Skip to main content

Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

Annals of Biomedical Engineering volume 46, pages 1419–1431 (2018)Cite this article

Abstract

Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron based machine learning classifiers that integrate with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. When applying CAD to DES and LE images with original segmentation, areas under ROC curves (AUC) were 0.759 ± 0.053 and 0.753 ± 0.047, respectively. After mapping the mass regions optimally segmented on DES images to LE images, AUC significantly increased to 0.848 ± 0.038 (p < 0.01). Study demonstrated that DES images eliminated overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. The study demonstrated that applying a novel approach to optimally map mass region segmented from DES images to LE images enabled CAD to yield significantly improved performance.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

References

  1. Aghaei, F., M. Tan, A. B. Hollingsworth, and B. Zheng. Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy. J. Magn. Reson. Imaging 44:1099–1106, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Baltzer, P. A. T., M. Benndorf, M. Dietzel, M. Gajda, I. B. Runnebaum, and W. A. Kaiser. False-positive findings at contrast-enhanced breast MRI: a BI-RADS descriptor study. Am. J. Roentgenol. 194:1658–1663, 2010.

    Article  Google Scholar 

  3. Berg, W. A., Z. Zhang, D. Lehrer, and R. A. Jong. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. J. Am. Med. Assoc. 307:1394–1404, 2012.

    Article  CAS  Google Scholar 

  4. Brodersen, J., and V. Siersma. Long-term psychosocial consequences of false-positive mammography screening. Ann. Fam. Med. 11:106–115, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Carney, P. A., D. L. Miglioretti, B. C. Yankaskas, K. Kerlikowske, R. Rosenberg, C. M. Rutter, B. M. Geller, L. A. Abraham, S. H. Taplin, M. M. Dignan, and R. Gary Cutter. Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann. Intern. Med. 138:168–175, 2003.

    Article  PubMed  Google Scholar 

  6. Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16:321–357, 2002.

    Article  Google Scholar 

  7. Danala, G., T. Thai, C. C. Gunderson, K. M. Moxley, K. Moore, R. S. Mannel, H. Liu, B. Zheng, and Y. Qiu. Applying quantitative CT image feature analysis to predict response of ovarian cancer patients to chemotherapy. Acad. Radiol. 24:1233–1239, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Dromain, C., C. Balleyguier, S. Muller, M.-C. Mathieu, F. Rochard, P. Opolon, and R. Sigal. Evaluation of tumor angiogenesis of breast carcinoma using contrast-enhanced digital mammography. Am. J. Roentgenol. 187:W528–W537, 2006.

    Article  Google Scholar 

  9. Eltonsy, N. H., G. D. Tourassi, A. S. Elmaghraby, and S. Member. A concentric morphology model for the detection of masses in mammography. IEEE Trans. Med. Imaging 26:880–889, 2007.

    Article  PubMed  Google Scholar 

  10. Gur, D., J. Stalder, and L. A. Hardesty. CAD performance on sequentially ascertained mammographic examinations of masses: an assessment. Radiology 233:418–423, 2004.

    Article  PubMed  Google Scholar 

  11. Kriege, M., C. T. M. Brekelmans, C. Boetes, and P. E. Besnard. 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.

    Article  CAS  PubMed  Google Scholar 

  12. Laya, M. B., E. B. Larson, S. H. Taplin, and E. White. Effect of estrogen replacement therapy on the specificity and sensitivity of screening mammography. J. Natl. Cancer Inst. 88:643–649, 1996.

    Article  CAS  PubMed  Google Scholar 

  13. Lee, A. Y., D. J. Wisner, S. Aminololama-Shakeri, V. A. Arasu, S. A. Feig, J. Hargreaves, H. Ojeda-Fournier, L. W. Bassett, C. J. Wells, J. De Guzman, C. I. Flowers, J. E. Campbell, S. L. Elson, H. Retallack, and B. N. Joe. Inter-reader variability in the use of BI-RADS descriptors for suspicious findings on diagnostic mammography: a multi-institution study of 10 academic radiologists. Acad. Radiol. 24:60–66, 2017.

    Article  PubMed  Google Scholar 

  14. Li, Q., and K. Doi. Reduction of bias and variance for evaluation of computer-aided diagnostic schemes. Med. Phys. 33:868–875, 2006.

    Article  PubMed  Google Scholar 

  15. Lu, W., Z. Li, and J. Chu. A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning. Comput. Biol. Med. 83:157–165, 2017.

