Department of Radiology and Clinical Research Institute, Seoul National University Hospital and the Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
AJR Am J Roentgenol. 2010 Dec;195(6):1460-5. doi: 10.2214/AJR.09.3140.
The purpose of this study was to evaluate computer-aided analysis of ultrasound elasticity images for the classification of benign and malignant breast tumors.
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.
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).
Computer-aided analysis of ultrasound elasticity images has the potential to aid in the classification of benign and malignant breast tumors.
本研究旨在评估超声弹性成像的计算机辅助分析在良性和恶性乳腺肿瘤分类中的应用。
对 140 名（平均年龄 46 岁；年龄范围 35-67 岁）经触诊无法触及的乳腺肿块（101 例良性，39 例恶性）的患者进行实时超声弹性成像检查。在进行针吸活检之前，在肿块边缘周围画一个感兴趣区域（ROI），并为每个像素分配一个分数；分数范围从最大应变的 0 到无应变的 255。比较基于六个弹性特征值的神经网络诊断性能与弹性图像的视觉评估以及 B 型超声图像的 BI-RADS 评估。
六个弹性特征值（平均色调直方图值、偏度、峰度、直方图差值变化、边缘密度和游程长度）的受试者工作特征曲线下面积（A(z)）值分别为 0.84、0.69、0.63、0.75、0.68 和 0.71。基于所有六个特征的神经网络的灵敏度、特异性、阳性预测值和阴性预测值分别为 92%（36/39）、74%（75/101）、58%（36/62）和 96%（75/78），A(z)值为 0.89，明显高于放射科医生视觉评估的 A(z)值 0.81（p = 0.01）和 B 型超声图像 BI-RADS 评估的 A(z)值 0.76（p = 0.002）。
超声弹性成像的计算机辅助分析有可能辅助良性和恶性乳腺肿瘤的分类。
