超声联合钼靶检查对非哺乳期乳腺炎的诊断价值

目的:探讨超声联合钼靶检查对非哺乳期乳腺炎的诊断价值。方法:回顾性分析经病理证实的145例(154个病灶)非哺乳期乳腺炎在超声与钼靶检查的表现。结果:根据超声特征将154个病灶分为乳腺导管扩张、实性肿块、混合性包块等6种类型。钼靶检查表现为类圆形肿块,不对称性致密影,血管影增粗,乳腺脂肪层浑浊,局部皮肤增厚,钙化灶。结论:超声检查较好地反映非哺乳期乳腺炎症病理过程,钼靶检查对炎性肿块和恶性肿块具有鉴别诊断价值,二者联合检查对于明确病变性质具有重要意义。     

彩色多普勒高频超声在女性乳腺增生症中的诊断价值

目的:探讨彩色多普勒高频超声对乳腺增生症的诊断价值,以提高超声对乳腺增生症的诊断价值。方法:观察59例经手术病理或病理活检证实为乳腺增生症患者的乳腺声像图及血流情况。结果:乳腺增生症的声像特点分为以下四种类型:(1)单纯性增生:共28例;(2)囊性增生:共6例;(3)腺瘤样增生:共6例:(4)混合型:以上三型同时存在,共19例。结论:彩色多普勒高频超声诊断女性乳腺增生症具有重要的临床诊断价值。     

灰阶超声和彩色多普勒超声诊断早期乳腺癌的价值

目的探讨超声声像图特征对早期乳腺癌诊断价值.方法84例女性患者(89个肿瘤)术前分别进行灰阶超声、彩色多普勒超声检查,观察肿块的形态、边缘、内部回声及钙化、肿块后回声以及肿块内血流信号情况.结果84例患者手术病理证实乳腺导管癌的7例,早期浸润癌的32例,良性肿瘤45例.灰阶超声、彩色超声多普勒对导管内癌及早期浸润性癌诊断的准确率分别为80.7%,79.3%和59.9%,65.9%.结论超声诊断早期乳腺癌目前主要依靠灰阶超声.     

乳腺X线摄影诊断乳腺癌的敏感性和特异性分析

目的:评价乳腺X线摄影诊断乳腺癌效用,分析其敏感性和特异性,总结诊断经验。方法:回顾性分析既往经病理诊断高度疑似乳腺癌患者90例,调取X线、B超声、病理诊断资料,交由两位放射科医师进行诊断,评价X线诊断效用,分析其征象特征。结果:90例患者诊断为乳腺癌71例(78.89%),浸润型导管癌50例,浸润型小叶癌8例,导管原位癌3例,其它10例,良性病变中乳腺病伴导管上皮不典型增生11例,脂肪坏死8例;X线诊断符合率、敏感度、特异度分别为70.00%、敏感度70.42%、特异度68.42%与B超声65.56%、67.71%、57.89%,差异无统计学意义(P0.05);X线+B超声联合诊断符合率96.67%、敏感度98.59%、特异度89.47%,高于单独应用乳腺X线、B超声,差异具有统计学意义(P0.05);71例乳腺癌,乳腺X线表现微钙化伴肿块60.56%,单纯微钙化22.54%,微钙化伴结构紊乱16.90%,不同类型肿瘤乳腺X线表现分布差异无统计学意义(P0.05);X线观察病灶形态包括分叶、圆形或类椭圆形、不规则行,边缘见毛刺征、清晰、模糊,不规则形态、毛刺征边缘恶性率达100.00%,有钙化恶性率94.20%。结论:乳腺X线摄影诊断乳腺癌敏感性、特异性尚有待提高,联合B超声诊断有助于提高诊断效用;钙化、不规则形态、毛刺征边缘是重要恶性征象;乳腺癌钙化特点较复杂,可能与乳腺癌类型无关;通过详细会诊,协商一致诊断,有助于提高诊断效用。     

