Abstract
Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsy-proved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multi-feature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78 ± 0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.
Similar content being viewed by others
References
Jemal A, Siegel R, Xu J, Ward E: Cancer statistics, 2010. CA Cancer J Clin 60:277–300, 2010
Cady B, Michaelson JS: The life-sparing potential of mammographic screening. Cancer 91:1699–1703, 2001
Tabar L, Vitak B, Chen HH, et al: Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer 91:1724–1731, 2001
Smith RA, Cokkindes V, Brooks D, et al: Cancer screening in the United States, 2011. CA Cancer J Clin 61:8–30, 2011
Mandelson MT, Oestreicher N, Porter PL, et al: Breast density as a predictor of mammographic detection: comparison of interval-and screen-detected cancers. J Natl Cancer Inst 92:1081–1087, 2000
Kolb TM, Lichy J, Newhouse JH: Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology 225:165–175, 2002
Berg WA, Gutierrez L, NessAiver MS, et al: Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology 233:830–849, 2004
Saslow D, Boetes C, Burke W, et al: American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 57:75–89, 2007
Leach MO, Boggis CR, Dixon AK, et al: Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS). Lancet 365:1769–1778, 2005
Kuhl CK, Schrading S, Leutner CC, et al: Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. J Clin Oncol 23:8469–8476, 2005
Sardanelli F, Podo F, D’Agnolo G, et al: Multicenter comparative multimodality surveillance of women at genetic-familial high risk for breast cancer (HIBCRIT study): interim results. Radiology 242:698–715, 2007
Kriege M, Brekelmans CT, Boetes C, et al: Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N Engl J Med 351:427–437, 2004
Kriege M, Brekelmans CT, Boetes C, et al: Differences between first and subsequent rounds of the MRISC breast cancer screening program for women with a familial or genetic predisposition. Cancer 106:2318–2326, 2006
Warner E: Intensive radiologic surveillance: a focus on the psychological issues. Ann Oncol 15:143–147, 2004
Berg WA, Blume JD, Adams AM, et al: Reasons women at elevated risk of breast cancer refuse breast MR imaging screening: ACRIN 6666. Radiology 254:79–87, 2010
Gibbs P, Turnbull LW: Textural analysis of contrast-enhanced MR image of the breast. Magn Reson Med 50:92–98, 2003
Chen W, Giger ML, Bick U, Newstead GM: Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys 33:2878–2887, 2006
Meinel LA, Stolpen AH, Berbaun KS, Reinhardt JM: Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. J Magn Reson Imaging 25:89–95, 2007
Williams TC, DeMartini WB, Partridge SC, et al: Breast MR imaging: computer-aided evaluation program for discriminating benign from malignant lesions. Radiology 244:94–103, 2007
Bhooshan N, Giger ML, Jansen SA, Li H, Lan L, Newstead GM: Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology 254:680–690, 2010
Yuan Y, Giger ML, Li H, Bhooshan N, Sennett CA: Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol 17:1158–1167, 2010
King V, Brooks JD, Bernstein JL, Reiner AS, Pike MC, Morris EA: Background parenchymal enhancement at breast MR imaging and breast cancer risk. Radiology 260:50–60, 2011
Uematsu T, Kasami M, Watanabe J: Does the degree of background enhancement in breast MRI affect the detection and staging of breast cancer? Eur Radiol 21:2261–2267, 2011
Harvey J, Bovbjerg VE: Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology 230:29–41, 2004
Kopans DB: Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk. Radiology 246:348–353, 2008
Scutt D, Lancaster GA, Manning JT: Breast asymmetry and predisposition to breast cancer. Breast Cancer Res 8:R14, 2006. doi:10.1186/bcr1388
Kim M, Wu G, Shen D: Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching. Med Phys 39:353–366, 2012
Filev P, Hadjiiski L, Sahiner B, Chan HP, Helvie MA: Comparison of similarity measures for the task of template matching of masses on serial mammograms. Med Phys 32:515–529, 2005
Wang XH, Park SC, Zheng B: Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment. Phys Med Biol 54:949–961, 2009
Kahn CE, Robert LM, Wang K, et al: Construction of a Bayesian network for mammographic diagnosis of breast cancer. Comput. Biol. Med. 27:1929–1940, 1997
Wang X, Zheng B, Good WF, King JK, Chang Y: Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network. Int J Med Informatics 54:115–126, 1999
Wang X, Lederman D, Tan J, Zheng B: Computer-aided detection: the impact of machine learning classifier and image feature selection on scheme performance. Int J Intell Inf Process 1:30–40, 2010
Mitchell TM: Machine learning. McGraw-Hill, Boston, MA, 1997
Cheng J: BN Power constructor. University of Alberta, Edmonton, Alberta, Canada, 2001. Available from http://www.cs.ualberta.ca/~jcheng/bnsoft.htm
Metz CE: ROCKIT 0.9B beta version. University of Chicago, 1998. Available from http://www-radiology.uchicago.edu/krl/
Wang X, Li L, Xu W, Liu W, Lederman D, Zheng B: Improving performance of computer-aided detection of subtle breast masses using an adaptive cueing method. Phys Med Biol 57:561–575, 2012
Zheng B, Sumkin JH, Zuley ML, Wang X, Klym AH, Gur D: Bilateral mammographic density asymmetry and breast cancer risk: a preliminary assessment. Eur J Radiol. 81:3222–3228, 2012
Acknowledgments
This work is supported in part by grants from the National Natural Science Foundation of China (61271063), 973 Program (2013CB329502), National Distinguished Young Research Scientist Award (60788101), and Grant CA160205 from the National Cancer Institute, National Institutes of Health, USA.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, Q., Li, L., Zhang, J. et al. Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts. J Digit Imaging 27, 152–160 (2014). https://doi.org/10.1007/s10278-013-9617-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10278-013-9617-4