Diagnostic Performance Using Automated Breast Ultrasound System for Breast Cancer in Chinese Women Aged 40 Years or Older: A Comparative Study

The purpose of this study was to investigate the diagnostic performance of the automated breast ultrasound system (ABUS) compared with hand-held ultrasonography (HHUS) and mammography (MG) for breast cancer in women aged 40 y or older. A total of 594 breasts in 385 patients were enrolled in the study. HHUS, ABUS and MG exams were performed for these patients. Follow-up and pathologic findings were used as the reference standard. Based on the reference standard, 519 units were benign or normal and 75 were malignant. The sensitivity, specificity, accuracy and Youden index were 97.33%, 89.79%, 90.74% and 0.87 for HHUS; 90.67%, 92.49%, 92.26% and 0.83 for ABUS; 84.00%, 92.87%, 91.75% and 0.77 for MG, respectively. The specificity of ABUS was significantly superior to that of HHUS (p = 0.024). The area under the receiver operating characteristic curve was 0.936 for HHUS, which was the highest, followed by 0.916 for ABUS and 0.884 for MG. However, the difference was not statistically significant (p > 0.05). In conclusion, the diagnostic performance of ABUS for breast cancer was equivalent to HHUS and MG and potentially can be used as an alternative method for breast cancer diagnosis.     

Diagnostic Performance of Automated Breast Ultrasound as a Replacement for a Hand-Held Second-Look Ultrasound for Breast Lesions Detected Initially on Magnetic Resonance Imaging

To evaluate the diagnostic performance of automated breast ultrasound (ABUS) after breast magnetic resonance imaging (MRI) as a replacement for hand-held second-look ultrasound (HH-SLUS), we evaluated 58 consecutive patients with breast cancer who had additional suspicious lesions on breast MRI. All patients underwent HH-SLUS and ABUS. Three breast radiologists evaluated the detectability, location, characteristics and conspicuity of lesions on ABUS. We also evaluated inter-observer variability and compared the results with HH-SLUS results. Eighty additional suspicious lesions were identified on breast MRI. Fifteen of the 80 lesions (19%) were not detected on HH-SLUS. Eight of the 15 lesions (53%) were detected on ABUS, whereas the remaining 7 were not detected on ABUS. Among the 65 lesions detected on HH-SLUS, only 3 lesions were not detected on ABUS. The intra-class correlation coefficients for lesion location and size all exceeded 0.70, indicating high reliability. Moderate to fair agreement was found for mass shape, orientation, margin and Breast Imaging Reporting and Data System (BI-RADS) final assessment. Therefore, ABUS can reliably detect additional suspicious lesions identified on breast MRI and may help in the decision on biopsy guidance method (US vs. MRI) as a replacement tool for HH-SLUS.     

超声弹性成像及硬环征、直方图分析鉴别乳腺肿块良恶性的诊断价值

目的：本研究旨在比较弹性成像、弹性成像联合“硬环征”、弹性成像直方图分析的诊断价值。方法：选取2017年1月至2019年1月间于陆军军医大学第二附属医院普通外科就诊的共59个乳房肿块构成本研究组。启动弹性成像功能采集数据,然后在弹性成像图上调节阈值显示硬环征,之后勾选直方图分析。结果：直方图分析的诊断性能(AUC=0.93)优于弹性成像联合“硬环征”(AUC= 0.87)和弹性成像(AUC=0.81)。结论：超声弹性成像直方图分析方法有助于乳腺良、恶性病变的分类,“硬环征”也可提高弹性成像的准确性,有助于提高医师鉴别乳腺肿块良恶性的能力。     

Deformable Mapping Method to Relate Lesions in Dedicated Breast CT Images to Those in Automated Breast Ultrasound and Digital Breast Tomosynthesis Images

