磁共振弥散加权成像与动态增强MRI诊断乳腺良恶性病变的应用价值对比评价

目的对比分析磁共振弥散加权成像与动态增强MRI在诊断乳腺良恶性病变的临床价值。方法选取2014年1月~4月我院收治的乳腺肿瘤患者59例,对所有患者均分别采用磁共振弥散加权成像和动态增强MRI扫描,将结果与病理学结果对比,分析评价二者在诊断乳腺良恶性肿瘤方面的临床价值。结果 MRI分析有恶性23例,良性36例,TIC分析有恶性24例,良性35例,MRI组符合率为92.0%(23/25),TIC组符合率为96.0%(24/25)。结论磁共振弥散加权成像与动态增强MRI均可有效判断乳腺肿瘤的性质,在乳腺癌的早期诊断中具有较高的应用价值,但比较来看磁共振弥散加权成像诊断准确性更高。     

乳腺动态增强MRI与X射线钼靶摄影对致密型乳腺对比研究

目的探讨乳腺动态增强MRI(DCE-MRI)与乳腺X射线钼靶摄影(MG)对致密型乳腺的诊断价值。方法选择2014年至2015年上海市第六人民医院行乳腺MG诊断为致密型乳腺(ACR3-4类)的患者112例,均为女性,年龄28~81岁,平均年龄50.1岁。其中86例患者行乳腺DCE-MRI。对这两种影像学方法在致密型乳腺中良、恶性病变检出进行对照研究,通过统计学方法分析算出两种检查方法在致密型乳腺中良、恶性病变的灵敏度、特异度、准确度并与病理对照,以期客观评价两种检查方法对致密型乳腺疾病的诊断效能。结果 86例患者中组织病理学诊断为恶性32例,良性54例。乳腺MG对致密型乳腺中病变诊断的符合率为68.60%(59/86),误诊率为29.63%(16/54),漏诊率为34.38%(11/32)。DCE-MRI诊断的符合率为89.53%(77/86),误诊率为9.26%(5/54),漏诊率为12.50%(4/32)。MG诊断致密型乳腺恶性病变的灵敏度、特异度、准确度分别为65.63%、70.37%、68.60%;DCE-MRI诊断致密型乳腺恶性病变的灵敏度、特异度、准确度分别为87.50%、90.74%、89.53%。两种影像学方法对致密型乳腺恶性病变诊断的灵敏度、特异度、准确度差异均有显著统计学意义(P=0.001,P=0.001,P=0.002)。结论乳腺DCE-MRI对致密型乳腺病变的诊断效能优于MG检查。     

磁共振弥散加权成像在鉴别肝脏局灶性良恶性病变中的诊断价值

目的:探讨磁共振(MR)弥散加权成像(DWI)在鉴别肝脏局灶性良恶性病变中的诊断价值。方法:选取83例肝脏局灶性病变患者,全部患者均行常规磁共振成像(MRI)和DWI检查,在DWI检查中选定兴趣区后对病灶不同扩散敏感系数(b值)下的表观弥散系数(ADC值)进行测量,分析肝脏局灶性病变患者的DWI信号表现,对比良恶性病变患者不同b值下的ADC值,以病理结果为参照,比较常规MRI、DWI两种检查方式诊断肝脏局灶性恶性病变的结果,评价诊断效能。结果:83例肝脏局灶性病变患者中恶性病变57例(包括肝转移瘤22例,原发性肝癌35例),良性病变26例(包括肝囊肿12例,肝血管瘤14例),在DWI上原发性肝癌呈高信号或混杂信号,肝转移瘤、肝血管瘤均呈高信号,肝囊肿呈低信号;肝脏局灶性恶性病变高、中、低b值下的ADC值均低于良性病变的ADC值(P0.05);DWI诊断肝脏局灶性恶性病变患者的特异性(96.15%vs 69.23%)、准确性(90.36%vs 72.29%)高于常规MRI(P0.05),误诊率(3.85%vs 30.77%)低于常规MRI(P0.05),DWI诊断肝脏局灶性恶性病变患者的敏感性(87.72%vs 73.68%)、漏诊率(12.28%vs 26.32%)与常规MRI比较无显著差异(P0.05)。结论:在MR鉴别肝脏局灶性良恶性病变中,应用DWI检查可通过比较肝脏局灶性良恶性病变患者的DWI图像信号特征和ADC值判断病变性质,有效提高诊断肝脏局灶性恶性病变患者的特异性和准确性。     

