Parotid gland tumors: Correlation between routine cytology and cytomorphometry by digital image analysis using conventional and newly introduced cytomorphometric parameters

The objective of this study was to compare qualitative cytomorphology and morphometric characteristics of parotid gland tumor cells, with the aid of a computer‐assisted system of image analysis. Routine qualitative cytologic and quantitative morphometric results from 64 parotid gland tumors were compared. Ultrasound (US)‐guided fine‐needle aspiration (FNA) specimens were taken from 54 patients. Eleven conventionally used morphometric parameters were studied: area, perimeter, convex area, convexity, maximal and minimal radius, length, breadth, form factor (FF), elongation factor, and nuclear‐ cytoplasmatic (N/C) ratio. Two newly introduced nuclear form factors were also measured: area symmetry factor and perimeter symmetry factor. The following nuclear morphometric parameters were significantly different between malignant and benign tumors: area, perimeter, convex area, convexity, maximal and minimal radius, length, breadth, FF, elongation factor, area symmetry factor, and perimeter symmetry factor. Comparing the cutoff values and receiver operating characteristic (ROC) curves the following nuclear morphometric parameters were found most useful in separating benign from malignant tumors: area, perimeter, convex area, maximal radius, length, and FF. The following whole cell morphometric parameters were significantly different between malignant and benign tumors: minimal and maximal radius, convexity, breadth, FF, and elongation factor. N/C ratio was significantly higher in malignant tumors. The quantitative morphometric analysis is a useful tool in the cytological differentiation between benign and malignant parotid gland tumors. Computerized image analysis may add to morphological evaluation by turning qualitative data into quantitative values. Diagn. Cytopathol. 2013;41:776–784. © 2013 Wiley Periodicals, Inc.     

Fractal Analysis of Contours of Breast Masses in Mammograms

Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. Breast masses present shape and gray-scale characteristics that vary between benign masses and malignant tumors in mammograms. Limited studies have been conducted on the application of fractal analysis specifically for classifying breast masses based on shape. The fractal dimension of the contour of a mass may be computed either directly from the 2-dimensional (2D) contour or from a 1-dimensional (1D) signature derived from the contour. We present a study of four methods to compute the fractal dimension of the contours of breast masses, including the ruler method and the box counting method applied to 1D and 2D representations of the contours. The methods were applied to a data set of 111 contours of breast masses. Receiver operating characteristics (ROC) analysis was performed to assess and compare the performance of fractal dimension and four previously developed shape factors in the classification of breast masses as benign or malignant. Fractal dimension was observed to complement the other shape factors, in particular fractional concavity, in the representation of the complexity of the contours. The combination of fractal dimension with fractional concavity yielded the highest area (A ( z )) under the ROC curve of 0.93; the two measures, on their own, resulted in A ( z ) values of 0.89 and 0.88, respectively.     

用于乳腺肿瘤分级诊断超声图像增强的算法

为提高乳腺肿瘤分级诊断的能力,提出一种基于超声信号用于乳腺肿瘤分级诊断的图像增强算法。通过分析良性和不同恶性程度肿瘤的超声图像的特征差异,提出了一种将灰度的动态变换方法和利用局部标准差及熵特征相结合的办法,对图像对比度进行增强处理,增强了乳腺超声图像的细节,提高了图像质量。该算法可对良性、恶性肿瘤等不同超声图像进行增强处理,使得图像之间差异更加明显,为临床医生分级诊断提供更加清晰的图像,具有一定的实际应用价值。     

