2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.     

Computer-Aided Diagnosis Based on Quantitative Elastographic Features with Supersonic Shear Wave Imaging

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 (A_z value) for the proposed CAD system using the combination of elastographic features was significantly higher than the A_z 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.     

Computer-Aided Diagnosis for Breast Tumors by Using Vascularization of 3-D Power Doppler Ultrasound

This study assessed the accuracy of three-dimensional (3-D) power Doppler ultrasound in differentiating between benign and malignant breast tumors by using a support vector machine (SVM). A 3-D power Doppler ultrasonography was performed on 164 patients with 86 benign and 78 malignant breast tumors. The volume-of-interest (VOI) in 3-D ultrasound images was automatically generated from three rectangular regions-of-interest (ROI). The vascularization index (VI), flow index (FI) and vascularization-flow index (VFI) on 3-D power-Doppler ultrasound images were evaluated for the entire volume area, computer extracted VOI area and the area outside the VOI. Furthermore, patient's age and VOI volume were also applied for breast tumor classifications. Each ultrasonography in this study was classified as benign or malignant based on the features using the SVM model. All the tumors were sampled using k-fold cross-validation (k = 10) to evaluate the diagnostic performance with receiver operating characteristic (ROC) curves. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of SVM for classifying malignancies were 94%, 69%, 73%, 92% and 81%, respectively. The classification performance in terms of Az value for the ROC curve of the features derived from 3-D power Doppler is 0.91. This study indicates that combining 3-D power Doppler vascularity with patient's age and tumor size offers a good method for differentiating benign andmalignant breast tumors. (E-mail: ylhuang@thu.edu.tw (Y.-L.H.); darren_chen@cch.org.tw (D.-R.C.))     

Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound

The purpose of this study was to test the efficacy of using small training sets in computer-aided diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129 malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263 tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in future application of CAD, especially when a large database cannot be obtained for training or a newly developed ultrasonic system has smaller sets of samples.     

Logistic回归分析在超声多因素鉴别乳腺良恶性肿瘤中的应用

目的 探讨Logistic回归分析在超声多因素鉴别乳腺良恶性肿瘤中的应用价值.方法 选取经手术病理证实的乳腺恶性肿瘤患者119例,良性肿瘤患者47例,分析其声像图特征,包括乳腺肿块内部和后方回声、边缘特征、血流分布、收缩期峰值血流速度（PSV）及阻力指数（RI）.对所有患者的声像图特征行单因素分析,将有统计学差异的指标作多因素Logistic回归分析.结果 超声诊断乳腺恶性肿瘤111例,误诊8例,良性肿瘤44例,误诊3例,其对恶性肿瘤诊断的准确性、特异性及敏感性分别为93.4%、93.6%及93.3%,阳性预测值和阴性预测值分别为97.4%和84.6%.单因素分析显示所有观察指标在乳腺良恶性肿瘤的鉴别诊断中差异均有统计学意义（P〈0.05）.多因素分析显示边缘毛刺、微钙化、血流分布、阻力指数及纵横比〉1与乳腺恶性肿瘤有显著相关性（P〈0.05）.结论 超声多因素分析对乳腺良恶性肿瘤的鉴别诊断有重要价值     

Breast tumor classification using fuzzy clustering for breast elastography

Elastography is a new ultrasound imaging technique to provide the information about relative tissue stiffness. The elasticity information provided by this dynamic imaging method has proven to be helpful in distinguishing benign and malignant breast tumors. In previous studies for computer-aided diagnosis (CAD), the tumor contour was manually segmented and each pixel in the elastogram was classified into hard or soft tissue using the simple thresholding technique. In this paper, the tumor contour was automatically segmented by the level set method to provide more objective and reliable tumor contour for CAD. Moreover, the elasticity of each pixel inside each tumor was classified by the fuzzy c-means clustering technique to obtain a more precise diagnostic result. The test elastography database included 66 benign and 31 malignant biopsy-proven tumors. In the experiments, the accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic curve for the classification of solid breast masses were 83.5% (81/97), 83.9% (26/31), 83.3% (55/66) and 0.902 for the fuzzy c-means clustering method, respectively, and 59.8% (58/97), 96.8% (30/31), 42.4% (28/66) and 0.818 for the conventional thresholding method, respectively. The differences of accuracy, specificity and Az value were statistically significant (p < 0.05). We conclude that the proposed method has the potential to provide a CAD tool to help physicians to more reliably and objectively diagnose breast tumors using elastography.(E-mail: rfchang@csie.ntu.edu.tw)     

Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images

New automated whole breast ultrasound (ABUS) machines have recently been developed and the ultrasound (US) volume dataset of the whole breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student’s t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features (p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of breast tumors. (E-mail: rfchang@csie.ntu.edu.tw)     

灰阶超声鉴别诊断良、恶性乳腺肿瘤

目的探讨灰阶超声鉴别良、恶性乳腺肿瘤的价值。方法利用改进的Level Set变分模型对126例乳腺肿瘤的超声图像进行分割,提取肿瘤边界,分别计算16个形态特征参数,结合特征参数间的相关性及部分特征参数性质确定特征向量组合,最后用模糊C-均值方法鉴别乳腺肿瘤的良、恶性。结果 126例中,恶性肿瘤50例,良性肿瘤76例。通过Level Set模型得到了较好的分割良、恶性的准确率达80.95%(102/126),其敏感度、特异度、阳性预测值和阴性预测值分别为80.00%(40/50)、81.58%(62/76)、74.07%(40/54)和86.11%(62/72)。结论良、恶性乳腺肿瘤在形态上有较大差异,灰阶超声可有效鉴别乳腺肿瘤的性质。     

灰阶超声造影结合MRI:重新认识乳腺肿瘤生长方位

目的采用超声造影(CEUS)结合MRI观察乳腺肿瘤生长方位。方法收集103例接受检查和治疗的乳腺肿瘤患者,包括良性肿瘤35例(良性组)、恶性肿瘤68例(恶性组),常规超声(CUS)显示为非平行位生长,观察同切面CEUS显示的肿瘤生长方位;对其中20例行MR检查,观察肿瘤与邻近皮肤的关系,并与CEUS显示的肿瘤方位进行比较。结果CEUS后,良性组4例生长方位改变,31例无改变;恶性组59例生长方位改变,9例无改变(χ2=55.210,P<0.001)。以病理结果为金标准,根据CEUS生长方位改变诊断乳腺良恶性肿瘤的敏感度为93.65%(59/63),特异度77.50%(31/40),阳性预测值86.76%(59/68),阴性预测值88.57%(31/35)。MRI显示19例乳腺癌生长方位与CEUS一致(P=0.500)。CEUS判定肿瘤方位与MRI一致性良好(Kappa=0.828)。结论CUS显示非平行位生长乳腺良性肿瘤多与CEUS一致,而乳腺恶性肿瘤CEUS后多表现为平行位生长。CEUS与MRI显示肿瘤生长方位一致性良好。CEUS判定乳腺肿瘤生长方位较CUS更可靠。     

剪切波弹性成像鉴别乳腺良恶性肿瘤的价值

目的应用剪切波弹性成像(SWE)分析乳腺肿瘤的成像特点,定量及定性评价SWE对乳腺良、恶性肿瘤的鉴别诊断价值。方法收集我院病理证实的乳腺肿瘤女性患者52例,共69个病灶,行剪切波弹性成像检查,采集肿瘤二维及SWE图像,获得乳腺肿瘤的弹性模量值E(kPa):平均值(Emean)、最小值(Emin)、最大值(Emax)和标准差(SD),对照病理结果,分析良、恶性肿瘤SWE成像特点,比较良、恶性肿瘤的弹性模量值。比较二维与弹性图像测量良、恶性肿瘤最大径。结果 (1)乳腺良性肿瘤SWE典型表现:蓝色为主,颜色较均一;恶性肿瘤SWE典型表现:红色为主,颜色较杂乱。(2)乳腺良性肿瘤的Emean、Emax及SD均低于恶性肿瘤,Emin高于恶性肿瘤,P<0.05。(3)弹性图像测量良性肿瘤的最大径与二维图像测量比较,差异无统计学意义,P>0.05;弹性图像测量恶性肿瘤的最大径大于二维测量,P<0.05。结论乳腺良、恶性肿瘤有各自典型的SWE成像特点,鉴别良、恶性有较好的价值。     

