title: Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.
abstract: OBJECTIVE
  To identify associations between semiautomatically extracted MRI features and breast cancer molecular subtypes.
  
  
  METHODS
  We analyzed routine clinical pre-operative breast MRIs from 275 breast cancer patients at a single institution in this retrospective, Institutional Review Board-approved study. Six fellowship-trained breast imagers reviewed the MRIs and annotated the cancers. Computer vision algorithms were then used to extract 56 imaging features from the cancers including morphologic, texture, and dynamic features. Surrogate markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor-2 [HER2]) were used to categorize tumors by molecular subtype: ER/PR+, HER2- (luminal A); ER/PR+, HER2+ (luminal B); ER/PR-, HER2+ (HER2); ER/PR/HER2- (basal). A multivariate analysis was used to determine associations between the imaging features and molecular subtype.
  
  
  RESULTS
  The imaging features were associated with both luminal A (P = 0.0007) and luminal B (P = 0.0063) molecular subtypes. No association was found for either HER2 (P = 0.2465) or basal (P = 0.1014) molecular subtype and the imaging features. A P-value of 0.0125 (0.05/4) was considered significant.
  
  
  CONCLUSIONS
  Luminal A and luminal B molecular subtype breast cancer are associated with semiautomatically extracted features from routine contrast enhanced breast MRI.
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA.
authors.forename: Lars J
authors.initials: LJ
authors.lastname: Grimm
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA.
authors.forename: Maciej A
authors.initials: MA
authors.lastname: Mazurowski
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics* / Q000473:pathology*; D005260:Female; D020022:Genetic Predisposition to Disease / Q000235:genetics; D023281:Genomics / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D057285:Precision Medicine / Q000379:methods; D011871:Radiology / Q000379:methods; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000235:genetics*; D011960:Receptors, Estrogen / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; C508053:ERBB2 protein, human
keywords: MRI; breast cancer; computer extracted features; molecular subtype