title: [Quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography].
abstract: OBJECTIVE
  The aim of the study was an evaluation of the quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography.
  
  
  METHODS
  In 137 patients, 183 contrast-enhanced lesions were identified in dynamic MR mammography. In 82 lesions histopathology was performed and in 101 lesions follow-up MR mammography was carried out. The contrast kinetics of lesions was analyzed quantitatively, on a pixel-by-pixel basis. The initial signal enhancement was coded by color intensity (bright, medium, dark), the post-initial signal enhancement was coded by color hue (blue, green, red). ROC analysis and logistic regression were performed.
  
  
  RESULTS
  Malignant lesions showed a significantly higher number of bright red, medium red and dark red, bright green and medium green pixels than benign lesions. Benign lesions showed a significantly higher number of bright blue, medium blue and dark blue pixels than malignant lesions. The highest areas under the ROC curves (AUC) were found for medium red (AUC = 0.782) and medium green pixels (AUC = 0.733). A regression model with medium red and medium green pixels allows diagnosis of malignant lesions with a sensitivity of 60.7% and a specificity of 83.6%.
  
  
  CONCLUSIONS
  The quantification of contrast-enhanced lesions allows objective analysis of the signal intensities in malignant and benign lesions. Therefore, this method might increase the specificity of MR mammography. Further developments are necessary before this method can be used for routine analysis of contrast-enhancing lesions in MR mammography.
authors.affiliation: Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Hufelandstr. 55, 45122, Essen. elke.hauth@uni-essen.de
authors.forename: E A M
authors.initials: EA
authors.lastname: Hauth
authors.forename: H
authors.initials: H
authors.lastname: Jaeger
authors.forename: S
authors.initials: S
authors.lastname: Maderwald
authors.forename: A
authors.initials: A
authors.lastname: Mühler
authors.forename: R
authors.initials: R
authors.lastname: Kimmig
authors.forename: M
authors.initials: M
authors.lastname: Forsting
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods; D008536:Meglumine / Q000031:analogs & derivatives*; D009942:Organometallic Compounds; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; C064572:gadobenic acid; D008536:Meglumine