title: Detection and classification of contrast-enhancing masses by a fully automatic computer-assisted diagnosis system for breast MRI.
abstract: OBJECTIVE
  To evaluate a fully automatic computer-assisted diagnosis (CAD) method for breast magnetic resonance imaging (MRI), which considered dynamic as well as morphologic parameters and linked those to descriptions laid down in the Breast Imaging Reporting and Data System (BI-RADS) MRI atlas.
  
  
  METHODS
  MR images of 108 patients with 141 histologically proven mass-like lesions (88 malignant, 53 benign) were included. The CAD system automatically performed the following processing steps: 3D nonrigid motion correction, detection of lesions by a segmentation algorithm, extraction of multiple dynamic and morphologic parameters, and classification of lesions. As one final result, the lesions were categorized by defining their probability of malignancy; this so-called morpho-dynamic index (MDI) ranged from 0%-100%. The results of the CAD system were correlated with histopathologic findings.
  
  
  RESULTS
  The CAD system had a high detection rate of the histologically proven lesions, missing only two malignancies of invasive multifocal carcinomas and four benign lesions (three fibroadenomas, one atypical ductal hyperplasia). The 86 detected malignant lesions showed a mean MDI of 86.1% (± 15.4%); the mean MDI of the 49 coded benign lesions was 41.8% (± 22.0%; P < 0.001). Based on receiver-operating characteristic analysis, the diagnostic accuracy of the CAD system was 93.5%. Using an appropriate cutoff value (MDI 50%), sensitivity was 96.5% combined with specificity of 75.5%.
  
  
  CONCLUSIONS
  The fully automatic CAD technique seems to reliably distinguish between benign and malignant mass-like breast tumors. Observer-independent CAD may be a promising additional tool for the interpretation of breast MRI in the clinical routine.
authors.affiliation: Department of Radiology, Charité University Medicine Berlin, Berlin, Germany. diane.renz@charite.de
authors.forename: Diane M
authors.initials: DM
authors.lastname: Renz
authors.forename: Joachim
authors.initials: J
authors.lastname: Böttcher
authors.forename: Felix
authors.initials: F
authors.lastname: Diekmann
authors.forename: Alexander
authors.initials: A
authors.lastname: Poellinger
authors.forename: Martin H
authors.initials: MH
authors.lastname: Maurer
authors.forename: Alexander
authors.initials: A
authors.lastname: Pfeil
authors.forename: Florian
authors.initials: F
authors.lastname: Streitparth
authors.forename: Federico
authors.initials: F
authors.lastname: Collettini
authors.forename: Ulrich
authors.initials: U
authors.lastname: Bick
authors.forename: Bernd
authors.initials: B
authors.lastname: Hamm
authors.forename: Eva M
authors.initials: EM
authors.lastname: Fallenberg
mesh_terms: D000704:Analysis of Variance; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000473:pathology* / Q000628:therapy; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D003951:Diagnostic Errors / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media