三维超声冠状面成像鉴别乳腺实性病灶良恶性的应用价值

目的：探讨三维超声冠状面成像鉴别乳腺肿块良恶性的应用价值。方法观察分析97例患者106个乳腺实性病灶的二维和三维超声冠状面成像,对二维超声图像进行乳腺超声影像报告与数据系统（BI-RADS-US）分类,并与病理结果对照,计算二维超声对乳腺病灶良恶性病灶的鉴别诊断价值;根据良恶性病灶在三维超声冠状面上的声像图特征,建立Logistic回归模型,绘制受试者操作特性（ROC）曲线及计算曲线下面积来分析其对乳腺癌的诊断价值。结果106个乳腺病灶中,恶性病灶71个,良性病灶35个。二维超声诊断准确性85.8%,敏感度84.5%,特异度88.6%。多因素回归分析显示最后进入Logistic模型的特征分别为病灶边缘的成角或毛刺和“太阳征”。ROC曲线下面积为0.899,标准误为0.033,95%可信区间（0.834,0.965）。以成角或毛刺、“太阳征”为自变量的Logistic回归模型诊断乳腺肿块的准确性为88.7%（94/106）,敏感度为90.1%（64/71）,特异度为85.7%（30/35）,阳性预测值为92.8%（64/69）,阴性预测值为81.1%（30/37）。结论乳腺三维超声冠状面,特别是成角或毛刺征及“太阳征”在乳腺肿块的良恶性鉴别中具有重要价值。对于疑难病灶,三维超声冠状面上的信息有助于提高医生的诊断自信心。     

1.5 T MR影像特征与腮腺肿瘤病人良恶性诊断的关系及病理结果对比

目的探究并分析1.5 T MR影像特征与腮腺肿瘤病人良恶性诊断的关系及病理结果。方法选取2016年5月~2019年5月收治的早期腮腺肿瘤患者70例,所有患者均在我院进行1.5 T MR检查,并经病理诊断确诊。其中良性肿瘤51例（良性肿瘤组）,恶性肿瘤19例（恶性肿瘤组）。分析并比较良恶性肿瘤的MR及病理结果、良恶性肿瘤MR影像结果特点（包括位置、形态、密度、轮廓）、良恶性组MR时间信号曲线类型及峰值时间。结果MR诊断腮腺肿瘤结果与病理学结果差异无统计学意义（P>0.05）;腮腺良恶性肿瘤MR影像学结果比较,差异有统计学意义（P < 0.05）,良恶性肿瘤在位置、形态、密度、轮廓等表现差异均具有统计学意义（P < 0.05）;两组肿瘤MR动态特点比较,差异具有统计学意义（P < 0.05）,良性组持续型、廓清型（廓清率≥0.3%）、平坦型比率高于恶性组（P < 0.05）,恶性组廓清型（廓清率 < 0.3%）比率高于良性组（P < 0.05）,良性组峰值时间高于对照组,差异具有统计学意义（P < 0.05）。结论1.5 T MR在腮腺肿瘤病人良恶性诊断中具有较高的诊断效能,对于良恶性肿瘤的鉴别具有较大的价值,值得临床推广。      

Vascular Morphology and Tortuosity Analysis of Breast Tumor Inside and Outside Contour by 3-D Power Doppler Ultrasound

This study aimed to evaluate morphologic and tortuous features of vessels inside and outside the tumor region on three-dimensional power Doppler ultrasonography (PDUS) in 113 breast mass lesions, including 60 benign and 53 malignant tumors. Compared with benign lesions, malignant breast lesions had significantly larger values of vascular morphologic and tortuous features and larger tumor sizes. The receiver operating characteristic curve analysis and Student's t-test were used to estimate the performance of a proposed classification system using 13 vascular features and tumor size selected by the neural network. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value and the A_Z value of the diagnosis performance based on 14 features were 89.38% (101/113), 84.91% (45/53), 93.33% (56/60), 91.84% (45/49), 87.50% (56/64) and 0.9188, respectively. The three-dimensional PDUS morphologic and tortuous characteristics of blood vessels inside and outside breast mass lesions can be effectively used to classify benign and malignant tumors.     

