Xiao Yang, Zeng Jie, Zhang Xue, Niu Li-Li, Qian Ming, Wang Cong-Zhi, Zheng Hai-Rong, Zheng Rong-Qin
Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
J Ultrasound Med. 2017 Jun;36(6):1089-1100. doi: 10.7863/ultra.16.01032. Epub 2017 Mar 10.
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.
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).
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).
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.
基于超声实时应变弹性成像(SE)开发并评估一组可量化的弹性成像特征,以鉴别乳腺良恶性病变。
连续纳入226例女性,获取其226个乳腺病变(81个恶性,145个良性)的SE和传统B超图像。使用计算机辅助工具分别计算并评估四个弹性成像特征(弹性评分、病变硬度、病变与脂肪应变比、弹性成像与B超病变面积比)。以组织病理学结果作为参考标准。采用B超乳腺影像报告和数据系统分类法比较B超与SE的性能。进行敏感性、特异性、阳性和阴性预测值以及受试者工作特征曲线分析,以评估三个数据集(单独的传统B超、单独的SE特征、联合的SE特征)的诊断性能。
恶性病变的可量化SE特征均显著高于良性病变(均P <.001)。与B超相比,任何单个SE特征的评估均显著提高了乳腺病变鉴别诊断的特异性(均P <.001)。与B超相比,结合SE特征的逻辑回归模型显著提高了诊断性能,特异性显著提高(95.2%对54.5%;P <.001),受试者工作特征曲线下面积增大(0.988对0.921,P <.001)。
带有SE的计算机辅助工具为乳腺特征分析提供了更多弹性信息。使用可量化SE特征进行评估在乳腺病变鉴别诊断中显示出比传统B超更好的诊断性能。