Paper
3 March 2009 Computerized breast parenchymal analysis on DCE-MRI
Hui Li, Maryellen L. Giger, Yading Yuan, Sanaz A. Jansen, Li Lan, Neha Bhooshan, Gillian M. Newstead
Author Affiliations +
Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72600N (2009) https://doi.org/10.1117/12.813567
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hui Li, Maryellen L. Giger, Yading Yuan, Sanaz A. Jansen, Li Lan, Neha Bhooshan, and Gillian M. Newstead "Computerized breast parenchymal analysis on DCE-MRI", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600N (3 March 2009); https://doi.org/10.1117/12.813567
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Cited by 3 scholarly publications.
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KEYWORDS
Breast

Breast cancer

Magnetic resonance imaging

Image segmentation

Fuzzy logic

3D image processing

Cancer

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