Local breast density assessment using reacquired mammographic images.

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.

[1]  L. Tabár,et al.  Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening , 2003, The Lancet.

[2]  Brian B. Avants,et al.  Explicit B-spline regularization in diffeomorphic image registration , 2013, Front. Neuroinform..

[3]  M. Daly,et al.  Dietary intake and breast density in high-risk women: a cross-sectional study , 2007, Breast Cancer Research.

[4]  J. Heine,et al.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors. , 2002, Academic radiology.

[5]  N. Boyd,et al.  Mammographic density and breast cancer risk: current understanding and future prospects , 2011, Breast Cancer Research.

[6]  Xavier Lladó,et al.  Revisiting Intensity-Based Image Registration Applied to Mammography , 2011, IEEE Transactions on Information Technology in Biomedicine.

[7]  M. Yaffe,et al.  Validation of a method for measuring the volumetric breast density from digital mammograms , 2010, Physics in medicine and biology.

[8]  Nico Karssemeijer,et al.  Volumetric breast density estimation from full-field digital mammograms , 2006, IEEE Trans. Medical Imaging.

[9]  Sung-Hyuk Cha.  Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[10]  Michael Waller,et al.  Effect of mammographic screening from age 40 years on breast cancer mortality at 10 years' follow-up: a randomised controlled trial , 2006, The Lancet.

[11]  Michael Brady,et al.  Mammographic Image Analysis , 1999, Computational Imaging and Vision.

[12]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[13]  Frank D Gilliland,et al.  Physical activity, body mass index, and mammographic density in postmenopausal breast cancer survivors. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  Daniel Rueckert,et al.  Diffeomorphic Registration Using B-Splines , 2006, MICCAI.

[15]  S. Duffy,et al.  Mammographic parenchymal patterns and mode of detection: implications for the breast screening programme , 1998, Journal of medical screening.

[16]  Nico Karssemeijer,et al.  Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study , 2014, IEEE Transactions on Medical Imaging.

[17]  Reyer Zwiggelaar,et al.  A volumetric approach to glandularity estimation in mammography: a feasibility study. , 2005, Physics in medicine and biology.

[18]  J. Wolfe.  Risk for breast cancer development determined by mammographic parenchymal pattern , 1976, Cancer.

[19]  Nico Karssemeijer,et al.  Robust breast composition measurement - Volpara™ , 2010 .

[20]  S. Cummings,et al.  Personalizing Mammography by Breast Density and Other Risk Factors for Breast Cancer: Analysis of Health Benefits and Cost-Effectiveness , 2011, Annals of Internal Medicine.

[21]  R. Jong,et al.  Breast Cancer: The Art and Science of Early Detection with Mammography , 2006 .

[22]  Olivier Alonzo-Proulx,et al.  Reliability of automated breast density measurements. , 2015, Radiology.

[23]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[24]  Hans Knutsson,et al.  Non-rigid Registration Using Morphons , 2005, SCIA.

[25]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[26]  Mark F McEntee,et al.  Mammographic Breast Density Assessment Using Automated Volumetric Software and Breast Imaging Reporting and Data System (BIRADS) Categorization by Expert Radiologists. , 2016, Academic radiology.

[27]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[28]  J. Heine,et al.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 2. Serial breast tissue change and related temporal influences. , 2002, Academic radiology.