Segmentation and classification of triple negative breast cancers using dce-mri

Shannon C. Agner, Jun Xu, Hussain Fatakdawala, Shridar Ganesan, Anant Madabhushi, Sarah Englander, Mark Rosen, Kathleen Thomas, Mitchell Schnall, Michael Feldman, John Tomaszewski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2009
Pages1227-1230
Number of pages4
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Conference

Conference2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
CountryUnited States
CityBoston, MA
Period06/28/0907/1/09

Keywords

  • Breast cancer
  • CAD
  • Classification
  • Image analysis
  • Kinetic texture curves
  • Molecular subtypes
  • Triple negative

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    Agner, S. C., Xu, J., Fatakdawala, H., Ganesan, S., Madabhushi, A., Englander, S., Rosen, M., Thomas, K., Schnall, M., Feldman, M., & Tomaszewski, J. (2009). Segmentation and classification of triple negative breast cancers using dce-mri. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (pp. 1227-1230). [5193283] (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). https://doi.org/10.1109/ISBI.2009.5193283