Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay

Abstract

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.

Breast cancer molecular subtype classifier that incorporates MRI features

“…GLCM features, also referred to as Harlick or second-order texture features, included: 1: energy (or coherence); 2: entropy; 3: contrast; and 4: homogeneity. For more information about image feature extraction, see El Naqa et al 15 and for method see Sutton et al 16 …”

Imaging genomics in cancer research: limitations and promises

“…Other features were not routinely evaluated, likely because GBM commonly demonstrated significant intratumour heterogeneity. 73,82 To overcome this limitation, Gevaert et al segmented the tumour into enhancing, oedematous and necrotic regions. Quantitative imaging features were then extracted from each region and correlated with genomic data.…”

Computer‐aided heterogeneity analysis in breast MR imaging assessment of ductal carcinoma in situ: Correlating histologic grade and receptor status

“…Heterogeneity analysis of breast cancer has been largely performed with ultrasound and MR imaging (15–22). Previous studies utilizing heterogeneity analysis in the development of MR imaging biomarkers focused on differentiating benign from malignant breast lesions and molecular subtypes of invasive breast cancer (22–24). Recent applications of heterogeneity analysis show promising results in the assessment of response of invasive breast cancer being treated with neoadjuvant therapy and the recurrence risk of invasive breast cancer (25–28).…”