title: New spatiotemporal features for improved discrimination of benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging of the breast.
abstract: OBJECTIVE
  The objective of this study was to measure the efficacy of 7 new spatiotemporal features for discriminating between benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging (MRI) of the breast.
  
  
  METHODS
  A total of 48 breast lesions from 39 patients were used: 25 malignant and 23 benign. Lesions were acquired using 1.5-T MRI machines in 3 different protocols. Two experiments were performed: (i) selection of the most discriminatory subset of features drawn from the new features and features from the literature and (ii) validation of classification performance of the selected subset of features.
  
  
  RESULTS
  Results of the feature selection experiment show that the subset comprising 2 of the new features is the most useful for automatic classification of suspicious lesions in the breast: (i) gradient correlation of maximum intensity and (ii) mean wash-in rate. Results of the validation experiment show that using these 2 features, unseen data can be classified with an area under the receiver operating characteristic curve of 0.91 ± 0.06.
  
  
  CONCLUSIONS
  Results of the experiments suggest that suspicious lesions in dynamic contrast-enhanced-MRI of the breast can be classified, with high accuracy, using only 2 of the proposed spatiotemporal features. The selected features indicate heterogeneity of enhancement and speed of enhancement in a tissue. High values of these indicators are likely to be correlated with malignancy.
authors.affiliation: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Queensland, Australia. ygal@itee.uq.edu.au
authors.forename: Yaniv
authors.initials: Y
authors.lastname: Gal
authors.forename: Andrew
authors.initials: A
authors.lastname: Mehnert
authors.forename: Andrew
authors.initials: A
authors.lastname: Bradley
authors.forename: Dominic
authors.initials: D
authors.lastname: Kennedy
authors.forename: Stuart
authors.initials: S
authors.lastname: Crozier
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003287:Contrast Media; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media