3.0TMRI增强扫描对恶性乳腺非肿块样强化病变的评估价值研究

目的:分析3.0T超导磁共振成像(3.0TSuperconducting magnetic resonance imaging,3.0TMRI)增强扫描对恶性乳腺非肿块样强化病变(Non mass like enhancement of breast,NME)的评估价值.方法:选取2019年6月至2020年7月在本院行X线或B超检查为可疑病变者后,接受3.0TMRI乳腺确诊为NME患者87例,以病理检查结果为标准,计算3.0TMRI对NME良、恶性鉴别准确性,并对比不同性质NME 3.0TMRI影像征像.结果:3.0TMRI对恶性NME检出准确性94.23％,良性NME为94.28％,与病理结果比较无差异(P>0.05);良性、恶性NME在中分布方式、内部强化情况等征象中比较并无差异(P>0.05),恶性NME脂肪抑制T2加权像(T2 weighted imaging,T2WI)信号以等信号为主,时间强度曲线(Time intensity curve,TIC)类型以Ⅲ型为主;良性NME脂肪抑制T2WI信号以稍等信号为主,TIC类型以I-Ⅱ型为主,恶性NME与良性NME在脂肪抑制T2WI信号、TIC类型上比较有差异(P<0.05);恶性NME MRI定量参数中Ktrans、Kep值明显高于良性NME、(P<0.05),Ve比较则无差异(P>0.05).结论:3.0TMRI征象、半定量参数在恶性NME病变诊断鉴别中有较高的使用价值.     

乳腺钼靶片中钙化灶对乳腺疾病的诊断价值分析

目的分析乳腺钼靶片中钙化灶对乳腺疾病的诊断价值。方法选取我院2011年3月~2014年3月收治的经手术或穿刺活检病理证实的58例乳腺疾病病灶钙化患者,所有患者均行乳腺钼靶片检查,分析乳腺良恶性疾病的钙化特点。结果本组58例病灶中,良性病变23例,占39.7%,其中乳腺纤维瘤14例,乳腺囊肿5例,乳腺血管粥样硬化2例,乳腺增生2例,恶性病变35例,占60.3%,其中浸润导管癌18例,腺癌13例,黏液癌4例。良性钙化可见大颗粒钙化,爆米花样钙化、弧形钙化、圆形钙化,恶性钙化可见杆状、分支状及泥沙样多形性、泥沙样钙化、小杆状及分支样。单位面积钙化数目≥5枚/cm252例,其中47例为恶性病变,单位面积钙化数目<5枚/cm26例,均为良性病变。结论乳腺钼靶片中钙化灶对乳腺疾病的诊断以及乳腺疾病良恶性的鉴别具有重要的意义,能够降低误诊和漏诊的发生率,为临床治疗提供重要依据。     

DWI+DCE-MRI对乳腺癌恶性病灶检出率的影响

目的:探讨扩散加权成像(Diffusion weighted imaging,DWI)+动态增强磁共振成像(Dynamic enhanced-magnetic resonance imaging,DCE-MRI)对乳腺癌恶性病灶检出率的影响.方法:选取164例我院2018年6月至2020年6月就诊的疑似乳腺病变患者,所有患者均采用DWI、DCE-MRI进行检查,以手术病理检查结果为"金标准",比较DWI、DCE-MRI单独及联合检查诊断结果、诊断效能及对不同类型乳腺癌恶性病变的检出率,并通过DWI检查比较良、恶性乳腺癌病灶表观扩散系数(Apparent diffusion coefficient,ADC).结果:经DWI检查,阳性119例;经DCE-MRI检查,阳性121例;经联合检查,阳性136例;与DWI、DCE-MRI单独检查相比,联合检查灵敏度、准确率较高,漏诊率较低(P<0.05);与DWI、DCE-MRI单独检查相比,联合检查对于微浸润癌检出率较高(P<0.05);与良性病灶相比,当b=800s?mm-2、b=1000s?mm-2时恶性病灶ADC值较小(P<0.05).结论:DWI+DCE-MRI检查诊断准确率显著高于单独检查,可有效提高诊断效能及对不同类型乳腺病变检出率,对临床早期筛查诊断、制定治疗方案具有重要意义.     

