超声联合乳腺钼靶在乳腺癌筛查中的应用

目的 探究超声联合乳腺钼靶在乳腺癌筛查中的应用价值。方法 选取江西省赣州市人民医院体检科2018年6月至2020年11月检查的疑似乳腺癌患者82例,均行病理、超声及钼靶检查。以病理检查结果作为判断标准,分析超声、钼靶及联合检查在乳腺癌中的诊断价值和三者结果与病理结果的一致性。结果 82例疑似乳腺癌患者中经病理检查明确乳腺恶性病变66例,乳腺良性病变16例;超声检查结果乳腺恶性病变54例,乳腺良性病变28例;钼靶检查结果乳腺恶性病变57例,乳腺良性病变25例;联合检查结果乳腺恶性病变64例,乳腺良性病变18例;超声联合乳腺钼靶联合检查在乳腺癌诊断中的灵敏度、特异度、准确度、阳性预测值及阴性预测值均高于单独超声检查或钼靶检查(P<0.05)。单独超声检查或钼靶检查与病理检查的一致性均不佳(Kappa=0.214和0.392),超声联合乳腺钼靶联合检查与病理检查的一致性良好(Kappa=0.852,P<0.001)。结论 超声联合乳腺钼靶在乳腺癌诊断中具有较高的临床应用价值,与病理结果一致性较强,可进一步提升诊断准确度。     

乳腺超声影像报告数据系统在乳腺病灶诊治中的应用价值分析

目的:研究乳腺超声影像报告数据系统在乳腺病灶诊治中的应用价值。方法:本次研究选取的研究对象为2014年2月1日~2017年2月3日期间在我院进行检查的乳腺病变患者,200例患者均采用乳腺超声影像报告数据系统进行检查,之后进行病理检查,将其结果作为金标准。结果:病理检查结果显示200例患者224个乳腺病灶中良性病变和恶性病变分别为140个和84个,而BI-RADS检出良性病变108个,恶性病变116个,且其敏感性较高为90.74%,特异性和准确性依次为63.79%、76.79%,阳性预测值、阴性预测值分别为70.00%、88.10%。阳性预测值随着BI-RADS分级的增高而增高,且各级别之间互相进行比较差异较大(P0.05)。结论:乳腺超声影像报告数据系统应用在乳腺病灶诊治中不仅可以较好地鉴别诊断良恶性病灶,还可根据分级情况选择适合的治疗方法。     

乳腺超声BI-RADS系统在乳腺肿块评价中的应用研究

目的:探讨乳腺超声BI-RADS系统在乳腺肿块评价中的应用效果。方法:本次研究选取2017年3月至2018年4月我方医院就诊及病理证实的乳腺病变患者136例。共切除186个病灶。136例患者采用彩色超声多普勒检查仪器，按照乳腺超声BI-RADS系统描写病灶特点，记录完整的乳腺超声报告。结果:乳腺影像学报告及数据系统评级诊断结果与病理检查对比发现，炎症、导管内乳头状瘤、乳腺癌等准确度两者无明显差异，对比无统计学意义(P>0.05)。切除186个病灶的乳腺影像学报告及数据系统评级诊断未检出评级标准中最低的0级。结论:乳腺影像学报告及数据系统评级对鉴别乳腺肿块良恶性有重要意义。值得广泛推广和临床应用。     

超声光散射乳腺成像技术在乳腺疾病诊断中的应用

目的 评价超声光散射成像技术(diffuse optical imaging(DOI)with ultrasound localization(UL)在乳腺疾病诊断中的应用价值.方法 采用新奥博为有限公司研制的Optimus Ⅱ型超声光散射乳腺成像系统和百胜Esaote Megas GPX彩色多普勒超声诊断仪,以双盲方式随机选择了49例次进行乳腺检查.这些受检者同时接受了临床检查、乳腺钼靶X-线检查.另有2例新辅助化疗的患者也接受了超声光散射检查.根据北美放射协会制定的BI-RADS分级标准对乳腺病变进行良恶性分级.结果 在49例受检者中,31例被检出患有乳腺疾病,其中5例评为4级以上者经超声引导下穿刺或手术而获得病理结果,包括乳腺癌4例,乳腺腺病1例.4例乳腺癌中有3例光散射成像判定在4级,与病理诊断相符.19例患有良性病变者光散射成像均评为2、3级.结论 超声光散射成像技术在乳腺疾病检查中集形态结构检查与功能检查于一体,可对形态结构信息和功能信息进行综合判断,有助于超声检查对乳腺良恶性病变给予更准确的判断及对乳腺癌患者新辅助化疗疗效的观察.     

