Abstract
The purpose of this study is to evaluate the potential of computer-aided diagnosis (CADx) methods utilizing three breast imaging modalities: full-field digital mammography (FFDM), sonography, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for breast lesion classification. Three separate databases for each modality were retrospectively organized: FFDM (255 malignant lesions, 177 benign lesions), ultrasound (968 malignant lesions, 158 benign lesions), and DCE-MRI (347 malignant lesions, 129 benign lesions). From these single-modality databases, three dual-modality databases were constructed as well as a triple-modality database (31 malignant lesions, 17 benign lesions). Our computerized analysis methods consisted of several steps: (1) automatic lesion segmentation; (2) automatic feature extraction; (3) automatic feature selection; (4) merging of selected features into a probability of malignancy. Stepwise linear discriminant analysis using a Wilks lambda cost function in a leave-one-lesion-out method was used for feature selection. The selected features were merged using a Bayesian artificial neural network (BANN) with a leave-one-lesion-out method. The classification performance was assessed using receiver-operating characteristics (ROC) analysis. Results showed that the computerized analysis of breast lesions using image information from all three modalities yielded an AUC of 0.95±0.03. The observed trend of increasing performance as information from more modalities is included in the classifier indicates that the use of all three modalities can potentially improve the diagnostic classification of CADx.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jemal, A., Siegel, R., Ward, E., Hao, Y., et al.: Cancer statistics. Cancer J. Clin. 58, 71–96 (2008)
Giger, M.L., Chan, H.-P., Boone, J.: Anniversary paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM. Med. Phys. 35, 5799–5820 (2008)
Drukker, K., Giger, M.L., Vyborny, C.J., Mendelson, E.: Computerized detection and classification of cancer on breast ultrasound. Acad. Radiol. 11, 526–535 (2004)
Horsch, K., Giger, M.L., Venta, L.A., Vyborny, C.: Computerized diagnosis of breast lesions on ultrasound. Med. Phys. 29, 157–164 (2002)
Huo, Z., Giger, M.L., Vyborny, C.J., et al.: Automated computerized classification of malignant and benign masses on digitized mammograms. Acad. Radiol. 5, 155–168 (1998)
Li, H., Giger, M.L., Yuan, Y., Chen, W., Horsch, K., Lan, L., Jamieson, A., Sennett, C.A., Jansen, S.A.: Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad. Radiol. 15, 1437–1445 (2008)
Chen, W., Giger, M.L., Bick, U., Newstead, G.M.: Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med. Phys. 33, 2878–2887 (2006)
Chen, W., Giger, M.L., Lan, L., Bick, U.: Computerized interpretation of breast MRI: Investigation of enhancement-variance dynamics. Med. Phys. 31, 1076–1082 (2004)
Chen, W., Giger, M.L., Li, H., Bick, U., Newstead, G.M.: Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Mag. Reson. in Med. 58, 562–571 (2007)
Drukker, K., Horsch, K., Giger, M.L.: Multimodality Computerized Diagnosis of Breast Lesions Using Mammography and Sonography. Acad. Radiol. 12, 970–979 (2005)
Yuan, Y., Giger, M.L., Li, H., Bhooshan, N., Sennett, C.: Multi-modality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad. Radiol. (in press)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, 3rd edn. Prentice-Hall, Englewood Cliffs (1992)
Kupinski, M.A., Edwards, D.C., Giger, M.L., Metz, C.E.: Ideal observer approximation using Bayesian classification neural networks. IEEE Trans. Med. Imag. 20, 886–899 (2001)
Metz, C.E.: ROCKIT (computer program), version 0.9b (1998), http://www-radiology.uchicago.edu/krl/roc_soft.htm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bhooshan, N. et al. (2010). Performance of Triple-Modality CADx on Breast Cancer Diagnostic Classification. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science, vol 6136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13666-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-13666-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13665-8
Online ISBN: 978-3-642-13666-5
eBook Packages: Computer ScienceComputer Science (R0)