常规超声基础上结合超声弹性成像在乳腺癌诊断中的应用方法探讨

目的:探析乳腺癌诊断中运用常规超声联合超声弹性成像的临床价值。方法:以2019年1月至2020年5月于我院接受诊断和治疗的100例乳腺肿块患者为主要对象,所有患者均接受常规超声检查、常规超声+超声弹性成像检查,且均经手术病理确诊,比较乳腺良性肿瘤患者与乳腺恶性肿瘤患者的常规超声检查结果,并对比三种不同检查方法(单用常规超声、单用超声弹性成像、常规超声+超声弹性成像)的诊断特异度、敏感度和准确性。结果:乳腺良性肿瘤患者在病灶形态、内部回声、边界、微小钙化方面与乳腺恶性肿瘤患者相比均存在显著差异(P0.05);常规超声+超声弹性成像的诊断特异度、敏感度、准确率均显著高于单用常规超声和单用超声弹性成像,存在显著差异。结论:在乳腺癌的临床诊断上,采用常规超声+超声弹性成像进行诊断有较高的准确性,可用于病灶性质的鉴别,为早期诊断和分期提供影像学依据,指导治疗方案的制定,可推广。     

近红外影像诊断的物理基础和乳腺癌的早期诊断

目的:研究红外影像针对乳腺癌早期诊断的临床价值以及影像表现。方法:选取本院自2011年1月-2014年1月间收治的200例乳腺癌诊断患者,术后病理确诊为早期乳腺癌患者89例,非早期乳腺癌14例,良性乳腺肿块97例,比较分析三种诊断结果的红外影像诊断结果,并结合钼靶乳腺X射线检查。结果:乳腺癌患者病灶超声图像具有形态不规则、边缘不整齐、后方声减弱、纵横比1,内部见微小钙化以及II级以上的彩色血流阻力指数0.7。早期乳腺癌乳腺红外影像诊断符合率为76.40%(68/89),临床乳腺外科诊断符合率为13.48%(12/89),X靶射线诊断符合率为75.28%(67/89),三种联合诊断,符合率为92.13%(82/89),联合诊断符合率明显高于单独诊断(P0.05),具有统计学意义。结论:针对乳腺癌患者早期诊断,可以通过乳腺外科诊断、红外影像诊断以及X靶射线诊断联合进行诊断,从而有效的提升乳腺癌患者早期诊断效果。     

常规超声及超声弹性成像对乳腺肿块的定性诊断价值

目的:探析乳腺肿物进行超声弹性成像和常规超声检查的定性诊断价值。方法:将2013年4月至2014年3月我院收治经病理检查及手术证实的乳腺纤维腺瘤病人20例(病灶23个)、乳腺癌23例(病灶23个),予以常规超声和超声弹性成像法检查,半定量分析全部病灶的硬度;在灰阶超声下及弹性模式下测量病灶面积,比较两组的病灶面积。结果:乳腺癌组的病灶硬度情况显著高于纤维腺瘤组,差异有统计学意义(P0.05);乳腺癌组的病灶面积比值显著高于纤维腺瘤组,差异有统计学意义(P0.05)。结论:乳腺肿块进行超声弹性成像检查有助于评估肿物的良恶性,具有重要的临床诊断价值。     

钨铑靶X线DR摄影对乳腺肿块类疾病的诊断价值分析

目的:研究钨铑靶X线DR摄影对肿块类乳腺疾病的诊断及鉴别诊断价值。方法:回顾性分析2013年9月至2015年6月经病理证实为非增生性良恶性的肿块1000例患者中220例住院患者的DR和病理结果 ,从而得出钨铑靶DR摄影对乳腺肿块类疾病的诊断价值。结果:220例患者中恶性肿块80例,术前DR诊断符合71例,准确率约为88.7%;良性肿块140例,术前钼靶诊断符合123例,准确率约为87.8%。结论:数字化乳腺钨铑靶摄影在乳腺良,恶性肿块的鉴别诊断中具有明显优势,尤其对早期的乳腺癌更有其独特的优势。     

