Segmentation and classification of triple negative breast cancers using DCE-MRI

Authors:
Shannon C. Agner;Jun Xu;Hussain Fatakdawala;Shridar Ganesan;Anant Madabhushi;Sarah Englander;Mark Rosen;Kathleen Thomas;Mitchell Schnall;Michael Feldman;John Tomaszewski
Affiliations:
Rutgers, The State Univeristy of New Jersey, Department of Biomedical Engineering, Piscataway, NJ;Rutgers, The State Univeristy of New Jersey, Department of Biomedical Engineering, Piscataway, NJ;Rutgers, The State Univeristy of New Jersey, Department of Biomedical Engineering, Piscataway, NJ;Rutgers, The State Univeristy of New Jersey, Department of Biomedical Engineering, Piscataway, NJ;Rutgers, The State Univeristy of New Jersey, Department of Biomedical Engineering, Piscataway, NJ;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

Citing 2
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Abstract

Triple-negative (TN) breast cancer has gained much interest recently due to its lack of response to receptor-targeted therapies and its aggressive clinical nature. In this study, we evaluate the ability of a computer-aided diagnosis (CAD) system to not only distinguish benign from malignant lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but also to quantitatively distinguish triple negative breast cancers from other molecular subtypes of breast cancer. 41 breast lesions (24 malignant, 17 benign) as imaged on DCE-MRI were included in the dataset. Of the 24 malignant cases, 13 were of the TN phenotype. Using the dynamic signal intensity information from the DCE-MRIs, an Expectation Maximization-driven active contours scheme is used to automatically segment the breast lesions. Following quantitative morphological, textural, and kinetic feature extraction, a support vector machine classifier was employed to distinguish (a) benign from malignant lesions and (b) TN from non-TN cancers. In the former case, the classifier yielded an accuracy of 83%, sensitivity of 79%, and specificity of 88%. In distinguishing TN from non-TN cases, the classifier had an accuracy of 92%, sensitivity of 92%, and specificity of 91%. The results suggest that the TN phenotype has distinct and quantifiable signatures on DCE-MRI that will be instrumental in the early detection of this aggressive breast cancer subtype.