超声非肿块型乳腺疾病83例临床分析

目的 分析超声非肿块型乳腺疾病的超声及组织病理特征,探讨超声非肿块型乳腺疾病恶性病变的影响因素.方法 83例超声非肿块型乳腺疾病患者,均行超声检查和超声下穿刺活检组织病理检查,分析超声声像图与临床病理特征的关系.根据组织病理结果分为良性病变组52例和恶性病变组31例,比较2组临床病理特征.采用多因素logistic回归...     

钼靶联合超声诊断乳腺良恶性病变的应用价值

目的:评价在乳腺良恶性病变诊断时钼靶联合超声的应用价值.方法:将2020年1月—2021年5月来惠州市惠阳区三和医院检查的乳腺良恶性病变患者58例作为探究对象,回顾患者的相关资料,在患者病情确诊前,均进行钼靶、超声检查,以病理结果作为金标准,评价钼靶联合超声的诊断符合率、特异度及灵敏度.结果:58例患者经病理检查,确诊...     

彩色多普勒超声诊断在乳腺结节的确诊中的应用价值体会

目的:探讨彩色多普勒超声诊断在乳腺结节的确诊中的应用价值。方法:研究选取于2016年6月—2019年6月期间在本院行超声检查发现乳腺结节,在外院行手术治疗并行病理诊断的患者200例作为资料,与手术病理结果进行比较,均行彩色多普勒超声诊断仪诊断,判断诊断准确率。结果:彩色多普勒超声诊断仪诊断良性176例,恶性24例,以手术病理结果为标准,诊断恶性结节敏感度为85.18%、特异度为97.73%,准确率为97.50%;良性结节患者PSV及RI水平均低于对照组,比较差异有统计学意义,P<0.05。结论:在乳腺结节患者诊断中采用彩色多普勒超声诊断仪检查可尽早发现结节病变,综合结节大小、超声表现及血流信号等,明确病变性质,为治疗提供可靠依据,值得推广。     

彩色多普勒超声在乳腺结节病变中的诊断价值分析

目的：探讨临床鉴别诊断乳腺结节病变采用彩色多普勒超声技术的效果。方法：选取2020年1月—2022年12月泗洪中信医院门诊接诊的乳腺结节病变患者478例作为研究对象,患者均进行彩色多普勒超声检查,以病理结果为金标准,分析彩色多普勒超声检查乳腺结节病变患者疾病类型,同时分析该方法的诊断效能;另对比乳腺良性结节患者与恶性结节病变患者阻力指数、血流收缩期峰值流速两项血流动力学指标差异,归纳总结良恶性乳腺结节病变患者血流分级情况。结果：病理结果显示,478例乳腺结节病变患者中,305例患者为良性结节病变,包括乳腺纤维瘤、乳腺囊性增生、浆细胞性乳腺炎、乳头状瘤;173例患者诊断为恶性结节病变,包括原位癌及浸润性癌。彩色多普勒超声诊断准确率为98.74%、灵敏度为99.02%、特异度为98.27%。根据检查结果显示,乳腺良性结节病变患者阻力指数及血流收缩期峰值流速均显著低于恶性结节病变患者（P <0.01）;同时良性结节病变患者血流分级Ⅰ级与Ⅱ级占比显著高于恶性结节病变患者,Ⅲ级与Ⅳ级占比显著低于恶性结节病变患者（P <0.01）。结论：临床鉴别诊断乳腺结节病变可通过彩色多普勒超声技术...     