灰阶超声造影鉴别诊断前列腺良恶性结节的价值

目的探讨灰阶超声造影在前列腺良、恶性结节鉴别诊断中的价值。方法对90例总血清前列腺特异性抗原(TPSA)增高的疑似前列腺癌患者行经直肠超声(TRUS),采用SonoVue造影剂行超声造影(CEUS),观察病灶造影增强方式,对其中57个良、恶性结节患者用ACQ软件绘制时间强度曲线(TIC),分析造影参数,比较良、恶性病变间的差异。造影结束同时对患者行经直肠超声引导穿刺活检。结果90例前列腺疾病患者中,良性病变55例,其中结节性病变22例共29个结节,前列腺增生33例;恶性病变35例,结节病灶28例28个,弥漫性病变7例。良性结节超声造影以均匀增强为主,结节边界清晰;恶性结节早于正常外腺组织增强为主。恶性结节达峰时间短于良性结节(<0.05),峰值强度低于良性结节(<0.05),到达时间良、恶性间差异无统计学意义(P>0.05)。CEUS对前列腺病变的良恶性鉴别诊断符合率高于TRUS(<0.05)。CEUS诊断的敏感度、特异度和正确率均高于TRUS(<0.05)。结论经直肠灰阶超声造影对前列腺癌的早期发现及对良恶性病变的鉴别诊断均具有一定的临床应用价值。     

神经细胞黏附分子在老年乳腺癌中表达的研究

目的:研究神经细胞黏附分子(neural cell adhesion molecules,NCAM)在乳腺肿瘤中的表达并讨论其临床意义。方法:选择老年(60~72岁)乳腺癌组织25例(神经侵袭的8例,无神经侵袭的17例)、癌旁组织25例,乳腺良性病变26例(乳腺纤维腺瘤16例,乳腺腺病4例,囊性乳腺病6例),用免疫组织化学方法检测NCAM在这些组织中的表达。用酶联免疫吸附法(ELISA)测定NCAM在乳腺癌、乳腺良性病病和正常对照(n=30,61~68岁)血清中含量。结果经统计学处理。结果:NCAM在部分乳腺癌细胞质和细胞膜呈宗黄色阳性表达,癌旁组织和良性病变表达很少。乳腺癌组、癌旁组及乳腺良性病变NCAM阳性率分别为28.0%(7/25),4.0%(1/25)和3.8%(1/26),腺癌组织明显高于癌旁组和乳腺良性病变(P0.05)。NCAM阳性表达在乳腺癌有神经侵袭病例阳性率50.0%(4/8)明显高于无神经侵袭病例23.5%(4/17,P0.05)。血清NCAM浓度(pg/ml)结果,乳腺癌(69.8±29.4)显著高于乳腺良性病变(16.7±6.3)和正常对照(14.9±3.1),具有明显统计学差异(P0.05)。结论:NCAM在老年乳腺癌有明显表达,且伴发神经侵袭和转移时表达更为明显,表明NCAM表达影响乳腺癌发生发展,这为临床防治老年乳腺癌提供重要的参考依据。     

乳腺计算机辅助诊断中DCE-MRI图像特征的选择与分析

目的比较动态对比度增强磁共振成像（dynamic contrast—enhanced magnetic resonance imaging,DCE—MRI）图像的形态、纹理和时间强度曲线（time intensity curve,TIC）特征对乳腺病灶良恶性的诊断效果,讨论DCE—MRI图像特征的计算机辅助诊断价值。方法测量224个乳腺病灶样本（良性样本82个,恶性样本142个）的12个形态学特征、56个基于灰度共生矩阵（gray level co—occurrencematrix,GLCM）的纹理特征以及11个TIC特征,采用平均平方距离准则和SVM分类器评估这三类特征的良恶性分辨能力。结果反映病灶血流动力学特性的TIC特征的分类性能最优（SE0．9366,SP0．8293,AUC0．9495）;纹理特征次之（SE0．9225,SP0．7195,AUC0．8835）;形态学特征效果最差（SE0．8451,SP0．6951,AUC0．8079）。研究发现,在上述基础上融合三类特征可优化分类性能。最终结合平滑度、紧致度、熵等9个特征参数进行诊断,对乳腺病灶良恶性的分辨效果最好,AUC达0．9642。结论DCE—MRI的TIC特征对恶性乳腺病灶具有较高的灵敏度,可以提高乳腺计算机辅助诊断的恶性病灶检出率。综合分析形态、纹理和TIC特征可以提高病灶的诊断特异度,降低良性病灶的误诊率。     

