title: Use of volumetric features for temporal comparison of mass lesions in full field digital mammograms.
abstract: OBJECTIVE
  Temporal comparison of lesions might improve classification between benign and malignant lesions in full-field digital mammograms (FFDM). The authors compare the use of volumetric features for lesion classification, which are computed from dense tissue thickness maps, to the use of mammographic lesion area. Use of dense tissue thickness maps for lesion characterization is advantageous, since it results in lesion features that are invariant to acquisition parameters.
  
  
  METHODS
  The dataset used in the analysis consisted of 60 temporal mammogram pairs comprising 120 mediolateral oblique or craniocaudal views with a total of 65 lesions, of which 41 were benign and 24 malignant. The authors analyzed the performance of four volumetric features, area, and four other commonly used features obtained from temporal mammogram pairs, current mammograms, and prior mammograms. The authors evaluated the individual performance of all features and of different feature sets. The authors used linear discriminant analysis with leave-one-out cross validation to classify different feature sets.
  
  
  RESULTS
  Volumetric features from temporal mammogram pairs achieved the best individual performance, as measured by the area under the receiver operating characteristic curve (Az value). Volume change (Az = 0.88) achieved higher Az value than projected lesion area change (Az = 0.78) in the temporal comparison of lesions. Best performance was achieved with a set that consisted of a set of features extracted from the current exam combined with four volumetric features representing changes with respect to the prior mammogram (Az = 0.90). This was significantly better (p = 0.005) than the performance obtained using features from the current exam only (Az = 0.77).
  
  
  CONCLUSIONS
  Volumetric features from temporal mammogram pairs combined with features from the single exam significantly improve discrimination of benign and malignant lesions in FFDM mammograms compared to using only single exam features. In the comparison with prior mammograms, use of volumetric change may lead to better performance than use of lesion area change.
authors: Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia.
authors: Jelena
authors: J
authors: Bozek
authors: Department of Radiology, Radboud University Nijmegen Medical Centre, Geert Grooteplein Zuid 18, 6525 GA Nijmegen, The Netherlands.
authors: Michiel
authors: M
authors: Kallenberg
authors: Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia.
authors: Mislav
authors: M
authors: Grgic
authors: Department of Radiology, Radboud University Nijmegen Medical Centre, Geert Grooteplein Zuid 18, 6525 GA Nijmegen, The Netherlands.
authors: Nico
authors: N
authors: Karssemeijer
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D013997:Time Factors