压迫式弹性成像与声脉冲辐射力成像技术在乳腺肿瘤良恶性鉴别中的价值比较

目的:探讨分析压迫式弹性成像（CE）与声脉冲辐射力成像（ARFI）技术在乳腺肿瘤良恶性鉴别中的价值。方法:选择门诊或住院行超声检查发现的有乳腺肿块患者71例共89个病灶,经病理组织学确认良性病灶57个,恶性病灶32个。对各病灶进行彩色多普勒超声检查,并采取CE及ARFI技术,分别计算病灶弹性应变率比值（SR）及声触诊组织定量（VTQ）值,采用ROC曲线分析SR、VTQ对良恶性肿瘤的诊断效能。结果:恶性组病灶VTQ值与SR值均显著高于良性组（P<0.05）。采用ROC曲线分析VTQ、SR对乳腺良恶性肿瘤诊断效能,VTQ诊断曲线下面积（AUC）为0.918,95% CI为0.871~0.980（P<0.05）,最佳截断值为3.97,在此最佳截断值下,VTQ诊断敏感性94.64%、诊断特异性90.63%;SR诊断AUC为0.899,95% CI为0.854~0.956（P<0.05）,最佳截断值为4.12,在此最佳截断值下,SR诊断敏感性92.86%、诊断特异性84.38%。VTQ和SR诊断敏感性、特异性比较差异无统计学意义（P>0.05）。结论:两种超声诊断技术对乳腺良恶性肿瘤均具有较高的诊断价值,其诊断效能相似,临床上可联合使用,以实现优势互补,提高对乳腺癌的早期检出率。     

MRI对胰腺导管内乳头状粘液瘤良恶性的诊断价值

目的 探讨MRI对胰腺导管内乳头状粘液性肿瘤（IPMN）良恶性的鉴别诊断价值。方法 收集2012年1月~2018年6月我院经手术病理证实的IPMN患者24例,均行MRI检查（包括MRI平扫、三期增强以及MRCP）,分析IPMN MRI表现、IPMN良恶性因素,并采用ROC曲线分析IPMN肿瘤最大径及胰管扩张直径与肿瘤良恶性关系。结果 24例IPMN患者中,良性14例,恶性10例。良性与恶性在性别、肿瘤分型、病变位置间比较,差异无统计学意义（P＞0.05）;恶性年龄大于良性,差异有统计学意义（P＜0.05）。IPMN恶性肿瘤最大径为（55.70±10.73）mm,大于良性的（34.20±7.65）mm,差异有统计学意义（P＜0.05）;IPMN肿瘤最大径与肿瘤良恶性关系ROC曲线分析得出:曲线下面积（AUC）为0.87,肿瘤最大径最佳临界值为46.40 mm,敏感度为85.68%,特异性为83.35%。IPMN恶性主胰管扩张最大径为（8.91±3.22）mm,大于良性的（4.82±1.33）mm,差异有统计学意义（P＜0.05）;IPMN胰管扩张直径与肿瘤良恶性关系ROC曲线分析得出:AUC为0.88,胰管扩张最大径最佳临界值为7.35 mm,敏感度为70.00%,特异性为85.73%。结论 MRI能很好显示胰管扩张、囊性病变、管壁内结节等特征,在评估IPMN良恶性中具有敏感性。     

基于模糊数的乳腺肿瘤超声图像边缘快速提取方法

乳腺肿瘤边缘的准确提取在临床上对肿瘤良恶性的判别有重要的意义。本文利用三角模糊数的概念，采用重叠式窗口从图像中得到与不同隶属度对应的模糊数，从而建立以步进方格（marching square）为基本单元的模糊数平面；通过区间阈值得到步进方格上的映射区间，根据步进方格算法将对应映射区间着色绘制出肿瘤的边界。分别对恶性和良性肿瘤超声图像进行边缘提取。结果显示，本文方法相比一般提取边缘的算法具有快速准确提取乳腺肿瘤边缘的特点。实验证明本方法可以有效用于乳腺肿瘤超声图像边缘提取。     

计算机辅助系统在诊断乳腺良恶性肿瘤中的应用

目的:探讨计算机辅助诊断系统在良恶性肿瘤检测与特征提取基础上的分类对于乳腺肿瘤的诊断价值。方法:回顾性分析乳腺超声检查发现肿瘤且经过病理学证实的617例患者影像资料,采用手工提取的方式得到乳腺超声图像的感兴趣区域及病灶轮廓,再利用方向梯度直方图（HOG）、局部二值模式（LBP）和灰度共生矩阵（GLCM）3个特征进行乳腺肿瘤的良恶性病变真假阳性检测;最后用受试者操作特征曲线（ROC）分别分析每个特征对于两类病变判别的诊断性能和应用所有特征集合的分类诊断性能。结果:多特征融合方法的各项诊断效能及ROC曲线下面积（AUC）值均优于单特征LBP、HOG、GLCM（P值均<0.05）。与人工诊断相比,多特征融合的敏感性无显著差异,但特异度显著升高达98.57%（Z值=2.25, P<0.05）,同时AUC值为0.985,显著优于人工诊断的0.910（Z值=1.99, P<0.05）。结论:计算机辅助系统乳腺超声肿瘤良恶性检测的算法是有效的,能够对乳腺癌鉴别诊断提供有益的参考。     

Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images

This study aims to evaluate whether the distribution of vessels inside and adjacent to tumor region at three-dimensional (3-D) power Doppler ultrasonography (US) can be used for the differentiation of benign and malignant breast tumors. 3-D power Doppler US images of 113 solid breast masses (60 benign and 53 malignant) were used in this study. Blood vessels within and adjacent to tumor were estimated individually in 3-D power Doppler US images for differential diagnosis. Six features including volume of vessels, vascularity index, volume of tumor, vascularity index in tumor, vascularity index in normal tissue, and vascularity index in surrounding region of tumor within 2 cm were evaluated. Neural network was then used to classify tumors by using these vascular features. The receiver operating characteristic (ROC) curve analysis and Student’s t test were used to estimate the performance. All the six proposed vascular features are statistically significant (p < 0.001) for classifying the breast tumors as benign or malignant. The A_Z (area under ROC curve) values for the classification result were 0.9138. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis performance based on all six proposed features were 82.30 (93/113), 86.79 (46/53), 78.33 (47/60), 77.97 (46/59), and 87.04 % (47/54), respectively. The p value of A_Z values between the proposed method and conventional vascularity index method using z test was 0.04.     

常规MRI联合分段读出平面回波弥散加权成像在颞骨良恶性肿瘤鉴别诊断中的应用价值

目的 探讨常规MRI联合分段读出平面回波弥散加权成像(RESOLVE-DWI)鉴别颞骨良、恶性肿瘤的价值。方法 回顾性研究。纳入2014年1月-2019年12月复旦大学附属眼耳鼻喉科医院经病理学证实的96例颞骨肿瘤患者临床和影像资料。患者均在手术前2周内行颞骨常规MRI及RESOLVE-DWI检查,分别观察肿瘤大小(最大层面测量的病灶最大径)、形态、信号均匀性、边界及强化程度等图像特征,以及记录通过后处理软件在表观弥散加权成像图像上选择肿瘤实性区域手动绘制类圆形感兴趣区的表观弥散系数(ADC)。采用χ²检验和独立样本t检验比较良、恶性肿瘤的MRI信号特征及ADC值。绘制单独及联合常规MRI征像和RESOLVE-DWI测量的ADC值诊断颞骨良、恶性肿瘤的受试者工作特征曲线(ROC),并使用Delong检验比较诊断效能。结果 良性肿瘤59例,男28例、女31例,年龄(44.63±12.97)岁,肿瘤大小(2.39±0.89)cm,类圆形/浅分叶41例、不规则形18例,边界清晰52例、模糊7例,信号均匀37例、不均匀22例,轻中度强化34例、明显强化25例;恶性肿瘤37例,男18例、女19例,年龄(50.27±17.25)岁,肿瘤大小(2.58±1.16)cm,类圆形/浅分叶23例、不规则形14例,边界清晰17例、模糊20例,信号均匀27例、不均匀10例,轻中度强化9例、明显强化28例。良、恶性颞骨肿瘤间,除肿瘤边界和强化程度差异有统计学意义(P值均＜0.01)外,患者年龄、性别以及MRI图像其他特征间差异均无统计学意义(P值均>0.05)。颞骨恶性肿瘤的ADC值为(0.91 ± 0.23) ×10^-3 mm²/s,低于良性肿瘤组的 (1.09 ± 0.32) ×10^-3 mm²/s,差异有统计学意义(t=2.974, P＜0.01)。单独应用常规MRI和RESOLVE-DWI测量的ADC值诊断良、恶性颞骨肿瘤的ROC曲线下面积(AUC)分别为0.771(95%可信区间0.639~0.903)和0.727(95%可信区间0.565~0.889),其灵敏度、特异度及准确度分别为68.42%、85.71%、75.08%和63.16%、71.43%、66.35%。联合应用常规MRI征象及RESOLVE-DWI测量的ADC值诊断良、恶性颞骨肿瘤的AUC值为0.907(95%可信区间0.816~0.998),其灵敏度、特异度及准确度分别为89.47%、80.95%和86.19%。联合应用常规MRI及RESOLVE-DWI的AUC显著高于分别单独应用常规MRI和RESOLVE-DWI(Z=2.463、2.243, P值均＜0.05)。结论 常规MRI及RESOLVE-DWI在鉴别颞骨良、恶性肿瘤中具有重要价值,二者联合应用能有效提高其鉴别诊断效能。     

