title: Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.
abstract: OBJECTIVE
  To assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions.
  
  
  METHODS
  In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were first selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classification performance was assessed with receiver operating characteristic analysis.
  
  
  RESULTS
  Differentiation of DCIS from IDC lesions yielded an area under the receiver operating characteristic curve (AUC) of 0.83 +/- 0.03 (standard error). AUCs were 0.85 +/- 0.02 for differentiation between IDC and benign lesions and 0.79 +/- 0.03 for differentiation between DCIS and benign lesions. Differentiation between IDC lesions associated with positive LNs and IDC lesions associated with negative LNs yielded an AUC of 0.82 +/- 0.04. AUCs were 0.86 +/- 0.03 for differentiation between IDC lesions associated with positive LNs and benign lesions and 0.83 +/- 0.03 for differentiation between IDC lesions associated with negative LNs and benign lesions.
  
  
  CONCLUSIONS
  Computer-aided diagnosis of breast DCE MR imaging-depicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.
  
  
  BACKGROUND
  http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.09090838/-/DC1.
authors.affiliation: Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL 60637, USA. bhooshan@uchicago.edu
authors.forename: Neha
authors.initials: N
authors.lastname: Bhooshan
authors.forename: Maryellen L
authors.initials: ML
authors.lastname: Giger
authors.forename: Sanaz A
authors.initials: SA
authors.lastname: Jansen
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.forename: Li
authors.initials: L
authors.lastname: Lan
authors.forename: Gillian M
authors.initials: GM
authors.lastname: Newstead
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies
chemical_list: D003287:Contrast Media