This work demonstrates the potential for using a deformable mapping method to register lesions between dedicated breast computed tomography (bCT) and both automated breast ultrasound (ABUS) and digital breast tomosynthesis (DBT) images (craniocaudal [CC] and mediolateral oblique [MLO] views). Two multi-modality breast phantoms with external fiducial markers attached were imaged by the three modalities. The DBT MLO view was excluded for the second phantom. The automated deformable mapping algorithm uses biomechanical modeling to determine corresponding lesions based on distances between their centers of mass (d_COM) in the deformed bCT model and the reference model (DBT or ABUS). For bCT to ABUS, the mean d_COM was 5.2 ± 2.6 mm. For bCT to DBT (CC), the mean d_COM was 5.1 ± 2.4 mm. For bCT to DBT (MLO), the mean d_COM was 4.7 ± 2.5 mm. This application could help improve a radiologist's efficiency and accuracy in breast lesion characterization, using multiple imaging modalities.     

弹性成像结合卡方自动交互检测法在鉴别乳腺肿块良恶性中的应用

目的利用卡方自动交互检测法开发一种简单的算法来鉴别乳腺肿块的良恶性。方法经过病理证实的201例乳腺肿块患者纳入此项研究,每例患者都有一个乳腺肿块经常规超声检查、声触诊组织成像(VTI)及声触诊组织量化(VTQ)技术成像,并测量出VTI面积比(肿块VTI面积或肿块二维面积)及肿块VTQ或腺体VTQ。利用受试者操作特性曲线评价各项超声弹性成像参数的诊断性能,再使用卡方自动交互检测法进行分类分析。结果分类算法包括肿块VTQ及VTI面积比,其深度为两个分支(肿块VTQ>3.958或≤3.958,如果肿块VTQ≤3.958接着考虑VTI面积比≤1.304或1.304~1.493或>1.493)。分类算法的AUC为0.901、灵敏度为98.2%、特异度为68.1%,应用该算法,有30.8%的病例可以避免活检。结论联合应用肿块VTQ及VTI面积比的分类算法具有较高的诊断性能,准确度达97%,减少30%以上不必要的穿刺活检。     

Comparison of Strain and Shear Wave Elastography for the Differentiation of Benign From Malignant Breast Lesions,Combined With B-mode Ultrasonography: Qualitative and Quantitative Assessments

Our aim was to compare the diagnostic performance of strain elastography (SE) and shear-wave elastography (SWE), combined with B-mode ultrasonography (US), in breast cancer. For 79 breast lesions that underwent SE and SWE, two radiologists reviewed five data sets (B-mode US, SWE, SE and two combined sets). Qualitative and quantitative elastographic data and Breast Imaging Reporting and Data System (BI-RADS) categories were recorded. The area under the receiver operating characteristic curve (AUC) was evaluated. No significant difference in the AUC between the two elastography methods was noted. After subjective assessment by reviewers, the AUC for the combined sets was improved (SWE, 0.987; SE, 0.982; B-mode US, 0.970; p < 0.05). When SE and SWE were added, 38% and 56% of benign BI-RADS category 4a lesions with a low suspicion of cancer were downgraded without false-negative results, respectively. SE and SWE performed similarly. Therefore, addition of SE or SWE improved the diagnostic performance of B-mode US, potentially reducing unnecessary biopsies.     

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)     

应用去噪后增强算法辅助乳腺肿块的超声诊断研究

目的探讨超声图像去噪后增强算法对乳腺肿块良恶性检测与分类的价值。方法选用211例603幅乳腺肿块超声图片(良性109例,恶性102例)进行去噪后增强处理,以手术病理结果作为金标准,对乳腺肿块原始图片和处理后图片进行分析,来区分乳腺肿块的良、恶性。利用ROC曲线下面积表现去噪后增强前后的诊断性能,计算超声诊断的准确率。结果通过去噪后增强算法处理后,使腺体和周围组织能分离,突出了腺体和病灶的部位,细节显示更加清晰,超声与病理诊断的各项指标符合率明显提高,准确率提高至92.73%,原片与处理后图片ROC曲线下面积二者之间差异有显著性统计学意义(P0.001)。结论新的超声图像去噪后增强算法可明显地改善了图像质量,提高了乳腺肿块的正确诊断率。     

Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images

Tumor classification and segmentation are two important tasks for computer-aided diagnosis (CAD) using 3D automated breast ultrasound (ABUS) images. However, they are challenging due to the significant shape variation of breast tumors and the fuzzy nature of ultrasound images (e.g., low contrast and signal to noise ratio). Considering the correlation between tumor classification and segmentation, we argue that learning these two tasks jointly is able to improve the outcomes of both tasks. In this paper, we propose a novel multi-task learning framework for joint segmentation and classification of tumors in ABUS images. The proposed framework consists of two sub-networks: an encoder-decoder network for segmentation and a light-weight multi-scale network for classification. To account for the fuzzy boundaries of tumors in ABUS images, our framework uses an iterative training strategy to refine feature maps with the help of probability maps obtained from previous iterations. Experimental results based on a clinical dataset of 170 3D ABUS volumes collected from 107 patients indicate that the proposed multi-task framework improves tumor segmentation and classification over the single-task learning counterparts.     

Quantification of Elastic Heterogeneity Using Contourlet-Based Texture Analysis in Shear-Wave Elastography for Breast Tumor Classification

Ultrasound shear-wave elastography (SWE) has become a valuable tool for diagnosis of breast tumors. The purpose of this study was to quantify the elastic heterogeneity of breast tumors in SWE by using contourlet-based texture features and evaluating their diagnostic performance for classification of benign and malignant breast tumors, with pathologic results as the gold standard. A total of 161 breast tumors in 125 women who underwent B-mode and SWE ultrasonography before biopsy were included. Five quantitative texture features in SWE images were extracted from the directional subbands after the contourlet transform, including the mean (T_mean), maximum (T_max), median (T_med), third quartile (T_qt), and standard deviation (T_sd) of the subbands. Diagnostic performance of the texture features and the classic features was compared using the area under the receiver operating characteristic curve (AUC) and the leave-one-out cross validation with Fisher classifier. The feature T_mean achieved the highest AUC (0.968) among all features and it yielded a sensitivity of 89.1%, a specificity of 94.3% and an accuracy of 92.5% for differentiation between benign and malignant tumors via the leave-one-out cross validation. Compared with the best classic feature, i.e., the maximum elasticity, T_mean improved the AUC, sensitivity, specificity and accuracy by 3.5%, 12.7%, 2.8% and 6.2%, respectively. The T_med, T_qt and T_sd were also superior to the classic features in terms of the AUC and accuracy. The results demonstrated that the contourlet-based texture features captured the tumor's elastic heterogeneity and improved diagnostic performance contrasted with the classic features.     

Whole-Breast Ultrasound for Breast Screening and Archiving

The incidence of breast cancer is increasing worldwide, reinforcing the importance of breast screening. Conventional hand-held ultrasound (HHUS) for breast screening is efficient and relatively easy to perform; however, it lacks systematic recording and localization. This study investigated an electromagnetic tracking-based whole-breast ultrasound (WBUS) system to facilitate the use of HHUS for breast screening. One-hundred nine breast masses were collected, and the detection of suspicious breast lesions was compared between the WBUS system, HHUS and a commercial automated breast ultrasound (ABUS) system. The positioning error between WBUS and ABUS (1.39 ± 0.68 cm) was significantly smaller than that between HHUS and ABUS (1.62 ± 0.91 cm, p = 0.014) and HHUS and WBUS (1.63 ± 0.9 cm, p = 0.024). WBUS is a practical clinical tool for breast screening that can be used instead of the often unavailable and costly ABUS.     

声触弹性成像在乳腺良性病变中的应用价值

目的探讨声触弹性成像(STE)技术在乳腺良性病变中的应用价值。方法对95个乳腺良性病变及35例正常乳腺组织进行常规二维超声扫查及声触弹性成像检测,获取不同组织类型弹性模量值,以穿刺活检或手术病理结果为金标准,分析正常乳腺组、纤维腺瘤组、乳腺腺病组各弹性参数的差异性。结果正常乳腺腺体组、纤维腺瘤组、乳腺腺病组的肿块最大径、Amean、Amax、Shellmean、Shellmax、A′mean、A′max有统计学意义(P<0.05);多因素回归分析显示三个参数Amean&Shellmean&A′mean联合后AUC值为0.803、灵敏度为0.667%、特异度为0.809%。结论 STE成像弹性参数中平均值Emean (包括Amean、Shellmean、A′mean)对于纤维腺瘤及乳腺腺病具有较好的诊断效能,联合Amean&Shellmean&A′mean三者弹性参数指标诊断的价值更高。     