3-D ultrasound texture classification using run difference matrix

Ultrasonography is one of the most useful diagnostic tools for human soft tissue and it is in routine use in nearly all hospitals and many physicians' offices and clinics. However, the diagnosis mostly depends upon the personal experiences of the physicians. Moreover, the surface features and internal architecture of a tumor are not easy to be demonstrated simultaneously using the conventional two-dimensional (2-D) ultrasound. Recently, three-dimensional (3-D) ultrasound has been developed and allows the physician to view the 3-D anatomy. 3-D breast US can provide transverse, longitudinal planes as well as in addition simultaneously the coronal plane. This additional information has been proved to be helpful for clinical applications. In this paper, a new approach of texture classification of 3-D ultrasound breast diagnosis using run difference matrix with neural networks is developed. The test 3-D US image database includes 54 malignant and 161 benign tumors. In the experiments, the area index A(z) under the ROC curve of the proposal 3-D RDM method can achieve 0.9680. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed 3-D RDM method is 91.9%(148/161), 88.9%(48/54), 93.5%(100/107), 87.3%(48/55), and 94.3%(100/105), respectively.     

Prospective sonographic study of 3093 breast tumors.

To evaluate the predictive ability of sonographic tumor characteristics to differentiate benign from malignant tumors, we examined 3093 breast tumors (2360 benign and 733 malignant tumors) with ultrasonography. The ratio of the longest dimension to the anteroposterior diameter of benign tumors was significantly larger than that of malignant tumors (1.88+/-0.1 versus 1.69+/-0.02, P < 0.0001). Shape, margins, echogenicity, internal echo pattern, retrotumor acoustic shadowing, compressibility, and microcalcification were significant factors in the logistic regression model. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of breast sonography for malignancy were 86.1, 66.1, 44.1, 93.9, and 70.8%, respectively. Biopsy of the tumor for pathologic diagnosis is recommended if sonographic features are suggestive of malignancy.     

实时组织弹性成像技术在鉴别诊断乳腺良恶性肿块中的价值评估

目的评价组织弹性成像技术在鉴别诊断乳腺良、恶性肿块中的价值。方法用组织弹性成像技术对110例患者的115个乳腺肿块进行良、恶性的鉴别诊断，并与术后的病理结果进行对照。结果组织弹性成像技术对乳腺肿块良、恶性鉴别诊断的准确性、灵敏性和特异性分别为84．3％（97／115）、78.8％（26／33）和86.6％（71／82）。结论组织弹性成像为超声鉴别诊断良、恶性乳腺肿块提供了一个新的手段，尤其在一些边界不清、形态不规则病灶的良、恶性鉴别诊断中具有较高的临床应用价值。     

三维彩色血管能量成像对乳腺肿物血管的定量研究

目的探讨三维彩色血管能量成像（3D—CPA）定量检测乳腺肿物内血管参数对鉴别诊断肿物良恶性的临床价值。方法61例乳腺肿瘤患者,良性组31例,恶性组30例,对其行3D—CPA重建。采用Vocal分析软件,选择三维能量直方图获得血管形成指数（vI）、血流指数（FI）和血管形成一血流指数（VFI）;并对乳腺肿物进行3D—CPA血流分级,比较乳腺良恶性肿物vI、FI、VFI及其与乳腺肿物内血流分级的关系。结果3D—CPA血流Ⅲ级患者的VI、FI、VFI高于Ⅱ级（P〈0．05）,以Ⅲ级作为诊断乳腺良恶性标准,敏感性、特异性、准确性分别为91．3％、76．3％、82．0％。恶性组VI、FI、VFI均高于良性组（P〈0．05）。以vI≥1．157诊断乳腺恶性肿瘤的敏感性分别为87％,准确性为77％,特异性为68％;以FI≥32．397诊断乳腺恶性肿瘤的敏感性为83％,准确性为67％,特异性为52％;以VFI／〉0．426诊断乳腺恶性肿瘤的敏感性为83％,准确性为73％,特异性为61％。结论乳腺肿物3D—CPA血管定量参数VI、FI、VFI与乳腺肿物血流分级结果一致,可用来判断乳腺肿物内部血管丰富程度,恶性肿物参数均高于良性肿物,有助于乳腺良恶性肿瘤的鉴别。     

