Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

Kayla R. Mendel; Hui Li; Maryellen L. Giger

doi:10.1117/12.2217775

24 March 2016 Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

Kayla R. Mendel, Hui Li, Maryellen L. Giger

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97851W (2016) https://doi.org/10.1117/12.2217775
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Breast density is routinely assessed qualitatively in screening mammography. However, it is challenging to quantitatively determine a 3D density from a 2D image such as a mammogram. Furthermore, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used more frequently in the screening of high-risk populations. The purpose of our study is to segment parenchyma and to quantitatively determine volumetric breast density on pre-contrast axial DCE-MRI images (i.e., non-contrast) using a semi-automated quantitative approach. In this study, we retroactively examined 3D DCE-MRI images taken for breast cancer screening of a high-risk population. We analyzed 66 cases with ages between 28 and 76 (mean 48.8, standard deviation 10.8). DCE-MRIs were obtained on a Philips 3.0 T scanner. Our semi-automated DCE-MRI algorithm includes: (a) segmentation of breast tissue from non-breast tissue using fuzzy cmeans clustering (b) separation of dense and fatty tissues using Otsu’s method, and (c) calculation of volumetric density as the ratio of dense voxels to total breast voxels. We examined the relationship between pre-contrast DCE-MRI density and clinical BI-RADS density obtained from radiology reports, and obtained a statistically significant correlation [Spearman ρ-value of 0.66 (p < 0.0001)]. Our method within precision medicine may be useful for monitoring high-risk populations.

Citation Download Citation

Kayla R. Mendel, Hui Li, and Maryellen L. Giger "Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851W (24 March 2016); https://doi.org/10.1117/12.2217775

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available