Solitary pulmonary nodules: comparison of multi-slice computed tomography perfusion study with vascular endothelial growth factor and microvessel density

Background The solitary pulmonary nodule （SPN） is one of the most common findings on chest radiographs. The objectives of clinical practice are to differentiate malignant nodules from benign nodules in the least invasive way and to make a specific diagnosis. This study was aimed to evaluate the correlation between perfusion imaging features and microvessel density （MVD） and vascular endothelial growth factors （VEGF） in SPNs using multi-slice computed tomography （MSCT）; and to provide the theoretical basis for SPN blood flow pattern and blood flow quantitative features. Also, the study called for the discussion of the method＇s clinical application value in the differential diagnosis of benign and malignant SPNs.Methods Sixty-eight patients with SPN underwent multi-location dynamic contrast enhanced （nonionic contrast material was administrated via the antecubital vein at a rate of 4 ml/s） MSCT. Precontrast and postcontrast attenuations on every scan was studied. Perfusion, peak height, and the ratio of the peak height of the SPN to that of the aorta were analyzed. Perfusion was calculated using the maximum gradient of the time-density curves （TDC） and the peak height of the aorta. The quantitative parameters （perfusion, peak height, ratio of peak height of the SPN to that of the aorta） of the blood flow pattern were compared with MVD and the VEGF expression of immunohistochemistry. Results The perfusion peak heights of malignant （（96.15±11.55） HU） and inflammatory （（101.15±8.41） HU） SPNs were significantly higher than those of benign （（47.24±9.15） HU） SPNs （P 〈0.05, P 〈0.05）. Ratios of SPN-to-aorta of malignant and inflammatory SPNs were significantly higher than those of benign SPNs （P 〈0.05, P 〈0.05）. No significant differences were found between the peak height and SPN-to-aorta ratio of malignant SPNs and inflammatory SPNs （P 〉0.05, P 〉0.05）. The precontrast densities of inflammatory SPNs were lower than those of malignant SPNs （P 〈0.05）. Perfusion values of malignant and inflammatory SPNs were significantly higher than those of the benign SPNs （P 〈0.05, P 〈0.05）. The VEGF positive expressions appeared in 32 patients with malignant SPNs and 2 patients with benign SPNs, and the average value of the MVD was higher in patients with malignant SPNs （36.88±6.76） than in patients with either benign （4.51±0.60） or inflammatory （26.11±5.43） SPNs （P 〈0.05, P 〈0.05）. There were statistically significant correlations between the CT perfusion feature and the MVD. The highest correlation was between the peak height of SPN and the MVD （r=0.657, P 〈0.05）.Conclusions Tumor microvessel density and VEGF expression facilitate the exploration of the pathophysiological basis of CT perfusion in SPNs. Multi-slice CT perfusion has shown strong positive correlations with angiogenesis in SPNs.     

IMPROVED MARKING AND CHARACTERIZING OF PULMONARY NODULES ON DIGITAL RADIOGRAPHS USING A COMPUTER-AIDED DIAGNOSIS SYSTEM

Objective To evaluate and reduce inter-observer variations in the detection and characterization of pulmonary nodules on digital radiograph (DR) chest images. Methods Two hundreds and thirty-two new posterior-anterior DR chest images were collected from out-patient screening patients. Consensus was reached by two experienced radiologists on the marking, rating, and segmentation of small actionable nodules ranged from 5 to 15 mm in diameter using a computer-aided diagnosis (CAD) system. Both their own nodule findings and the computer's automatic nodule detection results were analyzed to make the consensus. Nodules identified together with corresponding likelihood rating and segmentation results were referred as "Gold Standard". Two un-experienced radiologists were asked to first mark and characterize suspicious nodules independently, then were allowed to consult the computer nodule detection results and change their decisions. Results Large inter-observer variations in pulmonary nodule identification and characterization on DR chest images were observed between un-experienced radiologists. Un-experienced radiologists could greatly benefit from the CAD system, including substantial decrease of inter-observer variation and improvement of nodule detection rates. Moreover, radiologists with different levels of skillfulness could achieve similar high level performance after using the CAD system. Conclusion The CAD system shows a high potential for providing a valuable assistance to the examination of DR chest images.     

Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma

Background Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis. Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and specificity for hepatocellular carcinoma at an early stage. In this study, the serum proteins in patients with hepatocellular carcinoma or liver cirrhosis and in normal controls were analysed. Surface enhanced laser desorption/ionization time-of-flight mass (SELDI-TOF-MS) spectrometry was used to fingerprint serum protein using the protein chip technique and explore the value of the fingerprint, coupled with artificial neural network, to diagnose hepatocellular carcinoma.Methods Of the 106 serum samples obtained, 52 were from patients with hepatocellular carcinoma, 22 from patients with liver cirrhosis and 32 from healthy volunteers. The samples were randomly assigned into a training group (n=70, 35 patients with hepatocellular carcinoma, 14 with liver cirrhosis, and 21 normal controls) and a testing group (n=36, 17 patients with hepatocellular carcinoma, 8 with liver cirrhosis, and 11 normal controls). An artificial neural network was trained on data from 70 individuals in the training group to develop an artificial neural network diagnostic model and this model was tested. The 36 sera in the testing group were analysed with blind prediction by using the same flowchart and procedure of data collection. The 36 serum protein spectra were clustered with the preset clustering method and the same mass/charge (M/Z) peak values as those in the training group.　 Matrix transfer was performed after data were output. Then the data were input into the previously built artificial neural network model to get the prediction value. The M/Z peaks of the samples with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3.1 for noise filtering. The first threshold for noise filtering was set at 5, and the second was set at 2. The 10% was the minimum threshold for clustering. The statistical analysis of the data of serum protein mass spectrum was performed in the groups (normal vs. hepatocellular carcinoma, and liver cirrhosis vs. hepatocellular carcinoma) with the t test. Results Comparison between the groups of hepatocellular carcinoma and normal control: The mass spectra from 56 samples (hepatocellular carcinoma and normal controls) in the training group were analysed and 241 peaks were obtained. In addition, 21 peaks from them were used for comparison between the groups of hepatocellular carcinoma and normal controls (P<0.01). Only 2 peaks at 3015 M/Z and 5900 M/Z were selected with significant difference ［P<10（-9）］. A model was developed based on these two proteins with different M/Z. It was confirmed that this artificial neural network model can be used for comparison between the groups of hepatocellular carcinoma and normal controls. The sensitivity was 100% (17/17), and the specificity was 100% (11/11). Comparison between the groups of hepatocellular carcinoma and liver cirrhosis: The mass spectra from 49 samples in the training group (including patients with hepatocellular carcinoma and liver cirrhosis) were analysed and 208 peaks were obtained. In addition, 21 peaks from them were used for comparison between the groups of hepatocellular carcinoma and liver cirrhosis (P<0.01). Only 2 peaks at 7759 M/Z, 13134 M/Z were selected with significant difference ［P<10（-9）］. A model was developed based on these two proteins with dfferent M/Z. It was confirmed that this artificial neural network model can be used for comparison between the groups of hepatocellular carcinoma and liver cirrhosis. The sensitivity was 88.2% (15/17), and the specificity was 100% (8/8).Conclusions The specific biomarkers selected with the SELDI technology could be used for early diagnosis of hepatocellular carcinoma.     

Air bronchogram in differential diagnosis of small peripheral lung cancers on CT image.

The computed tomographic (CT) images of 52 patients of small peripheral lung nodule < 3 cm proved pathologically were reviewed. Air bronchogram (AB) was found in 14 nodules on thin-section CT images, in which well or moderately differentiated small adenocarcinomas were only seen with lipidic growth. Squamous-cell, large-cell carcinomas and small benign lung nodules had no AB sign both on CT image and in pathologic specimens.

     

Diagnostic value of ACR TI-RADS combined with three-dimensional shear wave elastography in ACR TI-RADS 4 and 5 thyroid nodules

Background: Three-dimensional shear wave elastography (3D-SWE) is a promising method in distinguishing benign and malignant thyroid nodules. By combining with conventional method, it may further improve the diagnostic value. The study aimed to assess the diagnostic value of American College of Radiology (ACR) thyroid imaging reporting and data system (TI-RADS) combined with 3D-SWE in ACR TI-RADS 4 and 5 thyroid nodules.Methods: All nodules were examined by conventional ultrasonography, ACR TI-RA...     