高频超声诊断男性乳腺疾病

目的探讨男性乳腺常见疾病的临床和超声图像表现。方法收集在我院行乳腺超声检查并经临床证实的男性乳腺疾病病例,回顾性分析其临床及超声图像表现。结果男性乳腺疾病经手术及病理证实者23例,超声诊断符合率为87%、误诊率为13%。结论超声检测可较直观地显示男性乳腺及其周围的结构,获得病变的部位、大小、形态、内部回声及血流等信息。     

三维超声成像在乳腺疾病中临床应用研究

目的：评价三维超声成像在乳腺疾病中的临床应用价值。方法：对72例86个乳房病灶进行三维重建并与二维图像及手术病理结果作了对照。结果：表明三维超声可弥补二维超声的不足，三维超声所建图像逼真清晰、直观、立体感强、空间关系明确，并可确定病变的形态、范围和其表面及内部细微的特征。结论：三维超声能提供更准确的病变部位，尤其是乳腺导管扩张腔内的微小颗粒状病变的信息，并为临床医师制定手术方案提供了定位诊断，三维超声临床应用前途广泛。     

DGANet: A Dual Global Attention Neural Network for Breast Lesion Detection in Ultrasound Images

Deep learning-based breast lesion detection in ultrasound images has demonstrated great potential to provide objective suggestions for radiologists and improve their accuracy in diagnosing breast diseases. However, the lack of an effective feature enhancement approach limits the performance of deep learning models. Therefore, in this study, we propose a novel dual global attention neural network (DGANet) to improve the accuracy of breast lesion detection in ultrasound images. Specifically, we designed a bilateral spatial attention module and a global channel attention module to enhance features in spatial and channel dimensions, respectively. The bilateral spatial attention module enhances features by capturing supporting information in regions neighboring breast lesions and reducing integration of noise signal. The global channel attention module enhances features of important channels by weighted calculation, where the weights are decided by the learned interdependencies among all channels. To verify the performance of the DGANet, we conduct breast lesion detection experiments on our collected data set of 7040 ultrasound images and a public data set of breast ultrasound images. YOLOv3, RetinaNet, Faster R-CNN, YOLOv5, and YOLOX are used as comparison models. The results indicate that DGANet outperforms the comparison methods by 0.2%–5.9% in total mean average precision.     

Global guidance network for breast lesion segmentation in ultrasound images

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limited capabilities in capturing long-range dependencies of the input ultrasound image, resulting in degraded breast lesion segmentation accuracy. In this paper, we develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection (BD) modules for boosting the breast ultrasound lesion segmentation. The GGB utilizes the multi-layer integrated feature map as a guidance information to learn the long-range non-local dependencies from both spatial and channel domains. The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement. Experimental results on a public dataset and a collected dataset show that our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation. Moreover, we also show the application of our network on the ultrasound prostate segmentation, in which our method better identifies prostate regions than state-of-the-art networks.     

Completely automated segmentation approach for breast ultrasound images using multiple-domain features

Lesion segmentation is a challenging task for computer aided diagnosis systems. In this article, we propose a novel and fully automated segmentation approach for breast ultrasound (BUS) images. The major contributions of this work are: an efficient region-of-interest (ROI) generation method is developed and new features to characterize lesion boundaries are proposed. After a ROI is located automatically, two newly proposed lesion features (phase in max-energy orientation and radial distance), combined with a traditional intensity-and-texture feature, are utilized to detect the lesion by a trained artificial neural network. The proposed features are tested on a database of 120 images and the experimental results prove their strong distinguishing ability. Compared with other breast ultrasound segmentation methods, the proposed method improves the TP rate from 84.9% to 92.8%, similarity rate from 79.0% to 83.1% and reduces the FP rate from 14.1% to 12.0%, using the same database. In addition, sensitivity analysis demonstrates the robustness of the proposed method.     

