Cancer screening with magnetic resonance imaging (MRI) is currently recommended for very high risk women. The high variability in the diagnostic accuracy of radiologists analyzing screening MRI examinations of the breast is due, at least in part, to the large amounts of data acquired. This has motivated substantial research towards the development of computer-aided diagnosis (CAD) systems for breast MRI which can assist in the diagnostic process by acting as a second reader of the examinations. This retrospective study was performed on 184 benign and 49 malignant lesions detected in a prospective MRI screening study of high risk women at Sunnybrook Health Sciences Centre. A method for performing semi-automatic lesion segmentation based on a supervised learning formulation was compared with the enhancement threshold based segmentation method in the context of a computer-aided diagnostic system. The results demonstrate that the proposed method can assist in providing increased separation between malignant and radiologically suspicious benign lesions. Separation between malignant and benign lesions based on margin measures improved from a receiver operating characteristic (ROC) curve area of 0.63 to 0.73 when the proposed segmentation method was compared with the enhancement threshold, representing a statistically significant improvement. Separation between malignant and benign lesions based on dynamic measures improved from a ROC curve area of 0.75 to 0.79 when the proposed segmentation method was compared to the enhancement threshold, also representing a statistically significant improvement. The proposed method has potential as a component of a computer-aided diagnostic system.
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Curry SJ (2003) Fulfilling the potential of cancer prevention and early detection. National Academies Press, Washington, DC
Ford S et al (1998) Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. Am J Hum Genet 62:676–689
Warner E et al (2008) Systematic review: using magnetic resonance imaging to screen women at high risk for breast cancer. Annals of Internal Medicine 148(9):671–679
Saslow D et al (2007) American Cancer Society Guidelines for Breast Screening with MRI as an adjunct to mammography. Cancer J Clin 57:75–89
Berg W et al (2012) Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. Journal of the American Medical Association 307(13):1394–1404
Warren R et al (2006) A test of performance of breast MRI interpretation in a multicentre screening study. Magn Reson Imaging 24(7):917–929
Chen W, Giger M, Bick U (2006) A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Academic Radiology 13(1):63–72
Chen W, Giger M, Bick U, Newstead G (2006) Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Medical Physics 33:2878
Nie K et al (2008) Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Academic Radiology 15(12):1513–1525
Wu Q, et al., Interactive lesion segmentation on dynamic contrast enhanced breast MRI using a Markov model. Proceedings SPIE Medical Imaging 2006: Image Processing, 6144, 2006, San Diego, USA
Xiaohua C, Brady M, Lo J, Moore N (2005) Simultaneous segmentation and registration of contrast-enhanced breast MRI. Information Processing in Medical Imaging Lecture Notes in Computer Science 3565:126–137
Woods B et al (2007) Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data. Journal of Magnetic Resonance Imaging 25(3):495–501
Greenman RL et al (1998) Bilateral imaging using separate interleaved 3D volumes and dynamically switched multiple receive coil arrays. Magn Reson Med 39:108–115
Martel AL et al (2007) Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast. Phys Med Biol 52(13):3803–3816
Levman J et al (2008) Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines. IEEE Transactions on Medical Imaging 27(5):688–696
Levman J et al (2014) A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations. Journal of Digital Imaging 27:145–151
Levman J et al (2009) Effect of the enhancement threshold on the computer-aided detection of breast cancer using MRI. Academic Radiology 16(9):1064–1069
Levman J, Martel AL (2011) A margin sharpness measurement for the diagnosis of breast cancer from magnetic resonance imaging examinations. Academic Radiology 18(12):1577–1581
Warner E et al (2004) Surveillance of BRCA1 and BRCA2 mutation carriers with magnetic resonance imaging, ultrasound, mammography, and clinical breast examination. Journal of the American Medical Association 29(11):1317–1325
The MRI data was acquired using funding from the Canadian Breast Cancer Research Alliance. The authors would also like to thank the Canadian Breast Cancer Foundation and the Canadian Institute for Health Research for their financial support for this research project.
Conflicts of Interest
The authors report no conflict of interest.
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Levman, J., Warner, E., Causer, P. et al. Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening. J Digit Imaging 27, 670–678 (2014). https://doi.org/10.1007/s10278-014-9723-y
- Computer-aided diagnosis
- Magnetic resonance imaging
- Supervised learning
- Pattern recognition