计算机辅助设计抗感染中药复方的实验研究

Objective To study the feasibility of artificial immune system (AIS) in assistant designing of computer for new Chinese herbal compounds with anti-infectious function. Methods A group of Chinese herbal drug compounds from Treatise on Exogenous Febrile and Miscellaneous Diseases[WTBZ] were selected as training samples. Serum was obtained from mice which were infected by Staphylococcus aureus and ig administrated with the various Chinese herbal compounds. The inhibition ability against S. aureus of the serum was tested and input into an artificial neural network (ANN) together with the characters of the every Chinese herb included in those compounds. The ANN was optimized using AIS so that it can predict the inhibitory ability against S. aureus of the Chinese herbal compounds according to the characters of Chinese herbs included in those compounds. After that the computer generated a number of Chinese herbal compounds randomly, calculated their inhibitory ability against S. aureus and output those among the top levels, so as to verify whether the predictions were correct or not. Results After verification, the Chinese herbal drug compounds designed by computer could significantly raise the serum′s inhibitory ability against S. aureus. Conclusion The computer can be an assistant for designing new Chinese herbal compounds with anti-infectious function.     

Sublingual Nodules: Diagnostic Markers of Metastatic Breast Cancer

Objective: To evaluate the diagnostic significance of sublingual nodules for metastasis of patients with breast cancer and further to explore the mechanisms of sublingual nodules. Methods: The image data of 117 in-patients with breast cancer in stage Ⅰ–Ⅳ in Tianjin Medical University Cancer Institute and Hospital from December 2009 to September 2011 were assessed retrospectively. All photos of patients' tongue were recorded by the digital camera of uniform type within 1 month after serological examination and regular re-examined by computed tomography(CT), magnetic resonance imaging and positron emission tomography CT. The presence of sublingual nodules was the positive standard. Chi square test and two-independent-sample test were used to determine the diagnostic value between the status of sublingual nodules and Clinico-pathological characteristics. The optimal cut-off of uric acid(UA) level to diagnose sublingual nodules was determined by receiver operating curve(ROC) analysis. Results: Breast cancer patients with sublingual nodules had a higher risk of recurrence and/or metastasis than patients without it(P0.001). Sublingual nodules was significantly correlated with increased serum UA level(P=0.001). The optimal cut-off value of UA level to diagnose sublingual nodules was 290 μmol/L. Furthermore, the elevated serum UA level( 290 μmol/L) was significantly related to breast cancer recurrence and/or metastasis(P0.001). Conclusions: Sublingual nodules were potential diagnostic markers for metastatic breast cancer. The formation of sublingual nodules was associated with elevated level of serum UA.     

Clinical Value of Contrast-enhanced Ultrasound in Differentiating Benign and Malignant Focal Liver Lesions

To explore the clinical value of contrast-enhanced ultrasound （CEUS） in differentiating benign and malignant focal liver lesions （FLLs） with SonoVue, CEUS was used to examine 113 patients with focal liver lesions （FLLs） in our hospital during July 2005 to December 2006. All the patients underwent contrast-enhanced CT （CECT） or contrast-enhanced MRI （CEMRI）. Except for patients with focal fatty sparings （n=18） and with hemangiomas （n=8）, all the patients were confirmed by operation or ultrasonic-guided liver puncture biopsy. A sulfur hexafluoride gas-based contrast agent was used with a MI of 0.15 to 0.17. Forty-eight cases of malignant FLLs, including 30 hepatocellular carcinomas （HCCs）, 2 cholangiocarcinomas and 16 metastatic tumors, were detected. Seventy-eight cases of benign FLLs, including 33 hemangiomas, 9 focal nodular hyperplasias （FNHs）, 19 focal fatty sparings, 5 abscesses, 7 regenerative nodules and 2 inflammatory pseudo-tumor, were involved. The contrast pattern of benign and malignant FLLs was quite different. CEUS has higher specificity and sensitivity than conventional ultrasound in differentiating benign and malignant FLLs.     

Automated lung segmentation algorithm for CAD system of thoracic CT

Objective： To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic （CAD） system, especially on the segmentation between left and right lungs. Methods： We put forward the base frame of our lung segmentation firstly. Then, using optimal thresholding and mathematical morphologic methods, we acquired the rough image of lung segmentation. Finally, we presented a fast self-fit segmentation refinement algorithm, adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results： Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert＇s manual outlines, and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion： It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.     