Texture Analysis Based on Auto-Mutual Information for Classifying Breast Lesions with Ultrasound

Described here is a novel texture extraction method based on auto-mutual information (AMI) for classifying breast lesions. The objective is to extract discriminating information found in the non-linear relationship of textures in breast ultrasound (BUS) images. The AMI method performs three basic tasks: (i) it transforms the input image using the ranklet transform to handle intensity variations of BUS images acquired with distinct ultrasound scanners; (ii) it extracts the AMI-based texture features in the horizontal and vertical directions from each ranklet image; and (iii) it classifies the breast lesions into benign and malignant classes, in which a support-vector machine is used as the underlying classifier. The image data set is composed of 2050 BUS images consisting of 1347 benign and 703 malignant tumors. Additionally, nine commonly used texture extraction methods proposed in the literature for BUS analysis are compared with the AMI method. The bootstrap method, which considers 1000 bootstrap samples, is used to evaluate classification performance. The experimental results indicate that the proposed approach outperforms its counterparts in terms of area under the receiver operating characteristic curve, sensitivity, specificity and Matthews correlation coefficient, with values of 0.82, 0.80, 0.85 and 0.63, respectively. These results suggest that the AMI method is suitable for breast lesion classification systems.     

Current status of 3D/4D volume ultrasound of the breast

3D/4D volume ultrasound is an established method that offers various options for analyzing and presenting ultrasound volume data. The following imaging techniques are based on automatically acquired ultrasound volumes. The multiplanar view is the typical mode of 3D ultrasound data presentation. The niche mode view is a cut open view of the volume data set. The surface mode is a rendering technique that represents the data within a volume of interest (VOI) with different slice thicknesses (typically 1-4 mm) with a contrast-enhanced surface algorithm. Related to the diagnostic target, the transparency mode helps to present echopoor or echorich structures and their spatial relationships within the ultrasound volume. Glass body rendering is a special type of transparency mode that makes the grayscale data transparent and shows the color flow data in a surface render mode. The inversion mode offers a three-dimensional surface presentation of echopoor lesions. Volume Contrast Imaging (VCI) works with static 3D volume data and is able to be used with 4D for dynamic scanning. Volume calculation of a lesion and virtual computer-assisted organ analysis of the same lesion is performed with VoCal software. Tomographic Ultrasound Imaging (TUI) is the perfect tool to document static 3D ultrasound volumes. 3D/4D volume ultrasound of the breast provides diagnostic information of the coronal plane. In this plane benign lesions show the compression pattern sign, while malignant lesions show the retraction pattern or star pattern sign. The indeterminate pattern of a lesion combines signs of compression and retraction or star pattern in the coronal plane. Glass body rendering in combination with Power-Doppler, Color-Doppler or High-Definition Flow Imaging presents the intra- and peritumoral three-dimensional vascular architecture. 3D targeting shows correct or incorrect needle placement in all three planes after 2D or 4D needle guidance. In conclusion, it is safe to say that 3D/4D volume ultrasound of the breast is technically advanced and suitable for daily diagnostic and interventional breast work in addition to routinely used 2D sonography.     

Real-time elastography--an advanced method of ultrasound: First results in 108 patients with breast lesions.

OBJECTIVES: To evaluate whether real-time elastography, a new, non-invasive method for the diagnosis of breast cancer, improves the differentiation and characterization of benign and malignant breast lesions. METHODS: Real-time elastography was carried out in 108 potential breast tumor patients with cytologically or histologically confirmed focal breast lesions (59 benign, 49 malignant; median age, 53.9 years; range, 16-84 years). Tumor and healthy tissue were differentiated by measurement of elasticity based on the correlation between tissue properties and elasticity modulus. Evaluation was performed using the three-dimensional (3D) finite element method, in which the information is color-coded and superimposed on the B-mode ultrasound image. A second observer evaluated the elastography images, in order to improve the objectivity of the method. The results of B-mode scan and elastography were compared with those of histology and previous sonographic findings. Sensitivities and specificities were calculated, taking histology as the gold standard. RESULTS: B-mode ultrasound had a sensitivity of 91.8% and a specificity of 78%, compared with sensitivities of 77.6% and 79.6% and specificities of 91.5% and 84.7%, respectively, for the two observers evaluating elastography. Agreement between B-mode ultrasound and elastography was good, yielding a weighted kappa of 0.67. CONCLUSIONS: Our initial clinical results suggest that real-time elastography improves the specificity of breast lesion diagnosis and is a promising new approach for the diagnosis of breast cancer. Elastography provides additional information for differentiating malignant BI-RADS (breast imaging reporting and data system) category IV lesions.     