超声引导下乳腺肿块的穿刺活检

目的:探讨超声引导下乳腺肿块穿刺活检鉴别良、恶性肿瘤的临床价值。方法:对476例经常规超声检查明确有乳腺肿块的患者,应用巴德自动活检枪、16G组织切割针,在实时超声引导监视下,选择每个病灶的合适部位取材2-4次送病理检查。结果:476例共492个病灶,取材均较满意。除2例患者有术后血肿,其他患者术后均无明显并发症,病理诊断为乳腺Ca347例,约占全部病例的73%。结论:超声引导下乳腺肿块穿刺活检,对诊断乳腺良、恶性病变具有重要价值,就该在临床中大力推广应用。     

超声与乳腺摄影在乳腺导管原位癌诊断中的应用价值

目的:探讨超声与乳腺摄影在乳腺导管原位癌(DCIS)诊断中的应用价值。方法:回顾性分析62例乳腺导管原位癌的超声和乳腺摄影表现,记录肿瘤大小、形态、内部回声,对肿瘤内微钙化进行比较分析。结果:62例DCIS中,超声和乳腺摄影检出微钙化分别为34例(54.8%)和46例(74.2%),乳腺摄影显示微钙化高于超声检查(P=0.024)。超声检查显示阳性62例,乳腺摄影检查阳性58例,超声显示DCIS阳性率明显高于乳腺摄影(P=0.042)。结论:微钙化是诊断DCIS的重要征象,乳腺摄影是检出微钙化的最佳选择,超声能够弥补乳腺摄影不能够发现假阴性DCIS。     

乳腺癌彩色多普勒超声的诊断价值

目的:分析彩色多普勒超声诊断乳腺癌的价值。方法:选择2012年2月至2020年2月期间乳腺癌患者40例作为研究对象,采用随机数字表法将其均分为两组,各20例,分别为对照组和观察组。其中对照组采用常规乳腺癌检测方法,观察组则采用彩色多普勒超声检测方法。观察两组患者检测乳腺癌的准确率。结果:经过检测,观察组检测乳腺癌的准确率明显优于对照组,两组数据差异具有统计学意义(P<0.05)。结论:对乳腺癌患者使用彩色多普勒超声检查可提高临床诊断正确率,建议在临床上推广。     

特发性女童性早熟通过乳腺高频超声诊断的临床价值及准确率分析

目的:分析特发性女童性早熟通过乳腺高频超声诊断的临床价值及准确率。方法:本研究共62例研究对象,均为经激素检查与第二特征检查确诊为特发性性早熟女童,将62例女童列为研究组,另选择同时间段到我院接受健康体检的62例女童列为对照组,所有研究对象均选自2018年1月至2019年12月。两组均接受乳腺高频超声检查,比较两组检查情况。结果:在周围腺体回声与乳腺中央低回声区的长径和厚度上,对照组明显优于研究组(P<0.05)。在子宫纵径、子宫横径、子宫前后径与子宫体积上,研究组明显优于对照组(P<0.05)。在诊断准确率上,超声诊断低于病理学检查(P<0.05)。结论:乳腺高频超声用于特发性性早熟女童中的诊断价值显著,检查准确率较高,临床可进一步推广运用。     

术前乳腺B超及MRI检查对乳腺癌患者腋窝淋巴结状态评估的准确性评估

目的:对术前乳腺B超及MRI检查对乳腺癌患者腋窝淋巴结状态评估中的临床价值进行评价分析,为今后的临床诊治工作提供可靠的参考依据。方法:抽取在2010年8月-2013年8月间我院收治的乳腺癌临床患者136例,对其在根治术前行乳腺B超及MRI检查,并以术后病理结果为依据,对乳腺B超及MRI检查在腋窝淋巴结状态评估中的准确性进行评估,并展开对比分析。结果:经比较发现,乳腺B超与MRI检查对腋窝淋巴结状态评估准确性无明显差异(P0.05),二者联合后准确性较单独使用时发生显著升高(P0.05)。结论:在乳腺癌腋窝淋巴结状态评估中乳腺B超、MRI检查的临床准确性无明显差异,联合使用后可有效提高检测准确性,值得关注。     

