title: Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.
abstract: OBJECTIVE
  To determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)-positive cancer, human epidermal growth factor receptor type 2 (HER2)-positive cancer, and benign fibroadenoma lesions on dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images.
  
  
  METHODS
  This is a retrospective study of prospectively acquired breast MR imaging data collected from an institutional review board-approved, HIPAA-compliant study between 2002 and 2007. Written informed consent was obtained from all patients. The authors collected DCE MR images from 65 women with 76 breast lesions who had been recruited into a larger study of breast MR imaging. The women had triple-negative (n = 21), ER-positive (n = 25), HER2-positive (n = 18), or fibroadenoma (n = 12) lesions. All lesions were classified as Breast Imaging Reporting and Data System category 4 or higher on the basis of previous imaging. Images were subject to quantitative feature extraction, feed-forward feature selection by means of linear discriminant analysis, and lesion classification by using a support vector machine classifier. The area under the receiver operating characteristic curve (Az) was calculated for each of five lesion classification tasks involving triple-negative breast cancers.
  
  
  RESULTS
  For each pair-wise lesion type comparison, linear discriminant analysis helped identify the most discriminatory features, which in conjunction with a support vector machine classifier yielded an Az of 0.73 (95% confidence interval [CI]: 0.59, 0.87) for triple-negative cancer versus all non-triple-negative lesions, 0.74 (95% CI: 0.60, 0.88) for triple-negative cancer versus ER- and HER2-positive cancer, 0.77 (95% CI: 0.63, 0.91) for triple-negative versus ER-positive cancer, 0.74 (95% CI: 0.58, 0.89) for triple-negative versus HER2-positive cancer, and 0.97 (95% CI: 0.91, 1.00) for triple-negative cancer versus fibroadenoma.
  
  
  CONCLUSIONS
  Triple-negative cancers possess certain characteristic features on DCE MR images that can be captured and quantified with CAD, enabling good discrimination of triple-negative cancers from non-triple-negative cancers, as well as between triple-negative cancers and benign fibroadenomas. Such CAD algorithms may provide added diagnostic benefit in identifying the highly aggressive triple-negative cancer phenotype with DCE MR imaging in high-risk women.
authors.affiliation: From the Department of Biomedical Engineering, Rutgers University, 599 Taylor Rd, Room 213, Piscataway, NJ 08854 (S.C.A.); Departments of Radiology (M.A.R., S.E., M.D.S.) and Pathology (M.D.F., P.Z., C.M.), University of Pennsylvania, Philadelphia, Pa; Department of Pathology and Anatomical Science, State University of New York at the University at Buffalo, Buffalo, NY (J.E.T.); and Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio (A.M.).
authors.forename: Shannon C
authors.initials: SC
authors.lastname: Agner
authors.forename: Mark A
authors.initials: MA
authors.lastname: Rosen
authors.forename: Sarah
authors.initials: S
authors.lastname: Englander
authors.forename: John E
authors.initials: JE
authors.lastname: Tomaszewski
authors.forename: Michael D
authors.initials: MD
authors.lastname: Feldman
authors.forename: Paul
authors.initials: P
authors.lastname: Zhang
authors.forename: Carolyn
authors.initials: C
authors.lastname: Mies
authors.forename: Mitchell D
authors.initials: MD
authors.lastname: Schnall
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
mesh_terms: D000328:Adult; D000368:Aged; D001706:Biopsy; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005240:Feasibility Studies; D005260:Female; D018226:Fibroadenoma / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D053783:Magnetic Resonance Imaging, Interventional / Q000379:methods; D008536:Meglumine / Q000031:analogs & derivatives; D008875:Middle Aged; D009942:Organometallic Compounds; D012189:Retrospective Studies; D064726:Triple Negative Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; C064572:gadobenic acid; D008536:Meglumine