应用增强算法辅助乳腺肿块的超声诊断研究

目的 探讨基于模糊逻辑和纹理分析的增强算法对超声图像乳腺肿块良恶性的检测与分类的价值.方法 研制增强算法和软件程序,选用211个病例603张乳腺肿块超声图片(其中良性109例,恶性102例)进行增强处理,以手术病理结果作为金标准,超声专家通过对原始乳腺肿块图片和处理后乳腺肿块图片进行分析,区分乳腺肿块的良、恶性,利用ROC曲线下面积(Az)表示增强前后的诊断性能,得出其敏感性、特异性、阳性预测值及阴性预测值,计算常规超声检查和增强后诊断的正确诊断率.结果 增强后乳腺肿块的超声诊断结果与病理诊断结果符合率明显提高,敏感性从原片的75.4%提高至89.6%,特异性从66.7%提高至91.2%.准确率从78.20%提高至89.57%.ROC曲线计算出增强前、后乳腺图片对乳腺肿块的定性诊断Az面积:原始图片A1=0.842,增强图片A2=0.914,Z值为5.101,二者之间差异有显著统计学意义(P<0.001).结论 新的超声图像增强算法明显改善了图像质量,提高了乳腺肿块的正确诊断率,减低误诊率,可为乳腺肿块良,恶性的诊断提供可靠依据.     

边界增强像素级多模态声像图融合方法评估良、恶性乳腺肿瘤

目的 观察以边界增强多模态乳腺声像图像素级特征融合方法评估良、恶性乳腺肿瘤性质的价值。方法 基于乳腺肿瘤B型声像图提取边界增强图像,于超声弹性复合声像图中提取纯弹性信息图像。对多模态乳腺肿瘤声像图进行像素级特征融合,形成边界特征增强的融合图像,再以卷积神经网络(CNN)进行分类;评估融合方法分类良、恶性乳腺肿瘤的性能,并与单模态方法、特征级融合方法、无边界增强像素级图像融合方法及其他CNN模型进行对比。结果 边界增强像素级特征融合方法有助于CNN提取乳腺肿瘤特征,分类良、恶性乳腺性能最佳,其分类准确率为85.71%,特异度为85.49%,敏感度为86.16%,模型稳定。结论 边界特征增强像素级多模态声像图融合方法可用于判断良、恶性乳腺肿瘤。     

自动乳腺超声诊断系统（ABUS）结合计算机辅助诊断系统（CAD）前后对乳腺恶性病灶的诊断价值对比

目的 本研究旨在探讨应用自动乳腺超声诊断系统（ABUS）结合计算机辅助诊断系统（CAD）对于不同经验医师诊断乳腺恶性病灶的价值。 方法 收集行ABUS检查的乳腺病例1452例,结果均经病理或随访1年以上证实。比较6名医师（3名低年资医师和3名高年资医师）使用CAD系统前后的诊断敏感度、特异度、AUC及平均阅读时间。结果 1452例病例中,270例为恶性,共有282个恶性病灶,674例为良性,共有695个良性病灶,508例为阴性。应用CAD系统前,低年资与高年资医师诊断乳腺癌的敏感度分别为87%、93%,使用CAD后提高到94%、94%,低年资医师的诊断敏感度前后差异具有统计学意义（P<0.05）,高年资医师差异无统计学意义（P＞0.05）。6名医师在使用CAD系统前后诊断特异度均略有下降,但差异均无统计学意义（P＞0.05）。低年资医师在使用CAD系统前后的诊断符合率有所提高,ROC曲线下面积由0.85提高到0.89,差异具有统计学意义（P<0.05）。而高年资医师组,虽然ROC曲线下面积由0.91提高到0.92,但差异不具有统计学意义（P＞0.05）。所有医师使用CAD后的平均阅读时间均有不同程度的延长,差异具有统计学意义（P<0.05）。结论 虽然使用CAD后的平均阅读时间有所延长,但在可接受范围内,借助ABUS-CAD的阅读模式能大大提高医生诊断的准确度和敏感度,对于低年资医师帮助更大。     

