title: Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay.
abstract: OBJECTIVE
  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).
  
  
  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).
  
  
  CONCLUSIONS
  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.
authors: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors: Elizabeth J
authors: http://orcid.org/0000-0002-1407-7012
authors: EJ
authors: Sutton
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors: Jung Hun
authors: JH
authors: Oh
authors: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.|Weill Cornell Medical College, Cornell University, New York, New York, USA.
authors: Brittany Z
authors: BZ
authors: Dashevsky
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors: Harini
authors: H
authors: Veeraraghavan
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors: Aditya P
authors: AP
authors: Apte
authors: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.|Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors: Sunitha B
authors: SB
authors: Thakur
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors: Joseph O
authors: JO
authors: Deasy
authors: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors: Elizabeth A
authors: EA
authors: Morris
mesh_terms: D000328:Adult; D000368:Aged; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology*; D018270:Carcinoma, Ductal, Breast / Q000235:genetics* / Q000473:pathology*; D015331:Cohort Studies; D003287:Contrast Media; D005260:Female; D019786:Gadolinium DTPA; D015870:Gene Expression / Q000235:genetics; D023281:Genomics / Q000379:methods*; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D012189:Retrospective Studies
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA
keywords: breast cancer subtypes; genotype; invasive ductal carcinoma