实性乳腺肿块：通过对探头加压获取的连续超声图像进行计算机辅助分析进行分类。
Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression.
作者信息
Moon Woo Kyung, Chang Ruey-Feng, Chen Chii-Jen, Chen Dar-Ren, Chen Wei-Liang
机构信息
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.
出版信息
Radiology. 2005 Aug;236(2):458-64. doi: 10.1148/radiol.2362041095.
PURPOSE
To prospectively evaluate the accuracy of continuous ultrasonographic (US) images obtained during probe compression and computer-aided analysis for classification of biopsy-proved (reference standard) benign and malignant breast tumors.
MATERIALS AND METHODS
This study was approved by the local ethics committee, and informed consent was obtained from all included patients. Serial US images of 100 solid breast masses (60 benign and 40 malignant tumors) were obtained with US probe compression in 86 patients (mean age, 45 years; range, 20-67 years). After segmentation of tumor contours with the level-set method, three features of strain on tissue from probe compression--contour difference, shift distance, area difference--and one feature of shape--solidity-were computed. A maximum margin classifier was used to classify the tumors by using these four features. The Student t test and receiver operating characteristic curve analysis were used for statistical analysis.
RESULTS
The mean values of contour difference, shift distance, area difference, and solidity were 3.52% +/- 2.12 (standard deviation), 2.62 +/- 1.31, 1.08% +/- 0.85, and 1.70 +/- 1.85 in malignant tumors and 9.72% +/- 4.54, 5.04 +/- 2.79, 3.17% +/- 2.86, and 0.53 +/- 0.63 in benign tumors, respectively. Differences with P < .001 were statistically significant for all four features. Area under the receiver operating characteristic curve (A(Z)) values for contour difference, shift distance, area difference, and solidity were 0.88, 0.85, 0.86, and 0.79, respectively. The A(Z) value of three features of strain was significantly higher than that of the feature of shape (P < .01). The accuracy, sensitivity, specificity, and positive and negative predictive values of US classifications that were based on values for these four features were 87.0% (87 of 100), 85% (34 of 40), 88% (53 of 60), 83% (34 of 41), and 90% (53 of 59), respectively, with an A(Z) value of 0.91.
CONCLUSION
Continuous US images obtained with probe compression and computer-aided analysis can aid in classification of benign and malignant breast tumors.
目的
前瞻性评估在探头加压过程中获取的连续超声（US）图像以及计算机辅助分析对经活检证实（参考标准）的乳腺良恶性肿瘤进行分类的准确性。
材料与方法
本研究经当地伦理委员会批准，并获得所有纳入患者的知情同意。对86例患者（平均年龄45岁；范围20 - 67岁）的100个乳腺实性肿块（60个良性肿瘤和40个恶性肿瘤）进行超声探头加压并获取系列US图像。采用水平集方法分割肿瘤轮廓后，计算探头加压时组织应变的三个特征——轮廓差异、位移距离、面积差异——以及一个形状特征——实性。使用最大边界分类器利用这四个特征对肿瘤进行分类。采用Student t检验和受试者操作特征曲线分析进行统计分析。
结果
恶性肿瘤的轮廓差异、位移距离、面积差异和实性的平均值分别为3.52%±2.12（标准差）、2.62±1.31、1.08%±0.85和1.70±1.85，良性肿瘤分别为9.72%±4.54、5.04±2.79、3.17%±2.86和0.53±0.63。所有四个特征的P值均<0.001，差异具有统计学意义。轮廓差异、位移距离、面积差异和实性的受试者操作特征曲线下面积（A(Z)）值分别为0.88、0.85、0.86和0.79。应变的三个特征的A(Z)值显著高于形状特征的A(Z)值（P<0.01）。基于这四个特征值的US分类的准确性、敏感性、特异性、阳性和阴性预测值分别为87.0%（100例中的87例）、85%（40例中的34例）、88%（60例中的53例）、83%（41例中的34例）和90%（59例中的53例），A(Z)值为0.91。
结论
通过探头加压和计算机辅助分析获得的连续US图像有助于乳腺良恶性肿瘤的分类。
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