Related Experiment Video
Updated: Jun 6, 2026
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Computer-aided analysis of ultrasound elasticity images for classification of benign and malignant breast masses
Woo Kyung Moon1, Ji Won Choi, Nariya Cho
1Department of Radiology and Clinical Research Institute, Seoul National University Hospital and the Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
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.
Materials And 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).
Conclusion:
Computer-aided analysis of ultrasound elasticity images has the potential to aid in the classification of benign and malignant breast tumors.
