title: Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system.
abstract: OBJECTIVE
  To develop and test a computer-aided diagnosis (CAD) system to improve the performance of radiologists in classifying lesions on breast MRI (BMRI).
  
  
  METHODS
  A CAD system was developed that uses a semiautomated segmentation method. After segmentation, 42 features based on lesion shape, texture, and enhancement kinetics were computed, and the 13 best features were selected and used as inputs to a backpropagation neural network (BNN). The BNN was trained and tested using the leave-one-out method on 80 BMRI lesions (37 benign, 43 malignant). Lesion histopathology was used as the reference standard. Five human readers classified the 80 lesions first without and then with CAD assistance. The performance of the computer classifier and the human readers was assessed using receiver operating characteristic curves; the performance of the human readers was also evaluated using multireader multicase (MRMC) analysis.
  
  
  RESULTS
  The performance of the human readers significantly improved when aided by the CAD system (P < 0.05). MRMC analysis showed that human reader performance with and without CAD system assistance can be generalized to the population of cases (P < 0.001).
  
  
  CONCLUSIONS
  A CAD system based on lesion morphology and enhancement kinetics can improve the performance of human readers in classifying lesions on breast MRI.
authors.affiliation: Department of Biomedical Engineering, The University of Iowa, Iowa City, Iowa 52242, USA.
authors.forename: Lina Arbash
authors.initials: LA
authors.lastname: Meinel
authors.forename: Alan H
authors.initials: AH
authors.lastname: Stolpen
authors.forename: Kevin S
authors.initials: KS
authors.lastname: Berbaum
authors.forename: Laurie L
authors.initials: LL
authors.lastname: Fajardo
authors.forename: Joseph M
authors.initials: JM
authors.lastname: Reinhardt
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis*; D002983:Clinical Competence; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008536:Meglumine / Q000031:analogs & derivatives; D016571:Neural Networks, Computer; D015588:Observer Variation; D009942:Organometallic Compounds; D012372:ROC Curve; D012984:Software
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; C064572:gadobenic acid; D008536:Meglumine