COMPUTER-AIDED DIAGNOSIS BASED ON QUANTITATIVE ELASTOGRAPHIC FEATURES WITH SUPERSONIC SHEAR WAVE IMAGING
COMPUTER-AIDED DIAGNOSIS BASED ON QUANTITATIVE ELASTOGRAPHIC FEATURES WITH SUPERSONIC SHEAR WAVE IMAGING
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基于超声剪切波成像定量弹性成像特征的计算机辅助诊断
DOI：
10.1016/j.ultrasmedbio.2013.09.032
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发表时间：
2014-02-01
影响因子：
2.9
通讯作者：
Zheng, Hairong
中科院分区：
文献类型：
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作者：
Xiao, Yang;Zeng, Jie;Zheng, Hairong
超声剪切波成像（SSI）最近已被探索作为一种技术，以评估组织的弹性模量，并已成为一个有价值的工具，肿瘤定征。本研究的目的是开发一种新的计算机辅助诊断（CAD）系统，可以从彩色SSI弹性成像图像中自动客观地获取定量弹性成像信息，以分类良性和恶性乳腺肿瘤。获得了93名连续患者（平均年龄：40岁，年龄范围：16-75岁）的125个乳腺肿瘤（81个良性，44个恶性）的常规超声（US）和SSI弹性成像图像。在重建组织弹性数据并自动分割每个乳腺肿瘤后，分别计算和评价肿瘤和肿瘤周围区域的10个定量弹性成像特征（弹性模量平均值、最大值和标准差、硬度和弹性比）。支持向量机（SVM）分类器用于通过这些特征的组合进行最佳分类。使用B型乳腺成像报告和数据系统（BI-RADS）比较灰阶超声和SSI弹性成像的诊断性能。以组织学检查为参照标准。采用Student t检验、Mann-Whitney U检验、点双方差相关系数和受试者工作特征曲线分析进行统计学分析。结果显示，CAD方案对良恶性分类的准确性、敏感性、特异性、阳性预测值和阴性预测值分别为95.2%（119/125）、90.9%（40/44）、97.5%（79/81）、95.2%（40/42）和95.2%（79/83）。BI-RADS评估的阳性率分别为79.2%（99/125）、90.9%（40/44）、72.8%（59/81）、64.5%（40/62）和93.7%（59/63）。使用弹性成像特征组合的拟议CAD系统的受试者工作特征曲线下面积（Az值）显著高于放射科医师使用BI-RADS进行视觉评估的Az值（0.97 vs. 0.91）。结果表明，SSI弹性成像可用于计算机辅助特征提取，所提出的CAD方法可提高乳腺肿瘤分类诊断的准确性，避免不必要的活检。此外，肿瘤周围区域的弹性成像特征有可能在鉴别诊断中提供关键信息。（E-mail：hr. siat.ac.cn）（C）2014年世界医学与生物学超声联合会。
Supersonic shear wave imaging (SSI) has recently been explored as a technique to evaluate tissue elasticity modulus and has become a valuable tool for tumor characterization. The purpose of this study was to develop a novel computer-aided diagnosis (CAD) system that can acquire quantitative elastographic information from color SSI elastography images automatically and objectively for the purpose of classifying benign and malignant breast tumors. Conventional ultrasonography (US) and SSI elastography images of 125 breast tumors (81 benign, 44 malignant), in 93 consecutive patients (mean age: 40 y, age range: 16-75 y), were obtained. After reconstruction of tissue elasticity data and automatic segmentation of each breast tumor, 10 quantitative elastographic features of the tumor and peri-tumoral areas, respectively (elasticity modulus mean, maximum and standard deviation, hardness degree and elasticity ratio), were computed and evaluated. A support vector machine (SVM) classifier was used for optimum classification via combination of these features. The B-mode Breast Imaging Reporting and Data System (BI-RADS) was used to compare gray-scale US and SSI elastography with respect to diagnostic performance. Histopathologic examination was used as the reference standard. Student's t-test, the Mann-Whitney U-test, the point biserial correlation coefficient and receiver operating characteristic curve analysis were performed for statistical analysis. As a result, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of benign/malignant classification were 95.2% (119/125), 90.9% (40/44), 97.5% (79/81), 95.2% (40/42) and 95.2% (79/83) for the CAD scheme, respectively, and 79.2% (99/125), 90.9% (40/44), 72.8% (59/81), 64.5% (40/62) and 93.7% (59/63) for BI-RADS assessment, respectively. The area under the receiver operating characteristic curve (Az value) for the proposed CAD system using the combination of elastographic features was significantly higher than the Az value for visual assessment by the radiologists using BI-RADS (0.97 vs. 0.91). The results indicate that SSI elastography could be used for computer-aided feature extraction, and the proposed CAD method could improve the diagnostic accuracy of classification of breast tumors to avoid unnecessary biopsy. Furthermore, elastographic features of the peri-tumoral area have the potential to provide critical information in differential diagnosis. (E-mail: hr.zheng@siat.ac.cn) (C) 2014 World Federation for Ultrasound in Medicine & Biology.