Feasibility and value of quantitative dynamic contrast enhancement MR imaging in the evaluation of sinonasal tumors

Background Quantitative dynamic contrast enhancement MR imaging （DCE-MRI）,used to measure properties of tissue microvasculature and tumor angiogenesis,is a promising method for distinguishing benign and malignant tumors and characterizing tumor response to antiangiogenic treatment.The aim of this study was to assess the feasibility of quantitative parameters derived from clinically used DCE-MRI for distinguishing benign from malignant tumors in the sinonasal area,which may be potentially useful for prediction and monitoring of treatment response to chemoradiotherapy of sinonasal tumors.Methods One hundred and forty-three patients with sinonasal tumors,including 78 malignant tumors and 65 benign tumors and tumor-like lesions,underwent clinically used DCE-MRI.Parametric maps were obtained for quantitative parameters including Ktrans,kep and ve.Two radiologists reviewed these maps and measured Ktrans,kep and ve in the tumor tissue.Data were analyzed using independent T-test or Mann-Whitney U test analysis and receiver operating characteristic curves.Results Ktrans,kep and ve showed significant differences between benign and malignant tumors in the sinonasal area （P=-0.000 1）.The accuracy of Ktrans,kep and ve in differentiation between benign and malignant sinonasal tumors were 72.0％,76.2％ and 67.1％,respectively.There were significant differences in kep and ve between malignant epithelial sinonasal tumors and lymphomas （P ＜0.05）.Using a ve value of 0.213 as the threshold value differentiated malignant epithelial tumors from lymphomas with an accuracy of 78.3％,sensitivity of 88.2％,specificity of 68.0％,positive predictive value of 66.7％,and negative predictive value of 90.9％.However,no significant difference in Ktrans and kep was found between malignant epithelial and non-epithelial tumors in the sinonasal area （P ＞0.05）.Conclusions It is feasible that quantitative parameters of tumors can be derived from clinically used DCE-MRI in the sinonasal region.Preliminary findings suggest an     

薄层CT与18F-FDG PET/CT联合运用可提高肺孤立性结节定性诊断的准确性

目的 探索如何将病灶SUVmax与薄层CT相结合,提高18F-FDG PET/CT对肺孤立性结节的定性诊断准确性.方法 回顾性分析267例经手术病理检查或临床随访证实的SPN患者的18F-FDG PET/CT及薄层CT显像结果,依据薄层CT将SPN分为实性结节与非实性结节.分别采用标准1 (SUVmax≥2.5)和标准2(SUVmax结合薄层CT综合分析)诊断肺癌,以病理和临床随访为金标准,分析两种标准对肺孤立性结节的诊断效能.结果 采用两种标准诊断肺癌的灵敏度和准确性分别为80.4％、76.4％(标准1)和91.0％、87.2％(标准2)(均P＜0.05).在非实性结节中,良、恶性病灶的SUVmax无明显显著性差异(P＞0.05),而病灶大小和分叶征、含气支气管征或空泡征以及病灶内有无粗大血管等CT征象对鉴别诊断有意义(均P＜0.05).40例PET表现为低代谢的肺癌患者,均被误诊为良性病变,结合薄层CT图像,纠正了其中50％(20/40)的诊断.采用标准1诊断肺癌,灵敏度为40.0％,而采用标准2诊断肺癌,灵敏度为90％,标准2对非实性结节诊断的灵敏度明显高于标准1(P=0.000),但特异性无显著性差异(75.2％vs58.3％,P=0.667).然而,对实性结节,薄层CT对于诊断结果无明显影响(均P＞0.05).结论 对于肺孤立性结节,仅依据SUVmax≥2.5诊断肺癌,诊断效能并不理想.对非实性结节,须依据SUVmax和薄层CT所见进行综合分析.     

计算机辅助孤立性肺结节的良、恶性鉴别诊断

目的探讨良、恶性孤立性肺结节(SPN)增强后的形态学特征,为SPN的影像诊断提供依据。方法采用SPN图像处理专用软件,对23例恶性肿瘤患者和22例良性结节患者的增强前后CT扫描图像进行了分析比较。结果良、恶性结节组的强化与非强化比较结果显示差异有显著性(P<0.0001);良、恶性组的不规则强化比较结果显示差异有显著性(P=0.0084);恶性结节组的强化幅度平均为(45.04±26.76)HU,明显高于良性结节组的(15.70±17.84)HU(P=0.033);恶性结节组的最大强化CT值平均为(136.09±41.72)HU,明显高于良性结节组的(60.60±60.27)HU(P=0.007);恶性结节组的强化面积平均为(21.69±21.01)%,良性结节组的强化面积平均为(8.61±10.83)%,两组相比差异无显著性(P=0.203)。结论增强扫描后的强化幅度和最大强化值,以及在形态学上的不规则强化,可作为SPN临床影像诊断的参考依据。     

