Journal of Digital Imaging

, Volume 27, Issue 1, pp 152–160

Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts

Authors

  • Qian Yang
    • College of Life Information Science and Instrument EngineeringHangzhou Dianzi University
    • College of Life Information Science and Instrument EngineeringHangzhou Dianzi University
    • Department of Biomedical Engineering, College of Life Information Science and Instrument EngineeringHangzhou Dianzi University
  • Juan Zhang
    • Zhejiang Cancer Hospital
  • Guoliang Shao
    • Zhejiang Cancer Hospital
  • Chengjie Zhang
    • College of Life Information Science and Instrument EngineeringHangzhou Dianzi University
  • Bin Zheng
    • College of Life Information Science and Instrument EngineeringHangzhou Dianzi University
    • School of Electrical and Computer EngineeringUniversity of Oklahoma
Article

DOI: 10.1007/s10278-013-9617-4

Cite this article as:
Yang, Q., Li, L., Zhang, J. et al. J Digit Imaging (2014) 27: 152. doi:10.1007/s10278-013-9617-4

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.

Keywords

Breast diseasesComputer-aided diagnosis (CAD)MR mammographyContrast enhancementKinetic feature analysisAsymmetry

Copyright information

© Society for Imaging Informatics in Medicine 2013