title: Computer-aided analysis of ultrasound elasticity images for classification of benign and malignant breast masses.
abstract: OBJECTIVE
  The purpose of this study was to evaluate computer-aided analysis of ultrasound elasticity images for the classification of benign and malignant breast tumors.
  
  
  METHODS
  Real-time ultrasound elastography of 140 women (mean age, 46 years; age range, 35-67 years) with nonpalpable breast masses (101 benign and 39 malignant lesions) was performed before needle biopsy. A region of interest (ROI) was drawn around the margin of the mass, and a score for each pixel was assigned; scores ranged from 0 for the greatest strain to 255 for no strain. The diagnostic performances of a neural network based on the values of the six elasticity features were compared with visual assessment of elasticity images and BI-RADS assessment using B-mode images.
  
  
  RESULTS
  The values for the area under the receiver operating characteristic curve (A(z)) of the six elasticity features--mean hue histogram value, skewness, kurtosis, difference histogram variation, edge density, and run length--were 0.84, 0.69, 0.63, 0.75, 0.68, and 0.71, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of the neural network based on all six features were 92% (36/39), 74% (75/101), 58% (36/62), and 96% (75/78), respectively, with an A(z) value of 0.89, which is significantly higher than the A(z) of 0.81 for visual assessment by radiologists (p = 0.01) and 0.76 for BI-RADS assessment using B-mode images (p = 0.002).
  
  
  CONCLUSIONS
  Computer-aided analysis of ultrasound elasticity images has the potential to aid in the classification of benign and malignant breast tumors.
authors.affiliation: Department of Radiology and Clinical Research Institute, Seoul National University Hospital and the Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
authors.forename: Woo Kyung
authors.initials: WK
authors.lastname: Moon
authors.forename: Ji Won
authors.initials: JW
authors.lastname: Choi
authors.forename: Nariya
authors.initials: N
authors.lastname: Cho
authors.forename: Sang Hee
authors.initials: SH
authors.lastname: Park
authors.forename: Jung Min
authors.initials: JM
authors.lastname: Chang
authors.forename: Mijung
authors.initials: M
authors.lastname: Jang
authors.forename: Kwang Gi
authors.initials: KG
authors.lastname: Kim
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D054459:Elasticity Imaging Techniques / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*