孤立性肺结节的影像学特征及两种肺癌预测模型的比较

目的探讨肺部孤立性结节的影像学特征,并比较两种肺癌预测模型对孤立性肺结节（SPN）的诊断价值。方法回顾性分析上海交通大学医学院附属瑞金医院2002年至2009年期间入住呼吸科和胸外科,肺部有孤立性结节且获病理诊断的患者。总结患者的临床特征和胸部CT影像学特征,验证并比较两种肺癌预测模型在评估孤立性肺结节良恶性上的诊断准确性。结果入选病例共90例,其中良性32例,恶性58例。研究结果显示,孤立性肺结节的分叶征对于良恶性的鉴别最有意义,良恶性结节间存在显著差异（P〈0.05）。VA模型ROC曲线下面积（0.712,95%CI 0.606-0.821）;Mayo Clinic模型ROC曲线下面积（0.753,95%CI 0.652-0.843）,其诊断价值优于VA模型。结论分叶征对良恶性结节的鉴别有意义,而综合临床特征和影像学特征的肺癌预测模型更有助于临床医师的鉴别诊断。     

ROC分析在多层螺旋CT综合性诊断孤立性肺结节良恶性中的应用

目的探讨在孤立性肺结节(SPN)良恶性的多层螺旋CT(MSCT)综合性诊断中ROC分析的应用价值。方法经手术和/或穿刺活检组织病理结果证实的SPN 177个,由3位资深的CT诊断医师分别对每个SPN的MSCT图像进行全面分析,依其影像特征进行综合评分,根据SPN的最大径(D),以MSCT评分为分类值,分别绘制D≤3.0 cm、2.0 cm相似文献   

数字化X线胸片计算机辅助检测在肺结节诊断中的应用价值

目的:评价计算机辅助检测(CAD)系统对肺结节诊断的价值。方法:选取120例有肺部单发结节(直径9～30mm)的患者,所有肺结节病灶均经病理证实。另从健康体检人群中随机抽取120例年龄、性别与受检组相仿者作为对照组。两组患者均经CT证实,并由多位放射学专家共同诊断。所有X线胸片均通过数字化放射成像(DR)获得。由5名高年资和5名低年资影像医师作为观测者,分别对不用和用CAD输出图像的X线胸片进行诊断。观察者积分用受试者操作特性(ROC)曲线分析来衡量。结果:平均ROC曲线下面积从不用CAD输出图像的0.762增加至用CAD输出图像的0.825(P<0.05),在使用CAD输出图像时,低年资影像医师比高年资影像医师的平均ROC曲线下面积增加更多。结论:运用数字化X线胸片所配置的CAD系统,能帮助影像诊断医师提高对肺结节性病灶的检出率。     

基于Bayes理论的计算机辅助诊断系统在孤立性肺结节CT诊断中的应用

目的 初步探讨基于Bayes理论的计算机辅助诊断(computer-aided diagnosis, CAD)系统在孤立性肺结节(solitary pulmonary nodule, SPN)CT诊断中的价值.方法 依据Bayes理论先从352例SPN训练集(恶性135例,良性217例)中求出恶性SPN的验前比及各临床和CT表现的似然比,再运用VC 语言编制基于Bayes理论的CAD系统,用它计算每个SPN的恶性概率,并前瞻性地检验该系统在132例SPN测试集(恶性61例,良性71例)中的诊断效能,与2位高年资和2位低年资放射科医师常规阅片的表现作比较.结果 成功构建基于Bayes理论的CAD系统,它诊断训练集SPN的敏感度、特异度和符合率分别为88.9%、93.1%、91.5%,诊断测试集SPN的敏感度、特异度、符合率、阳性预测值及阴性预测值分别为88.5%、85.9%、87.1%、84.4%、89.7%,其诊断符合率与高年资甲、乙医师比较无统计学差异(P>0.05),但高于低年资丙、丁医师(P<0.05).结论 基于Bayes理论的CAD系统可帮助医师尤其是低年资医师提高鉴别SPN良恶性质的能力,并在指导SPN的临床决策中有一定的参考作用.     