乳腺肿瘤超声图像特征参数量化研究进展

乳腺肿瘤超声图像的特征量化分析对判别肿瘤的良、恶性具有重要价值。本文总结了良性和恶性乳腺肿瘤在超声图像上的特点，将乳腺良性肿瘤和恶性肿瘤鉴别特征在形状、边缘、边界、朝向、回声特点几个方面的量化方法和量化参数进行了较为全面的梳理，并对量化特征与肿瘤良、恶性之间的关系进行了分析。     

Image analysis of irregularity of cluster shape in cytological diagnosis of breast tumors: cluster analysis with 2D-fractal dimension

To establish diagnostic criteria using comparison of cell cluster shapes, between benign and malignant tumors, breast tumors demonstrating weak cellular atypia in low grade invasive ductal carcinoma (IDC) were compared. Fine-needle aspiration (FNA) specimens of breast tumors were obtained from 37 patients. Among these, 16 were histologically diagnosed as IDC low-grade and the other 21 as benign fibroadenoma (FA). For evaluation, we examined 740 clusters from these 37 FNA specimens. Nine image morphometric parameters were studied, including the cluster area, circumference, maximal length, maximal breadth, ratio of length to breadth, cluster roundness, cluster size, and the edge and distribution image fractal dimensions for cluster analysis. We evaluated the irregularity in cell cluster shape using fractal dimension analysis, and determined the correlation to cluster size. The irregularity in the IDC cluster shape was higher than that in the FA cluster shape. However, six cases (28.5%) of 21 FA clusters showed high fractal dimensions similar to those for IDC. The clusters were classified by cluster analysis into three types: IDC clusters, FA with irregular cluster shape, and FA with no irregular clusters. The average cell cluster area of the FA with irregular shape was found to be about three times larger than that of IDC clusters. When the differential diagnosis between IDC and FA is difficult, it is important to focus on irregularities in the shape and on overall size of the cell clusters. For accurate diagnosis, the cell cluster shape is as important as the individual cellular atypia.     

超声造影定量分析对乳腺良恶性肿瘤区域血流动力学灌注特征的研究

目的研究乳腺良恶性肿瘤区域血流灌注的变化特点。方法乳腺良恶性肿瘤共60例,均为女性,年龄2270岁,平均年龄40岁。应用超声造影时间-强度曲线定量分析技术,检测其中乳腺恶性肿瘤30例（33个病灶）、乳腺纤维腺瘤30例（34个病灶）瘤灶边缘及中心部区域灌注参数峰值强度（PI）、曲线下面积（AUC）、达峰时间（TTP）、廓清时间（WOT）,用光学、透射电子显微镜观察瘤内新生血管显微结构及超微结构改变,免疫组织化学技术检测CD34、VEGF、Flk-1/KDR在两组肿瘤中的表达特性。结果恶性组病灶边缘各灌注参数与中心区域相比差异有统计学意义（P〈0.05）,良性组病灶边缘各灌注参数与中心区域相比差异无统计学意义（P〉0.05）。光学显微镜下恶性组90.91%（30/33）见坏死灶,血管异常丰富区位于癌巢边缘。良性组未见坏死灶,血管管径和分布均一。透射电子显微镜下恶性组新生血管内皮细胞超微结构具有不同于正常血管内皮细胞的分裂旺盛的瘤性特征,癌巢边缘可见扩张、迂曲的大血管,癌巢中心常见狭窄、闭塞的幼稚新生血管。恶性组瘤内微血管密度（MVD）显著高于良性组（P〈0.05）,尤其富集于癌巢边缘。VEGF及Flk-1/KDR在恶性组血管内皮细胞尤其是癌巢边缘呈强阳性表达,在良性组血管内皮细胞几乎不表达（P〈0.05）。结论实时超声造影区域灌注参数的差异为乳腺良恶性肿瘤的鉴别诊断提供了重要依据。针对VEGF或Flk-1/KDR为靶点进行新生血管特异性分子显像可能为乳腺癌的准确诊断提供新的思路,癌巢边缘可能是下一步进行乳腺癌分子影像观察的重点靶区。     

