Computer-aided analysis of ultrasound elasticity images for classification of benign and malignant breast masses

Woo Kyung Moon, Ji Won Choi, Nariya Cho, Sang Hee Park, Jung Min Chang, Mijung Jang, Kwang Gi Kim

Research output: Contribution to journalArticle

17 Scopus citations

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. 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 (Az) 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 Az value of 0.89, which is significantly higher than the Az 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.

Original languageEnglish
Pages (from-to)1460-1465
Number of pages6
JournalAmerican Journal of Roentgenology
Volume195
Issue number6
DOIs
StatePublished - 1 Dec 2010

Keywords

  • Breast tumor
  • Breast ultrasound
  • Computer-aided diagnosis
  • Sonoelastography

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