数字化放射成像与普通X线诊断肺内单发结节的ROC曲线评价

目的比较数字化放射成像(DR)和普通X线对肺内单发结节的诊断价值。方法选取30例有肺部单发结节和30例无肺内结节的病例,所有肺结节病灶均经CT和病理学证实。分别摄取普通X线胸片和DR,4位高年资医生和4位低年资医生分别对两组影像资料进行评价,结果采用受试者工作特征(ROC)曲线进行统计分析。结果(1)在发现肺内单发结节方面:对高年资医生来说,DR组的ROC曲线下面积(Az=0.838)大于普通胸片组(Az=0.816)(P<0.05);对低年资医生来说,DR组的ROC曲线下面积(Az=0.842)大于普通胸片组(Az=0.712)(P<0.05);(2)在判断肺内单发结节的良恶性方面,两组无显著差异(P=0.272)。结论DR在发现肺内单发结节方面优于普通胸片,而在判断良恶性方面两者差别不大。     

SUV小于2．5的孤立性肺结节18F—FDGPET／CT诊断价值初探

目的正电子断层显像（PET）通常以标准摄取值（SUV）≥2．5为肺癌诊断标准,但仍有部分SUV〈2．5的孤立性肺结节（SPN〈2.5）为肺癌。本研究旨在分析SPN〈2.5,氟代脱氧葡萄糖（18F—FDG）PET／CT表现及其诊断价值。方法51例18F—FDGPET／CT早期显像SUV平均值（SUVavg）〈2．5的孤立性肺结节病人纳入研究。对SPN放射性摄取测量采用两种方法：视觉分析及半定量分析。测量SPN的早期及延迟显像的SUVavg、SUVmax及其早期一延迟变化率（△SUVavg、△SUVmax）。37例以病理诊断及14例以2年以上临床随诊结果为最终诊断标准。统计学分析采用SPSS16．0软件。结果51例SPN〈2.5患者中,良性11例,恶性40例。虽然良恶性SPN〈2.5患者各项SUV值间均无统计学差异（P〉0．05）,但恶性SPN〈2.5,患者各项SUV值均有高于良性的趋势,特别是ASUVavg、ASUVmax。以ASUVmax〉0为诊断阈值,18F—FDGPET／CT诊断恶性SPN的敏感性68．4％、特异性60．0％、准确性66．7％、阳性预测值86．7％及阴性预测值33．3％。视觉分析：无FDG摄取的SPN〈2.5 9例中,5例为良性,4例为恶性;有FDG摄取的SPN〈2.5 42例中,6例良性,36例恶性,两组之间有显著的统计学差异,P=0．02。良恶性SPN〈2.5的CT密度及大小之间均无统计学差异（P〉0．05）。综合PET和CT信息分析SPN〈2.5,18F—FDGPET／CT诊断恶性SPN〈2.5的敏感性97．5％、特异性54．5％、准确性88．2％、阳性预测值88．6％、阴性预测值85．7％。结论单纯以SUV≥2．5为标准鉴别诊断SPN会导致肺癌的漏诊。对于此类结节,ASUV有较高的肺癌阳性预测值;FDG摄取视觉分析能提高结节的良恶性鉴别准确率;综合FDGPET和cT分析的PET／CT能显著提高诊断肺癌的敏感性和准确性,但特异性较低。     

孤立性肺结节MR动态增强的初步研究

目的:探讨MR动态增强对孤立性肺结节的鉴别诊断价值。方法:对47例孤立性肺结节行MR动态增强,绘制MR动态增强时间-信号强度曲线,测量病灶的强化峰值(PH)、强化斜率(F)、最大强化率(Emax)及1、2、3、4、5、6 min时的信号强化率(E1、E2、E3、E4、E5、E6)。结果:恶性结节及炎性结节的强化程度明显高于良性结节,恶性结节及炎性结节的PH、SS、Emax、E1～E6均明显高于良性结节(P<0.01)。恶性结节与炎性结节均强化明显,但二者之间的PH、SS、Emax、E1～E6无明显差异(P>0.05)。结论:MR动态增强能反映病灶的血流动力学信息,对良恶性结节的鉴别诊断有较高的临床应用价值。