超声造影增强模式在乳腺良恶性肿块鉴别诊断中的价值

目的探讨超声造影增强模式对乳腺良恶性肿块鉴别诊断的价值。方法选择40例经病理组织检查证实的乳腺肿瘤患者,均为女性,年龄25～54岁,平均年龄35岁。其中良性肿块25例,恶性肿块15例。使用Siemens Sequoia 512彩色多普勒超声诊断仪,造影剂采用SonoVue,行超声造影增强方式检查。结果乳腺良恶性肿块超声增强方式不同,良性肿块以整体不同程度地均匀型增强改变为主(80%,20/25),恶性肿块主要表现为不均匀增强或周边增强(86.7%,13/15)。结论乳腺良恶性肿块超声造影增强模式不同,可为鉴别诊断提供帮助。     

Minichromosome maintenance protein 2 is a reliable proliferative marker in breast carcinoma

This is a study aimed to examine the distribution pattern of a specific minichromosome maintenance protein 2 (MCM2) in benign and malignant breast tissue. We also aim to correlate the frequency of expression of MCM2 with the degree of tumor differentiation. We used immunohistochemistry to examine the distribution and expression pattern of MCM2 on formalin-fixed paraffin-embedded tissue sections of benign (n = 30) and malignant breast tissue (n = 70) (IDC 56, DCIS 4, ILC 2, nonductal 4, mixed type 4). We quantified MCM2 expression by calculating a labeling index, which represents the percentage of epithelial nuclei that stained positively. Immunoreactivity was heterogenous in all the 70 malignant cases examined. Epithelial cells in cycle are most frequent at the tumor periphery. Labeling index of MCM2 was greatest in grade 3 (poorly differentiated) and lowest in grade 1 tumors (well differentiated). Minichromosome maintenance protein 2 expression in breast cancer showed a positive association with histologic grade (P < .05). In all the benign breast tissue examined, no proliferating compartments could be characterized. Minichromosome maintenance protein 2 is a useful proliferative marker of breast carcinoma. The frequency of expression of MCM2 showed an inverse correlation with the degree of tumor differentiation.     

Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts

Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsy-proved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multi-feature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78 ± 0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.     

高频超声、钼靶X射线在乳腺BI-RADSⅣ～Ⅴ级诊断中的应用对比研究

目的 探讨高频超声、钼靶X射线单独与联合应用在乳腺影像报告与数据系统(BI - RADS)Ⅳ～Ⅴ级诊断中的应用价值及对比研究.方法 136个病灶经病理证实为乳腺恶性肿瘤,回顾性分析高频超声、钼靶X射线影像表现,并对诊断的准确性进行统计学分析.结果 高频超声、钼靶X射线及两者联合应用对乳腺BI- RADSⅣ-Ⅴ级诊断的正确率、误诊率比较,差异有统计学意义,P＜0.01.结论 高频超声在乳腺肿块(BI - RADS Ⅳ～Ⅴ级)诊断正确率方面优于钼靶X射线,特别是肿瘤直径＜1.0cm,且不伴有钙化时,两者联合应用较单一方法更能提高乳腺肿块(BI - RADS Ⅳ～Ⅴ级)的检出率及良、恶性鉴别诊断.     

Ultrastructural study of benign, low-malignant potential (LMP), and malignant ovarian tumors

Ultrastructural characteristics of benign, low-malignant potential (LMP), and malignant ovarian tumors were investigated, considering the aspects of histologic subtypes and histologic grading. In addition, the histogenesis of ovarian cancer was histologically investigated in an attempt to elucidate whether malignant tumor was generated from benign or LMP tumor, or whether it was generated de novo from normal tissues. Although all the benign, LMP, and malignant tumors appeared to be derived from Mullerian duct in serous tumors, the origin of endometrioid or mucinous tumor could not be ultrastructurally clarified. However, there was ultrastructural similarity between benign and malignant tumors among serous, endometrioid, and mucinous tumors, and it was suggested that benign adenoma may be the developmental origin of malignant tumors regardless of the histologic subtype. In addition, the investigation of endometrioid tumors revealed that the differences of histologic grading in malignant tumors reflected the ultrastructural differences, and that G1 tumor had an ultrastructure that was more similar to that of benign and LMP tumors than to that of G2 tumor.     

