045-2190
                Article
                10.1177_20584601211072279
                10.1177/20584601211072279
                8801646
                35111337
                5cfb15fb-56cf-4876-8885-cf58e23eb1f1
                © The Author(s) 2022

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 23 September 2021
                : 17 December 2021
                Categories
                Original Article
                Custom metadata
                ts10

                mammogram readers,observer performance,mammography,breast density,breast cancer

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