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Local breast density assessment using reacquired mammographic images

      Abstract

      Purpose

      The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast.

      Materials and methods

      We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures.

      Results

      Global measures showed a high correlation (breast volume R = 0.99, volume of glandular tissue R = 0.94, and volumetric breast density R = 0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution.

      Conclusions

      This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions.

      Keywords

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      References

      1. International Agency for Research on Cancer, Globocan 2012.
        2016
        • Jong R.
        Breast cancer: the art and science of early detection with mammography.
        Am. J. Roentgenol. 2006; 187: W142
        • Tabar L.
        • Yen M.F.
        • Vitak B.
        • Chen H.H.
        • Smith R.A.
        • Duffy S.W.
        Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening.
        Lancet. 2003; 361: 1405-1410
        • Moss S.M.
        • Cuckle H.
        • Evans A.
        • Johns L.
        • Waller M.
        • Bobrow L.
        Effect of mammographic screening from age 40 years on breast cancer mortality at 10 years’ follow-up: a randomised controlled trial.
        Lancet. 2006; 368: 2053-2060
        • Schousboe J.T.
        • Kerlikowske K.
        • Loh A.
        • Cummings S.R.
        Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness.
        Ann. Intern. Med. 2011; 155: 10-20
        • Tseng M.
        • Byrne C.
        • Evers K.A.
        • Daly M.B.
        Dietary intake and breast density in high-risk women: a cross-sectional study.
        Breast Cancer Res. 2007; 9: 1
        • Irwin M.L.
        • Aiello E.J.
        • McTiernan A.
        • Bernstein L.
        • Gilliland F.D.
        • Baumgartner R.N.
        • Baumgartner K.B.
        • Ballard-Barbash R.
        Physical activity, body mass index, and mammographic density in postmenopausal breast cancer survivors.
        J. Clin. Oncol. 2007; 25: 1061-1066
        • Boyd N.
        • Martin L.
        • Yaffe M.
        • Minkin S.
        Mammographic density and breast cancer risk: current understanding and future prospects.
        Breast Cancer Res. 2011; 13: 223
        • Heine J.J.
        • Malhotra P.
        Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography: Part 1. Tissue and related risk factors.
        Acad. Radiol. 2002; 9: 298-316
        • Sala E.
        • Warren R.
        • McCann J.
        • Duffy S.
        • Day N.
        • Luben R.
        Mammographic parenchymal patterns and mode of detection: implications for the breast screening programme.
        J. Med. Screen. 1998; 5: 207-212
        • Wolfe J.N.
        Risk for breast cancer development determined by mammographic parenchymal pattern.
        Cancer. 1976; 37: 2486-2492
        • Heine J.J.
        • Malhotra P.
        Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography: Part 2. Serial breast tissue change and related temporal influences.
        Acad. Radiol. 2002; 9: 317-335
        • Highnam R.
        • Brady M.
        • Yaffe M.
        • Karssemeijer N.
        • Harvey J.
        Robust Breast Composition Measurement-Volpara™.
        IWDM Digital Mammography 2010. Springer, 2010: 342-349
        • Gubern-Mérida A.
        • Kallenberg M.
        • Platel B.
        • Mann R.
        • Martí R.
        • Karssemeijer N.
        Volumetric breast density estimation from full-field digital mammograms: a validation study.
        PLOS ONE. 2014; 9: e85952
        • Alonzo-Proulx O.
        • Packard N.
        • Boone J.
        • Al-Mayah A.
        • Brock K.
        • Shen S.
        • Yaffe M.
        Validation of a method for measuring the volumetric breast density from digital mammograms.
        Phys. Med. Biol. 2010; 55: 3027
        • Alonzo-Proulx O.
        • Mawdsley G.
        • Patrie J.
        • Yaffe M.
        • Harvey J.
        Reliability of automated breast density measurements.
        Radiology. 2015; 275: 366-376
      2. EUREF European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis.
        4th ed. 2006
        • Engeland S.V.
        • Snoeren P.
        • Huisman H.
        • Boetes C.
        • Karssemeijer N.
        Volumetric breast density estimation from full-field digital mammograms.
        IEEE Trans. Med. Imaging. 2006; 25: 273-282
        • Highnam R.
        • Brady M.
        Mammographic Image Analysis.
        Springer Science & Business Media, 1999
        • Damases C.
        • Brennan P.
        • Mello-Thoms C.
        • McEntee M.
        Mammographic breast density assessment using automated volumetric software and breast imaging reporting and data system (BIRADS) categorization by expert radiologist.
        Acad. Radiol. 2015; 23: 70-77
        • Diez Y.
        • Oliver A.
        • Lladó X.
        • Freixenet J.
        • Martí J.
        • Vilanova J.C.
        • Martí R.
        Revisiting intensity-based image registration applied to mammography.
        IEEE Trans. Inf. Technol. Biomed. 2011; 15: 716-725
        • Wrangsjö A.
        • Pettersson J.
        • Knutsson H.
        Non-rigid registration using morphons.
        Image Analysis. Springer, 2005: 501-510
        • Tustison N.J.
        • Avants B.B.
        Explicit B-spline regularization in diffeomorphic image registration.
        Front. Neuroinform. 2013; 7: 39
        • Rueckert D.
        • Aljabar P.
        • Heckemann R.
        • Hajnal J.
        • Hammers A.
        Diffeomorphic registration using B-splines.
        Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006. Springer, 2006: 702-709
        • Avants B.
        • Epstein C.
        • Grossman M.
        • Gee J.
        Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.
        Med. Image Anal. 2008; 12: 26-41
        • Ibanez L.
        • Schroeder W.
        • Ng L.
        • Cates J.
        The ITK Software Guide.
        Kitware, Inc. and The Insight Software Consortium, 2003
        • R Core Team
        R: A Language and Environment for Statistical Computing.
        R Foundation for Statistical Computing, Vienna, Austria2014
        • Swain M.J.
        • Ballard D.H.
        Color indexing.
        Int. J. Comput. Vis. 1991; 7: 11-32
        • Cha S.H.
        Comprehensive survey on distance/similarity measures between probability density functions.
        Int. J. Math. Models Methods Appl. Sci. 2007; 4: 300-307
        • Vercauteren T.
        • Pennec X.
        • Perchant A.
        • Ayache N.
        Diffeomorphic demons: efficient non-parametric image registration.
        NeuroImage. 2009; 45: 61-62
        • Blot L.
        • Zwiggelaar R.
        A volumetric approach to glandularity estimation in mammography: a feasibility study.
        Phys. Med. Biol. 2005; 50: 695-708