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Proceedings Paper

Computerized breast parenchymal analysis on DCE-MRI
Author(s): Hui Li; Maryellen L. Giger; Yading Yuan; Sanaz A. Jansen; Li Lan; Neha Bhooshan; Gillian M. Newstead
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Paper Abstract

Breast density has been shown to be associated with the risk of developing breast cancer, and MRI has been recommended for high-risk women screening, however, it is still unknown how the breast parenchymal enhancement on DCE-MRI is associated with breast density and breast cancer risk. Ninety-two DCE-MRI exams of asymptomatic women with normal MR findings were included in this study. The 3D breast volume was automatically segmented using a volume-growing based algorithm. The extracted breast volume was classified into fibroglandular and fatty regions based on the discriminant analysis method. The parenchymal kinetic curves within the breast fibroglandular region were extracted and categorized by use of fuzzy c-means clustering, and various parenchymal kinetic characteristics were extracted from the most enhancing voxels. Correlation analysis between the computer-extracted percent dense measures and radiologist-noted BIRADS density ratings yielded a correlation coefficient of 0.76 (p<0.0001). From kinetic analyses, 70% (64/92) of most enhancing curves showed persistent curve type and reached peak parenchymal intensity at the last postcontrast time point; with 89% (82/92) of most enhancing curves reaching peak intensity at either 4th or 5th post-contrast time points. Women with dense breast (BIRADS 3 and 4) were found to have more parenchymal enhancement at their peak time point (Ep) with an average Ep of 116.5% while those women with fatty breasts (BIRADS 1 and 2) demonstrated an average Ep of 62.0%. In conclusion, breast parenchymal enhancement may be associated with breast density and may be potential useful as an additional characteristic for assessing breast cancer risk.

Paper Details

Date Published: 3 March 2009
PDF: 6 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600N (3 March 2009); doi: 10.1117/12.813567
Show Author Affiliations
Hui Li, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)
Yading Yuan, The Univ. of Chicago (United States)
Sanaz A. Jansen, The Univ. of Chicago (United States)
Li Lan, The Univ. of Chicago (United States)
Neha Bhooshan, The Univ. of Chicago (United States)
Gillian M. Newstead, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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