Ultrasound Strain Elastography for Breast Lesions: Computer‐Aided Evaluation With Quantifiable Elastographic Features. (10th March 2017)

• Record Type: Journal Article
• Authors: Xiao, Yang; Zeng, Jie; Zhang, Xue; Niu, Li‐Li; Qian, Ming; Wang, Cong‐Zhi; Zheng, Hai‐Rong; Zheng, Rong‐Qin
• Is Part Of: Journal of ultrasound in medicine. Volume 36:Number 6(2017)
• Page Start: 1089
• Page End: 1100
• Publication Date: 2017-03-10
• Subjects: breast -- computer‐aided -- quantifiable elastographic features -- ultrasound real‐time strain elastograhpy; Ultrasonics in medicine -- Periodicals; Ultrasonics; Ultrasonography; Ultrasonics in medicine; Electronic journals; Periodicals; Periodicals; 616.07543
• DOI: 10.7863/ultra.16.01032
• Languages: English
• ISSNs: 0278-4297

Abstract

Abstract : Objectives: To develop and evaluate a set of quantifiable elastographic features based on ultrasound real‐time strain elastography (SE) in differentiating between benign and malignant breast lesions. Methods: The SE and conventional B‐mode ultrasound images of 226 breast lesions (81 malignant, 145 benign) were obtained from 226 consecutive women. By using a computer‐aided tool, four elastographic features (elasticity score, lesion stiffness degree, lesion‐to‐fat strain ratio, and elastography‐to‐B‐mode lesion area ratio) were respectively calculated and evaluated. Histopathologic results were used as the reference standard. B‐mode Breast Imaging Reporting and Data System categorization was used to compare the performances between B‐mode ultrasound and SE. Sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic curve analyses were performed to evaluate the diagnostic performances for three data sets (conventional B‐mode ultrasound alone, SE features alone, combined SE features). Results: Quantifiable SE features for malignant lesions all showed significantly higher values than those for benign lesions (all P < .001). The evaluation with any individual SE feature significantly improved the specificity in breast lesion differentiation compared with B‐mode ultrasound (all P <.001). The logistic regression model combing SE features significantly improved the diagnostic performance compared with B‐mode US, with significantly increased specificity (95.2% versus 54.5%; P < .001) and area under the receiver operating characteristic curve (0.988 versus 0.921, P < .001). Conclusions: Computer‐aided tool with SE provided further elasticity information for breast characterization. Evaluation using quantifiable SE features showed better diagnostic performance than conventional B‐mode ultrasound in breast lesion differentiation.