彩超对乳腺肿瘤的诊断及其临床价值分析

目的:分析彩超对乳腺肿瘤的诊断及其的临床价值。方法:给予120例乳腺检查患者二维超声的常规检查,并利用血流图仔细观察肿瘤的血流情况,术后分析且比较其的检验结果。结果:120例病患经病理检查后证实,100例和彩超检查的结果相符合,确诊率为83.33%。超声误诊5例,误诊率为4.2%。结论:进行彩超检查,有助于早期发现、诊治及预防乳腺肿瘤疾病,具有较大的临床意义,值得在临床医学中广泛推广。     

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.     

超声弹性成像与常规超声诊断100例乳腺良恶性的应用价值

目的:探究超声弹性成像及常规超声在诊断乳腺肿瘤定性中的效果及应用价值,为后期临床诊断提供参考。方法:选取我院2012年4月-2014年4月期间收治的102例乳腺肿瘤患者的临床资料进行回顾性分析。患者行常规超声及超声弹性成像,并经病理学检验确诊。结果:检验结果显示,超声弹性成像诊断阳性肿瘤79例,阴性21例,误诊5例;常规超声成像诊断阳性75例,阴性25例,误诊13例。两种检验方式在准确率与误诊率比较,差异具有统计学意义(P0.05)。结论:乳腺肿瘤采用超声弹性成像诊断的效果显著,尤其在常规超声诊断的基础上使用,效果更佳。     

Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.     

具有分割健壮性的超声图像分类方法探索

在早期癌症的发病表现中,肿块和微小钙化点是重要的特征和诊断依据.然而,病变可疑区域与正常组织区域之间的差别很小,并且肿块的边界和内部结构在超声图像上形态不一,与周围组织的差别也很细微,很难用人眼检查到,这样使得早期确诊变得比较困难.本文采用一种基于模糊数学理论的乳腺超声图像增强算法,经过增强处理之后,原先由于图像的模糊而隐藏的信息能够清晰的观察到,这也后后面的肿瘤的分割与分类打好基础.     

Digital Image Processing Technique for Breast Cancer Detection

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women’s quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. As masses and benign glandular tissue typically appear with low contrast and often very blurred, several computer-aided diagnosis schemes have been developed to support radiologists and internists in their diagnosis. In this article, an approach is proposed to effectively analyze digital mammograms based on texture segmentation for the detection of early stage tumors. The proposed algorithm was tested over several images taken from the digital database for screening mammography for cancer research and diagnosis, and it was found to be absolutely suitable to distinguish masses and microcalcifications from the background tissue using morphological operators and then extract them through machine learning techniques and a clustering algorithm for intensity-based segmentation.     

An Improved Fully Automated Breast Cancer Detection and Classification System

More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier on different datasets. This method specifically uses ResNet50 and AlexNet, convolutional neural networks (CNNs), for deep learning and a K-Nearest-Neighbor (KNN) algorithm to classify data. Various experiments have been conducted on five datasets: the Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD), King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD), Breast Histopathology Images (BHI), and Breast Cancer Histopathological Image Classification (BreakHis). These datasets were used to train, validate, and test the presented method. The obtained results achieved an average of 99.38% accuracy, surpassing other models. Essential performance quantities, including precision, recall, specificity, and F-score, reached 99.71%, 99.46%, 98.08%, and 99.67%, respectively. These outcomes indicate that the presented method offers essential aid to pathologists diagnosing breast cancer. This study suggests using the implemented algorithm to support physicians in analyzing breast cancer correctly.