BIRADS联合CEUS鉴别诊断乳腺肿瘤的良恶性

目的 探讨乳腺影像报告和数据系统(BIRADS)分类联合CEUS鉴别诊断乳腺肿瘤良恶性的价值。方法 对490例患者共524个病灶进行乳腺常规超声和CEUS检查,以病理为金标准,比较BIRADS分类及BIRADS分类联合CEUS诊断乳腺肿瘤良恶性的效能。结果 524个病灶中,良性病灶232个,恶性病灶292个。BIRADS分类诊断乳腺恶性肿瘤的特异度17.24%(40/232)、敏感度99.32%(290/292)、准确率62.98%(330/524)、阳性预测值60.17%(290/482)、阴性预测值95.24%(40/42),ROC曲线下面积0.583。BIRADS分类联合CEUS后诊断乳腺恶性肿瘤的特异度90.09%(209/232)、敏感度89.04%(260/292)、准确率89.50%(469/524)、阳性预测值91.87%(260/283)、阴性预测值86.72%(209/241),ROC曲线下面积0.896;两者曲线下面积差异有统计学意义(P<0.05)。结论 BIRADS联合CEUS有利于对乳腺肿瘤的鉴别诊断。     

Computer-aided diagnosis based on speckle patterns in ultrasound images

For breast ultrasound, the scatterer number density from backscattered echo was demonstrated in previous research to be a useful feature for tumor characterization. To take advantage of the scatterer number density in B-mode images, spatial compound imaging was obtained, and the statistical properties of speckle patterns were analyzed in this study for use in distinguishing between benign and malignant lesions. A total of 137 breast masses (95 benign cases and 42 malignant cases) were used in the proposed computer-aided diagnosis (CAD) system. For each mass, the average number of speckle pixels in a region of interest (ROI) was calculated to use the concept of scatterer number density. In addition, the first-order and second-order statistics of the speckle pixels were quantified to obtain the distributions of the pixel values and the spatial relations among the pixels. The performance of the speckle features extracted from each ROI was compared with the performance of the segmentation features extracted from each segmented tumor. As a result, the proposed CAD system using the speckle features achieved an accuracy of 89.1% (122/137); a sensitivity of 81.0% (34/42); and a specificity of 92.6% (88/95). All of the differences between the speckle features and the segmentation features are not statistically significant (p > 0.05). In a receiver operating characteristic (ROC) curve analysis, the Az value, area under ROC curve, of the speckle features was significantly better than the Az value of the segmentation features (0.93 vs. 0.86, p = 0.0359). The performance of this approach supports the notion that the speckle patterns induced by the scatterers in tissues can provide information for classifying tumors. The proposed speckle features, which were extracted readily from drawing an ROI without any preprocessing, also provide a more efficient classification approach than tumor segmentation.     

三维超声汇聚征诊断乳腺癌

目的 探讨乳腺三维超声冠状面成像显示汇聚征诊断乳腺癌的价值。方法 观察128例乳腺肿瘤患者(共132个病灶)的三维超声汇聚征和二维超声毛刺征显示情况,与病理结果对照,比较二者对乳腺癌的诊断价值,分析汇聚征显示率与肿瘤大小之间的关系。结果 132个乳腺肿块中,76个为乳腺癌,56个为乳腺良性病变,汇聚征和毛刺征诊断乳腺癌的敏感度、特异度、准确率、阳性预测值和阴性预测值分别为63.16%(48/76)、92.86%(52/56)、75.76%(100/132)、92.31%(48/52)、65.00%(52/80)和44.74%(34/76)、91.07%(51/56)、64.39%(85/132)、87.18%(34/39)、54.84%(51/93)。汇聚征对于诊断乳腺癌的敏感度明显高于毛刺征(P<0.05)。汇聚征的显示率随着乳腺肿块直径增加而逐渐降低(P<0.01)。结论 乳腺超声实时三维成像显示汇聚征对于乳腺癌的诊断价值高于二维超声显示毛刺征。汇聚征对于诊断乳腺癌具有较高特异度,对小乳癌有较高敏感度,对于早期乳腺癌的诊断和鉴别诊断具有重要价值。