基于卷积神经网络的病理活检胃癌诊断模型

目的 基于深度学习技术,建立胃活检病理切片胃癌诊断模型,并对模型的性能进行评价。方法 回顾性收集2015年1月—2020年1月浙江省人民医院胃活检诊断为正常胃黏膜、慢性胃炎、高级别上皮内瘤变和胃腺癌患者的病理切片。以20倍率扫描为全视野数字图像(whole slide image, WSI),并按2∶2∶1的比例随机分为图块分类数据集、切片分类训练集与切片分类测试集。对图块分类数据集病变区域进行标注、图块截取后,按20∶1∶1的比例随机分为训练集、测试集、验证集。基于Efficientnet和ResNet网络结构构建卷积神经网络(convolutional neural network, CNN)图块级癌与非癌分类模型,并以图块分类准确率、受试者操作特征曲线下面积(area under the curve, AUC)评价该模型的性能。基于此模型拼接获取整张WSI的癌变热力图,提取热力图中切片级癌与非癌分类特征,对LightGBM算法进行训练,最终完成整张胃癌活检切片的诊断与识别,其识别结果以AUC、准确率、灵敏度、特异度进行评价。结果 共入选符合纳入和排除标准的胃良性疾病(正常胃黏膜、...     

Deformable mapping using biomechanical models to relate corresponding lesions in digital breast tomosynthesis and automated breast ultrasound images

This work investigates the application of a deformable localization/mapping method to register lesions between the digital breast tomosynthesis (DBT) craniocaudal (CC) and mediolateral oblique (MLO) views and automated breast ultrasound (ABUS) images. This method was initially validated using compressible breast phantoms. This methodology was applied to 7 patient data sets containing 9 lesions. The automated deformable mapping algorithm uses finite element modeling and analysis to determine corresponding lesions based on the distance between their centers of mass (d_COM) in the deformed DBT model and the reference ABUS model. This technique shows that location information based on external fiducial markers is helpful in the improvement of registration results. However, use of external markers are not required for deformable registration results described by this methodology. For DBT (CC view) mapped to ABUS, the mean d_COM was 14.9 ± 6.8 mm based on 9 lesions using 6 markers in deformable analysis. For DBT (MLO view) mapped to ABUS, the mean d_COM was 13.7 ± 6.8 mm based on 8 lesions using 6 markers in analysis. Both DBT views registered to ABUS lesions showed statistically significant improvements (p ≤ 0.05) in registration using the deformable technique in comparison to a rigid registration. Application of this methodology could help improve a radiologist's characterization and accuracy in relating corresponding lesions between DBT and ABUS image datasets, especially for cases of high breast densities and multiple masses.     

计算机辅助多模态融合超声诊断乳腺良恶性肿瘤

目的 观察利用深度学习（DL）融合常规超声和超声弹性成像诊断乳腺良、恶性肿瘤的效能。方法 利用DL卷积神经网络（CNN）提取乳腺肿瘤超声灰阶与超声弹性特征,并进行多模态融合,评价融合弹性图像或弹性比值等不同信息方式对乳腺良、恶性肿瘤的诊断效能;绘制受试者工作特征（ROC）曲线,计算曲线下面积（AUC）,评估多模态融合模型的诊断效能。结果 多模态融合模型鉴别乳腺良、恶性肿物的效能优于单模态常规超声或弹性模型,其中融合灰阶与弹性图像模型鉴别诊断效能优于融合灰阶与弹性比值模型,分类准确率达93.51%,敏感度为94.88%,特异度为92.25%,AUC达0.975。结论 计算机辅助多模态融合有助于提高超声对乳腺良、恶性肿瘤的诊断效能。     