    Article  PubMed  Google Scholar 

  16. Mandelson, T. M. Breast density as a predictor of breast cancer risk. J. Natl. Cancer Inst. 12:1081–1087, 2000.

    Article  Google Scholar 

  17. Oliver, A., J. Freixenet, J. Martí, E. Pérez, J. Pont, E. R. E. Denton, and R. Zwiggelaar. A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14:87–110, 2010.

    Article  PubMed  Google Scholar 

  18. Patel, B. K., S. Ranjbar, T. Wu, B. A. Pockaj, J. Li, N. Zhang, M. Lobbes, B. Zhang, and J. R. Mitchell. Computer-aided diagnosis of contrast-enhanced spectral mammography: a feasibility study. Eur. J. Radiol. 98:207–213, 2018.

    Article  PubMed  Google Scholar 

  19. Peer, P. G. M., A. L. M. Verbeek, H. Straatman, J. H. C. L. Hendriks, and R. Holland. Age-specific sensitivities of mammographic screening for breast cancer. Breast Cancer Res. 38:153–160, 1996.

    Article  CAS  Google Scholar 

  20. Qiu, Y., S. Yan, R. R. Gundreddy, Y. Wang, S. Cheng, H. Liu, and B. Zheng. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J. Xray. Sci. Technol. 25:751–763, 2017.

    PubMed  PubMed Central  Google Scholar 

  21. Rebecca, A. H., K. Kerlikowske, C. I. Flowers, B. C. Yankaskas, Z. Weiwei, and D. L. Miglioretti. Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography. Ann. Intern. Med. 155:481–492, 2011.

    Article  Google Scholar 

  22. Saeys, Y., I. Inza, and P. Larranaga. A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507–2517, 2007.

    Article  CAS  Google Scholar 

  23. Tan, M., F. Aghaei, Y. Wang, and B. Zheng. Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions. Phys. Med. Biol. 62:358–376, 2017.

    Article  PubMed  Google Scholar 

  24. Tan, M., B. Zheng, J. K. Leader, and D. Gur. Association between changes in mammographic image features and risk for near-term breast cancer development. IEEE Trans. Med. Imaging 35:1719–1728, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Wang, Y., F. Aghaei, A. Zarafshani, Y. Qiu, W. Qian, and B. Zheng. Computer-aided classification of mammographic masses using visually sensitive image features. J. Xray. Sci. Technol. 25:171–186, 2017.

    PubMed  PubMed Central  Google Scholar 

  26. Wang, X., D. Lederman, J. Tan, X. H. Wang, and B. Zheng. Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry. Med. Eng. Phys. 27:934–942, 2011.

    Article  CAS  Google Scholar 

  27. Weaver, D. L., R. D. Rosenberg, W. E. Barlow, L. Ichikawa, P. A. Carney, K. Kerlikowske, D. S. Buist, B. M. Geller, C. R. Key, S. J. Maygarden, and R. Ballard-Barbash. Pathologic findings from the breast cancer surveillance consortium. Cancer 106(4):732–742, 2006.

    Article  PubMed  Google Scholar 

  28. Witten, I. H., E. Frank, and M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). Amsterdam: Elsevier, 2011.

    Google Scholar 

  29. Zheng, B., J. H. Sumkin, M. L. Zuley, D. Lederman, X. Wang, and D. Gur. Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment. Br. J. Radiol. 85:e153–e161, 2012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgment

This work is supported in part by Grant R01 CA197150 from the National Cancer Institute, National Institutes of Health, USA.

Author information

Affiliations

  1. School of Electrical and Computer Engineering, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA

    Gopichandh Danala, Faranak Aghaei, Morteza Heidari & Bin Zheng

  2. Department of Radiology, Mayo Clinic, Phoenix, AZ, 85054, USA

    Bhavika Patel

  3. School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA

    Jing Li & Teresa Wu

Authors
  1. Gopichandh Danala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bhavika Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Faranak Aghaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Morteza Heidari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Teresa Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Zheng.

Additional information

Associate Editor Ender A Finol oversaw the review of this article.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Danala, G., Patel, B., Aghaei, F. et al. Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms. Ann Biomed Eng 46, 1419–1431 (2018). https://doi.org/10.1007/s10439-018-2044-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-018-2044-4

Keywords

  • Breast cancer diagnosis
  • Computer-aided diagnosis (CAD)
  • Contrast-enhanced digital mammography (CEDM)
  • Classification of breast masses
  • Segmentation of breast mass regions
  • Performance comparison