DCE-MRI联合多b值DWI技术对乳腺病变性质的鉴别分析

目的:观察动态增强核磁共振检查技术(DCE-MRI)联合多b值磁共振弥散成像(DWI)技术在乳腺占位性病变性质中的诊断效能.方法:选取我院2020年1月至2020年12月期间收治的75例乳腺病变患者,均开展DCE-MRI联合多b值(b值取500 s·mm-2、800 s·mm-2、1000 s·mm-2)DWI技术检查,将病理切片作为金标准,分析联合检查在乳腺病变中的诊断效能.结果:入组75例乳腺病变患者经快速病理切片检查结果显示,恶性38例,占比50.67％,良性病变37例,占比49.33％;与良性组相比,b值取500 s·mm-2、800 s·mm-2、1000 s·mm-2时,恶性组ADC值均较低(P<0.05);经绘制ROC曲线图结果显示b值取800 s·mm-2时AUC最大(0.808),将b值取800 s·mm-2时最佳阈值作为阳性分界值,DWI诊断效能:特异度94.59％、灵敏度81.58％、阳性预测值93.94％,DWI检查一致性Kappa值为0.760;特异度94.59％、灵敏度84.21％、阳性预测值94.12％,DCE-MRI检查一致性Kappa值为0.783;联合诊断效能:特异度97.30％、灵敏度94.74％、阳性预测值97.30％,联合诊断一致性Kappa值为0.920.结论:DCE-MRI联合DWI检查(b值取800 s·mm-2)可显著提高乳腺恶性病变的诊断效能     

Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is being used increasingly in the detection and diagnosis of breast cancer as a complementary modality to mammography and sonography. Although the potential diagnostic value of kinetic curves in DCE-MRI is established, the method for generating kinetic curves is not standardized. The inherent reason that curve identification is needed is that the uptake of contrast agent in a breast lesion is often heterogeneous, especially in malignant lesions. It is accepted that manual region of interest selection in 4D breast magnetic resonance (MR) images to generate the kinetic curve is a time-consuming process and suffers from significant inter- and intraobserver variability. We investigated and developed a fuzzy c-means (FCM) clustering-based technique for automatically identifying characteristic kinetic curves from breast lesions in DCE-MRI of the breast. Dynamic contrast-enhanced MR images were obtained using a T1-weighted 3D spoiled gradient echo sequence with Gd-DTPA dose of 0.2 mmol/kg and temporal resolution of 69 s. FCM clustering was applied to automatically partition the signal-time curves in a segmented 3D breast lesion into a number of classes (i.e., prototypic curves). The prototypic curve with the highest initial enhancement was selected as the representative characteristic kinetic curve (CKC) of the lesion. Four features were then extracted from each characteristic kinetic curve to depict the maximum contrast enhancement, time to peak, uptake rate, and washout rate of the lesion kinetics. The performance of the kinetic features in the task of distinguishing between benign and malignant lesions was assessed by receiver operating characteristic analysis. With a database of 121 breast lesions (77 malignant and 44 benign cases), the classification performance of the FCM-identified CKCs was found to be better than that from the curves obtained by averaging over the entire lesion and similar to kinetic curves generated from regions drawn within the lesion by a radiologist experienced in breast MRI.     

Classification of breast lesions pre‐contrast injection using water resonance lineshape analysis

Inhomogeneously broadened, non‐Lorentzian water resonances have been observed in small image voxels of breast tissue. The non‐Lorentzian components of the water resonance are probably produced by bulk magnetic susceptibility shifts caused by dense, deoxygenated tumor blood vessels (the ‘blood oxygenation level‐dependent’ effect), but can also be produced by other characteristics of local anatomy and physiology, including calcifications and interfaces between different types of tissue. Here, we tested the hypothesis that the detection of non‐Lorentzian components of the water resonance with high spectral and spatial resolution (HiSS) MRI allows the classification of breast lesions without the need to inject contrast agent. Eighteen malignant lesions and nine benign lesions were imaged with HiSS MRI at 1.5 T. A new algorithm was developed to detect non‐Lorentzian (or off‐peak) components of the water resonance. After a Lorentzian fit had been subtracted from the data, the largest peak in the residual spectrum in each voxel was identified as the major off‐peak component of the water resonance. The difference in frequency between these off‐peak components and the main water peaks, and their amplitudes, were measured in malignant lesions, benign lesions and breast fibroglandular tissue. Off‐peak component frequencies were significantly different between malignant and benign lesions (p < 0.001). Receiver operating characteristic (ROC) analysis was used to assess the diagnostic performance of HiSS off‐peak component analysis compared with dynamic contrast‐enhanced (DCE) MRI parameters. The areas under the ROC curves for the ‘DCE rapid uptake fraction’, ‘DCE washout fraction’, ‘off‐peak component amplitude’ and ‘off‐peak component frequency’ were 0.75, 0.83, 0.50 and 0.86, respectively. These results suggest that water resonance lineshape analysis performs well in the classification of breast lesions without contrast injection and could improve the diagnostic accuracy of clinical breast MR examinations. In addition, this approach may provide an alternative to DCE MRI in women who are at risk for adverse reactions to contrast media. Copyright © 2012 John Wiley & Sons, Ltd.     