Diagnosis of ovarian tumor by measuring tumor markers]

Values of CA125, CA19-9, TPA, CA72-4, BFP and LDH in sera were detected in 148 malignant ovarian tumors, 41 borderline malignant ovarian tumors, 71 benign ovarian tumors and 64 benign uterine diseases. A new cut-off value was determined by ROC graph for distinguishing malignant and borderline ovarian tumors from benign ovarian tumors. CA125 (cut off: 30 U/ml) was a highly sensitive marker for malignant and borderline malignant ovarian tumors, the value being 88.1% (52/59) and 81.8% (9/11), respectively. On the other hand, in 37 benign ovarian tumors, the positive rate was 21.6% and in 21 benign uterine diseases it was 52.4%. CA19-9 (cut off: 150 U/ml) was inferior to CA125, but it was an effective marker for mucinous ovarian tumors. TPA (cut off: 40 U/ml) was also a sensitive (84.7%, 50/59) marker of malignant ovarian tumors. CA72-4 (cut off: 4 U/ml) was a highly specific (87.0%, 60/69) marker of malignant ovarian tumors. Combination assays of CA125/CA19-9, CA125/TPA and CA125/CA72-4 were not effective. Usefulness of BFP for early malignant ovarian tumors was suggested. Seven cases of dysgerminoma showed extremely elevated LDH levels (1,248 +/- 886 IU/1/37 degrees C). Malignancy and histological type of ovarian tumors could be decided by combination assay of these tumor markers, before surgical operation.     

Feature Extraction from a Signature Based on the Turning Angle Function for the Classification of Breast Tumors

Malignant breast tumors and benign masses appear in mammograms with different shape characteristics: the former usually have rough, spiculated, or microlobulated contours, whereas the latter commonly have smooth, round, oval, or macrolobulated contours. Features that characterize shape roughness and complexity can assist in distinguishing between malignant tumors and benign masses. Signatures of contours may be used to analyze their shapes. We propose to use a signature based on the turning angle function of contours of breast masses to derive features that capture the characteristics of shape roughness as described above. We propose methods to derive an index of the presence of convex regions (XR ( TA )), an index of the presence of concave regions (VR ( TA )), an index of convexity (CX ( TA )), and two measures of fractal dimension (FD ( TA ) and FDd ( TA )) from the turning angle function. The methods were tested with a set of 111 contours of 65 benign masses and 46 malignant tumors with different parameters. The best classification accuracies in discriminating between benign masses and malignant tumors, obtained for XR ( TA ), VR ( TA ), CX ( TA ), FD ( TA ), and FDd ( TA ) in terms of the area under the receiver operating characteristics curve, were 0.92, 0.92, 0.93, 0.93, and, 0.92, respectively.     

超微血管成像与乳腺肿瘤微血管密度的相关性研究

目的：探讨超微血管成像(superb microvascular imaging,SMI)与彩色多普勒血流显像(color Doppler flow imaging,CDFI)检出乳腺良恶性肿物的血流情况的差异,并分析二者与肿物病理微血管密度(microvessel density,MVD)的相关性。方法：对79名女性乳腺肿物患者共88个病灶(良性48个、恶性40个)进行SMI及CDFI检测,参照Adler分级标准,应用上述两种成像技术分别观察同一病灶的血流情况,Kappa分析以评估其一致性。对术后病理标本进行CD34免疫组织化学染色,检测肿瘤MVD表达,Spearman相关系数,分别分析MVD表达与两种模式的Adler分级的相关性。结果：两种技术方法对所有病灶血流丰富程度的一致性不佳(κ=0.67);对恶性病灶血流丰富程度的二者检测一致性差(κ=0.39);而SMI和CDFI对良性病灶血流丰富程度的检出一致性较好(κ=0.79)。相比较CDFI,SMI与MVD具有显著的正相关性(r=0.82、P<0.01)。结论：SMI对乳腺恶性肿物的微血管检出效果更佳,与病理MVD相关性较好,可作为术前无创性评估乳腺恶性肿瘤微血管的影像学方法。