Fifty-seventh annual meeting of the American association of physicists in medicine
TU-AB-BRA-09: Radiomics and Radiogenomics for Breast Cancer Using Magnetic Resonance Imaging
Recently, radiomics has emerged as a new research field with the aim of (1) identifying quantitative medical image features associated with outcomes, (2) building predictive models of outcomes, (3) and thereby better understanding the underlying mechanisms of outcomes. Our group has developed methods to extract quantitative features from medical images and to model the outcomes. In this work, we summarize our studies with magnetic resonance (MR) images, demonstrating the potential of using radiomics for outcomes research.
This retrospective study analyzed 178 women with invasive ductal carcinoma (IDC) and preoperative breast MR images. Tumor subtypes defined by immunohistochemistry surrogates are: estrogen and progesterone receptor positive (ERPR+; n=95), HER2 receptor positive (HER2+; n=35) and triple negative (TN; n=48). Tumors were contoured on a single central slice from fat-suppressed T1-weight pre- and three post-contrast images. Image features were extracted from the contours. Clinical and pathologic features were collected. Linear regression analysis was used to build a predictive model of the FDA approved OncotypeDx Breast Cancer 21-gene Assay Recurrence Score (RS) that was measured for ER positive patients. In addition, a machine learning method was used to differentiate three breast cancer subtypes using image features.
Using stepwise multiple linear regression analysis, a model with three features to predict the OncotypeDx RS achieved R-squared = 0.228 (adjusted R-squared = 0.198; p = 0.0002) and Spearman's rank correlation coefficient = 0.485 (p < 0.0001). Separately, a support vector machine model with 9 features distinguished IDC subtypes with an overall accuracy of 83.4%: 89.2% (ERPR+), 63.6% (HER2+) and 82.5% (TN). A Kruskal-Wallis test for the 9 features showed a statistically significant difference between ERPR+, HER2+ and TN subtypes with p < 0.0001.
In this study, we demonstrated the potential to use radiomics to understand breast cancer genomics with non-invasive MR images.