三维颅脑容积成像在小儿颅内肿瘤诊断中的价值

目的分析三维颅脑容积成像(3D BRAVO)在小儿颅内良恶性肿瘤鉴别诊断中的价值。方法选取71例颅内肿瘤患儿为观察对象,均行颅脑MRI普通增强扫描和3D BRAVO。观察MRI普通增强扫描和MRI普通增强扫描+3D BRAVO的肿瘤检出情况、不同直径肿瘤检出情况及良恶性肿瘤检出情况。结果MRI普通增强扫描+3D BRAVO的肿瘤检出符合率高于MRI普通增强扫描(χ²=25.101,P=0.001)。MRI普通增强扫描+3D BRAVO对不同直径肿瘤的检出符合率高于MRI普通增强扫描(χ²=3.842,P=0.021)。MRI普通增强扫描+3D BRAVO的良恶性肿瘤检出符合率均明显高于MRI普通增强扫描(χ²=13.940、9.401,P=0.000、0.002)。MRI普通增强扫描+3D BRAVO对肿瘤良恶性鉴别的准确率、敏感度、特异度、阳性预测率、阴性预测率(96.04%、91.43%、98.48%、96.97%、95.59%)均高于MRI普通增强扫描(71.29%、60.00%、77.27%、58.33%、78.46%),差异有统计学意义(χ²=22.638、9.401、13.940、14.430、6.153,P=0.000、0.002、0.000、0.000、0.013)。结论与单独使用MRI普通增强扫描相比,MRI普通增强扫描辅以3D BRAVO在小儿颅内肿瘤诊断中有更高的肿瘤检出符合率和不同直径肿瘤检出符合率,在准确鉴别颅内良恶性肿瘤上更具优势。     

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.     

基于空间FCM与MRF方法的乳腺MRI序列三维病灶分割研究

针对乳腺DCE-MRI病灶分割,提出一种空间FCM聚类与MRF随机场相结合的三维分割方法。首先,对MRI图像进行空间FCM粗分割,提取病灶粗轮廓。然后,在其基础上进行MRF精分割,并结合病灶三维信息：用相邻切片分割结果对应标号矩阵初始化MRF精分割标号场,同时用该张切片粗分割所得隶属度矩阵对MRF精分割进行参数自适应调整。用该方法与空间FCM、水平集、模糊MRF方法对50例MRI数据进行分割对比实验,得到良、恶性病灶分割重叠率分别为76.4、75.5;相比于空间FCM的68.%、69.5水平集的70.8、72.6以及模糊MRF的72.9、73.6有明显提升。对所有175例MRI数据分割结果进行非监督评价,得到良、恶性病灶区域均匀性均大于0.92;区域内差异性良性病灶92%小于150、恶性病灶98%小于150;区域间差异性良性病灶87%大于0.25、恶性病灶90%大于0.3综上表明,该方法具有较高的分割精度。     

Diagnostic performance of contrast-enhanced ultrasound and enhanced magnetic resonance for breast nodules

In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound (US), contrastenhanced US (CEUS), combined US and CEUS and magnetic resonance imaging (MRI) in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve. The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and 91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them (kappa < 0.20). In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.     