乳腺癌患者高频彩色多普勒超声血流特点及阻力系数的变化

目的探讨乳腺癌患者采用高频彩色多普勒超声检查的血流阻力指数（RI）、血流特点、声像特征及其诊断价值。方法选取2019年1月~2021年1月安徽省肿瘤医院甲乳外科经病理学确诊的96例乳腺癌患者（病例组）、同期经病理学检查确诊的乳腺良性疾病患者100例（良性组）,对两组患者的高频彩色多普勒超声检查资料进行分析,对比两组的RI、血流特点、声像特征差异,并以病理学结果作为金标准计算各项指标在诊断乳腺癌中的价值。结果病例组的肿块呈不规则形状、边缘毛刺征、微钙化病灶、边界模糊的检出率均高于良性组（P < 0.05）;肿块形状特征诊断乳腺癌与乳腺良性疾病的敏感度为66.67%,特异性为60.00%,ROC曲线下面积为0.633;边缘毛刺征诊断乳腺癌与乳腺良性疾病的敏感度为73.96%,特异性为58.00%,ROC曲线下面积为0.660;微钙化病灶诊断乳腺癌与乳腺良性疾病的敏感度为31.25%,特异性为91.00%,ROC曲线下面积为0.611;边界模糊诊断乳腺癌与乳腺良性疾病的敏感度为27.08%,特异性为89.00%,ROC曲线下面积为0.580;病例组的血流分级主要为Ⅱ级（48.96%）、Ⅲ级（27.08%）,良性组的血流分级主要为0级（53.00%）、Ⅰ级（24.00%）,两组差异有统计学意义（P < 0.05）;血流分级诊断乳腺癌与乳腺良性疾病的敏感度为76.04%,特异性为77.00%,ROC曲线下面积为0.765;病例组的RI值低于良性组（P < 0.05）;病灶RI值诊断乳腺癌与乳腺良性疾病的敏感度为77.03%,特异性为55.17%,ROC曲线下面积为0.681。结论根据高频彩色多普勒超声检查的声像特征、血流分级、RI参数鉴别诊断乳腺癌及乳腺良性疾病均具有一定的临床价值,临床上可以综合几种指标分析,提高临床的诊断效率。      

应变式弹性成像与剪切波弹性成像技术鉴别诊断乳腺良恶性病灶的价值比较

目的比较应变式弹性成像（SE）与剪切波弹性成像（SWE）技术鉴别诊断乳腺良恶性病灶的价值。 方法选择2015年1至12月南京医科大学第一附属医院收治的乳腺疾病患者150例共155个乳腺病灶进行SE检查,用改良5分法对病灶进行弹性评分,测量应变率比值（SR）;对病灶进行SWE检查,测量最大弹性模量值E_max、平均弹性模量值E_mean、弹性标准差E_sd、病灶/脂肪弹性比E_ratio。绘制SE、SWE弹性参数鉴别诊断乳腺良恶性病灶构建操作者工作特征（ROC）曲线,计算曲线下面积（AUC）。选取AUC最大的SE及SWE参数,采用McNemar检验比较其鉴别诊断不同乳腺影像报告与数据系统（BI-RADS）分类乳腺良恶性病灶的准确性。 结果ROC曲线显示,SE参数弹性评分及SR,SWE参数E_max、E_mean、E_sd及E_ratio鉴别诊断乳腺良恶性病灶ROC的AUC分别为0.823、0.810、0.877、0.835、0.881、0.853。ROC的AUC最大的SE、SWE弹性参数分别为弹性评分、E_sd。E_sd鉴别诊断BI-RADS 4A类乳腺良恶性病灶的准确性高于弹性评分（86.3% vs 64.7%）,且差异有统计学意义（χ²=4.639,P<0.05）;E_sd鉴别诊断BI-RADS 3类、4B类乳腺良恶性病灶的准确性高于弹性评分,且弹性评分鉴别诊断BI-RADS 4C类及5类乳腺良恶性病灶的准确性高于E_sd,但差异均无统计学意义。 结论SE和SWE技术对乳腺良恶性病灶鉴别诊断均有较高的诊断价值,且价值相当。与BI-RADS分类相结合可以优化弹性成像技术在临床中的选择及应用。