基于乳腺癌激素受体表达诊断与超声影像组学相关性临床研究

【】 目的：探讨乳腺癌患者超声影像组学定量特征分析与激素受体表达诊断相关性及其临床应用价值。方法：回顾性分析125例乳腺癌患者的术前超声特征与术后病理免疫组化结果。根据ER、PR、HER-2表达,将患者分为两组：激素受体阳性组(ER+、PR+、HER-2-),激素受体阴性组(ER-、PR-、HER-2-)。回顾性分析与评价乳腺癌肿块超声图像特征,通过基于相位信息动态轮廓模型筛选出与激素受体相关性强的特征性参数,应用支持向量机分类器及径向基核函数对研究数据进行研究分析。结果：超声定量组学代表性特征参数与激素受体表达相关联表明,除病灶形态外,两组间病灶大小、边缘、内部回声、后方回声、钙化等差异有统计学意义(P<0.05)。结论：超声影像组学定量特征分析可降低传统超声成像结论评价主观性,对乳腺癌激素受体表达特征及生物学行为预测方面具有一定的价值。     

超声引导穿刺在普通外科中的应用

目的　探讨高频超声在乳腺隐匿性病灶检出、超声引导穿刺定位切除活检和乳腺囊肿穿刺治疗的应用价值。同时介绍腹腔脓肿与积液的超声引导穿刺引流,超声引导PTCD。方法　1993年至今共检出乳腺隐匿性病灶250处,并在超声引导下作穿刺定位病灶切除术。对30处乳腺囊肿在超声引导下作穿刺治疗。5例腹腔脓肿、积液患者在超声引导下作穿刺引流。4例梗阻性黄疸患者在超声引导下行PTCD。结果　高频超声对乳腺隐匿性肿瘤诊断符合率为90%。同时检出9例隐匿性乳腺癌。5例腹腔脓肿、积液患者4例通过穿刺引流治愈。4例PTCD者3例引流成功。结论　超声为乳腺隐匿性病灶的检出、手术定位和囊肿穿刺治疗提供了有效手段。腹腔积液或脓肿超声下穿刺引流是一有效的非手术治疗,超声为PTCD提供了又一有效的引导方法。     

BI-RADS分级在乳腺单纯浸润性非特殊癌中的价值与应用

目的  分析BI-RADS标准化超声分级4C类病灶征象运用于乳腺浸润性癌非特殊型诊断时与病理征象的关联性及此类型癌的年龄分布,探讨BI-RADS分级标准应用于乳腺浸润性癌非特殊型诊断中的价值。方法  随机收集福建省立医院超声科于2020年5月~2023年5月所收治的88例超声BI-RADS 4C类的乳腺癌患者作为研究对象,针对患者年龄及病理检查结果进行分类,并对不同年龄段患不同类型乳腺疾病的患者的病灶超声特征与病理特征相关性进行讨论。结果  超声检查及病理追踪分析结果发现,在BI-RADS 4C类88例患者中,≥40岁的患者人数占90%, < 40岁的患者人数占10%;在乳腺浸润性非特殊癌中,≥40岁的患者人数占比95%, < 40岁的患者人数占比5%。单纯浸润性非特殊癌在病理检查结果中占总数的64.77%,其余非单纯浸润性非特殊癌占35.23%。结论  综合数据分析超声检查与BI-RADS分级结合在单纯浸润性非特殊癌中的检出率明显较高,有利于制定对应的治疗措施,更加突出BI-RADS分级在乳腺单纯浸润性非特殊癌中的检出率和运用。