鼻咽癌颈部淋巴结转移MR多b值DWI成像研究分析

目的：研究在鼻咽癌(nasopharyngeal carcinoma,NPC)颈部淋巴结转移的评价中1.5T MR多b值扩散加权成像(diffusion weighted imaging,DWI)的应用价值。方法：对良性淋巴结增大病人15例及鼻咽癌病人37例进行常规MR及多b值DWI检查,对不同b值的DWI图像质量进行比较。对不同b值下良、恶性淋巴结ADC值的ROC曲线进行记录。结果：b=800 s/mm2时鼻咽部变形小,图像背景抑制充分,伪影少,周围软组织与病灶具有较好的对比度,小淋巴结显示清楚;鼻咽癌、良性淋巴结及颈部转移性淋巴结的ADC值随着b值增大均呈下降趋势,8种b值下转移性淋巴结与鼻咽癌原发灶向比较,ADC值差异无统计学差异(P>0.05)。而良性淋巴结与转移性淋巴结相比较,ADC值差异均有统计学差异(P<0.05);b=800 s/mm2时对良恶性淋巴结的鉴定效果最好,灵敏度为100%,特异度为83.2%。结论：1.5T MR扩散加权成像(DWI)技术能有效鉴别淋巴结性质,b值取800 s/mm2时,DWI图像具有较好的质量,且对良恶性淋巴结的鉴定诊断效果最好,可在临床鼻咽癌颈部淋巴结转移的诊断中推广应用。     

乳腺三维断层摄影与乳腺彩超在乳腺疾病诊断中的效能对比

【摘 要】 目的:研究数字乳腺三维断层技术（DBT）和乳腺超声在诊断乳腺病变效能的不同以及联合应用对于乳腺诊断的价值。 方法:收集1 065例乳腺病变患者,其中333例有完整的病理检查资料。以乳腺影像报告和数据系统（BI-RADS）分类为标准,分析DBT、超声、DBT+超声在乳腺病变诊断中的分布差异。根据333例病理结果,比较3种诊断模式的诊断效能。 结果:DBT与超声的BI-RADS分布有显着性差异（P=0.001）,DBT与DBT+超声或超声与DBT+超声的BI-RADS分布无显著性差异（P=0.258, 0.394）。3种诊断模式均可明显区分恶性和良性乳腺病变（P<0.001）。多组独立样本Kruskal-Wallis秩和检验分析显示3种诊断模型存在差异（[χ2]=14.982, P=0.001）。DBT的特异性、误诊率、准确性和阳性预测值明显优于超声。超声显示囊性病变优于DBT,且超声对确定良性肿块的敏感性优于DBT。DBT+超声的特异性为99.5％,误诊率为0.5%,阳性预测值也达到99%。DBT+超声的检查模式优于单独使用DBT或超声。 结论:与单独的DBT或超声相比,DBT和超声的组合可以提高乳腺病变的诊断效能。     

Diffusion weighted MR imaging in acute vertebral compression fractures: differentiation between malignant and benign causes

Aim

The primary objective of this study was to evaluate the specificity and sensitivity of diffusion weighted MR imaging (DWI) in the differentiation and characterisation between benign and malignant vertebral compression fractures compared with conventional T1 WI, T2 WI and fat suppressed contrast enhanced T1 WI in the Malaysian population.

Materials and Methods

Thirty five patients with 68 vertebral compression fractures were imaged using the conventional T1 WI, T2 WI, fat suppressed contrast enhanced T1-weighted, and steady state free precession diffusion-weighted (SSFP DWI) sequences on a 1.5 T MR scanner. Signal intensities were analysed qualitatively for all the sequences by comparison to adjacent normal marrow. A quantitative assessment of the signal intensity in the SSFP DWI was also performed.

Results

T1 WI and T2 WI images are of limited diagnostic value because of the variability in signal intensities. Contrast enhanced images had sensitivity and specificity of 93% and 71%, respectively with a negative predictive value (NPV) of 93%. On diffusion-weighted MR imaging, sensitivity was 87% with specificity of 92%. The positive predicative value (PPV) and NPV were both 90%. The quantitative assessment of ratio revealed a statistical significant difference between the benign (0.96) and the malignant (1.73) group of lesion (Mann-Whitney U-test, p=0.0001).

Conclusions

We found that absence of contrast enhancement has a high NPV (90%) while SSFP DWI has both a high PPV (90%) and high NPV (90%) in detecting malignant vertebral compression fractures. Furthermore, in our study the ratio of lesion intensity technique offers an excellent criterion to differentiate between the benign and malignant lesions, and the presence of iso- or hypointensity of the collapsed vertebral bodies is suggestive of a benign lesion while hyperintensity is highly suggestive of malignancy. We also found that using the NLMR showed a statistical significant difference between the malignant and benign groups (p<0.0001) with osteoporotic and malignant lesions have mean values of 0.96 (SD 0.25) and 1.73 (SD 0.4) respectively.