Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay. Issue 5 (7th April 2015) - Sutton, Elizabeth J.; Oh, Jung Hun; Dashevsky, Brittany Z.; Veeraraghavan, Harini; Apte, Aditya P.; Thakur, Sunitha B.; Deasy, Joseph O.; Morris, Elizabeth A.
<x xml:space="preserve">Abstract</x> Purpose To investigate the association between a validated, gene‐expression‐based, aggressiveness assay, Oncotype Dx RS, and morphological and texture‐based image features extracted from magnetic resonance imaging (MRI). Materials and Methods This retrospective study received Internal Review Board approval and need for informed consent was waived. Between 2006–2012, we identified breast cancer patients with: 1) ER+, PR+, and HER2– invasive ductal carcinoma (IDC); 2) preoperative breast MRI; and 3) Oncotype Dx RS test results. Extracted features included morphological, histogram, and gray‐scale correlation matrix (GLCM)‐based texture features computed from tumors contoured on pre‐ and three postcontrast MR images. Linear regression analysis was performed to investigate the association between Oncotype Dx RS and different clinical, pathologic, and imaging features. P < 0.05 was considered statistically significant. Results Ninety‐five patients with IDC were included with a median Oncotype Dx RS of 16 (range: 0–45). Using stepwise multiple linear regression modeling, two MR‐derived image features, kurtosis in the first and third postcontrast images and histologic nuclear grade, were found to be significantly correlated with the Oncotype Dx RS with P = 0.0056, 0.0005, and 0.0105, respectively. The overall model resulted in statistically significant correlation with Oncotype Dx RS with an R‐squared value of 0.23 (adjusted R‐squared = 0.20; P = 0.0002) and a Spearman's rank correlation coefficient of 0.49 (P < 0.0001). Conclusion A model for IDC using imaging and pathology information correlates with Oncotype Dx RS scores, suggesting that image‐based features could also predict the likelihood of recurrence and magnitude of chemotherapy benefit. J. Magn. Reson. Imaging 2015;42:1398–1406.