Lung nodule detection in low-dose and thin-slice computed tomography

A computer-aided detection (CAD) system for the identification of small pulmonary nodules in low-dose and thin-slice CT scans has been developed. The automated procedure for selecting the nodule candidates is mainly based on a filter enhancing spherical-shaped objects. A neural approach based on the classification of each single voxel of a nodule candidate has been purposely developed and implemented to reduce the amount of false-positive findings per scan. The CAD system has been trained to be sensitive to small internal and sub-pleural pulmonary nodules collected in a database of low-dose and thin-slice CT scans. The system performance has been evaluated on a data set of 39 CT containing 75 internal and 27 sub-pleural nodules. The FROC curve obtained on this data set shows high values of sensitivity to lung nodules (80-85% range) at an acceptable level of false positive findings per patient (10-13 FP/scan).     

针对肺结节检测的肺实质CT图像分割

目的：针对CT图像上肺结节的自动检测，开发并评价对全肺螺旋CT扫描中的肺实质进行自动分割的一种综合方法。方法：首先利用全局阈值对CT图像进行二值化，然后消除由于支气管、细支气管等低密度影和由于结节、血管等高密度影以及由检查床造成的条状伪影等噪声，最后对包含胸膜连接结节的图像利用数学形态学运算和图像凸包运算进行完善形成肺实质掩膜。结果：利用该方法对从LIDC数据库中所有包含结节的505张CT扫描片（来自69个病例）进行肺实质分割，正确率为95．4％。其中，包含胸膜连接结节的139张CT扫描片的正确分割率为94．2％。结论：本文提出的方法较好地完成了肺实质分割任务，为利用CT图像进行计算机辅助肺结节的检测打下了基础。     

Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system

We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified by a k-means clustering technique. Each lung slice is classified as belonging to the upper, middle, or the lower part of the lung volume. Within each lung region, structures are segmented again using weighted k-means clustering. These structures may include true lung nodules and normal structures consisting mainly of blood vessels. Rule-based classifiers are designed to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We performed a preliminary study using 1454 CT slices from 34 patients with 63 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (53/63) with 5.48 (7961/1454) FP objects per slice. When rule-based classification was used before LDA, the free response receiver operating characteristic (FROC) curve improved over the entire sensitivity and specificity ranges of interest. In particular, the FP rate decreased to 1.74 (2530/1454) objects per slice at the same sensitivity. Thus, compared to FP reduction with LDA alone, the inclusion of rule-based classification lead to an improvement in detection accuracy for the CAD system. These preliminary results demonstrate the feasibility of our approach to lung nodule detection and FP reduction on CT images.     

A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.     

Pleural nodule identification in low-dose and thin-slice lung computed tomography

A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (CT) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by 12 morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a dataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level 1 (nodules annotated by only one radiologist) with six FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists).     

Automated lung nodule classification following automated nodule detection on CT: a serial approach

We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the "detection task" and the "classification task." Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation.     

Digital tomosynthesis of the chest for lung nodule detection: interim sensitivity results from an ongoing NIH-sponsored trial

The authors report interim clinical results from an ongoing NIH-sponsored trial to evaluate digital chest tomosynthesis for improving detectability of small lung nodules. Twenty-one patients undergoing computed tomography (CT) to follow up lung nodules were consented and enrolled to receive an additional digital PA chest radiograph and digital tomosynthesis exam. Tomosynthesis was performed with a commercial CsI/a-Si flat-panel detector and a custom-built tube mover. Seventy-one images were acquired in 11 s, reconstructed with the matrix inversion tomosynthesis algorithm at 5-mm plane spacing, and then averaged (seven planes) to reduce noise and low-contrast artifacts. Total exposure for tomosynthesis imaging was equivalent to that of 11 digital PA radiographs (comparable to a typical screen-film lateral radiograph or two digital lateral radiographs). CT scans (1.25-mm section thickness) were reviewed to confirm presence and location of nodules. Three chest radiologists independently reviewed tomosynthesis images and PA chest radiographs to confirm visualization of nodules identified by CT. Nodules were scored as: definitely visible, uncertain, or not visible. 175 nodules (diameter range 3.5-25.5 mm) were seen by CT and grouped according to size: < 5, 5-10, and > 10 mm. When considering as true positives only nodules that were scored definitely visible, sensitivities for all nodules by tomosynthesis and PA radiography were 70% (+/- 5%) and 22% (+/- 4%), respectively, (p < 0.0001). Digital tomosynthesis showed significantly improved sensitivity of detection of known small lung nodules in all three size groups, when compared to PA chest radiography.     

Automated detection of lung nodules in CT scans: preliminary results

We have developed a fully automated computerized method for the detection of lung nodules in helical computed tomography (CT) scans of the thorax. This method is based on two-dimensional and three-dimensional analyses of the image data acquired during diagnostic CT scans. Lung segmentation proceeds on a section-by-section basis to construct a segmented lung volume within which further analysis is performed. Multiple gray-level thresholds are applied to the segmented lung volume to create a series of thresholded lung volumes. An 18-point connectivity scheme is used to identify contiguous three-dimensional structures within each thresholded lung volume, and those structures that satisfy a volume criterion are selected as initial lung nodule candidates. Morphological and gray-level features are computed for each nodule candidate. After a rule-based approach is applied to greatly reduce the number of nodule candidates that corresponds to nonnodules, the features of remaining candidates are merged through linear discriminant analysis. The automated method was applied to a database of 43 diagnostic thoracic CT scans. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate nodule candidates that correspond to actual nodules from false-positive candidates. The area under the ROC curve for this categorization task attained a value of 0.90 during leave-one-out-by-case evaluation. The automated method yielded an overall nodule detection sensitivity of 70% with an average of 1.5 false-positive detections per section when applied to the complete 43-case database. A corresponding nodule detection sensitivity of 89% with an average of 1.3 false-positive detections per section was achieved with a subset of 20 cases that contained only one or two nodules per case.     

Registration of lung nodules using a semi-rigid model: method and preliminary results

The tracking of lung nodules across computed tomography (CT) scans acquired at different times for the same patient is helpful for the determination of malignancy. We are developing a nodule registration system to facilitate this process. We propose to use a semi-rigid method that considers principal structures surrounding the nodule and allows relative movements among the structures. The proposed similarity metric, which evaluates both the image correlation and the degree of elastic deformation amongst the structures, is maximized by a two-layered optimization method, employing a simulated annealing framework. We tested our method by simulating five cases that represent physiological deformation as well as different nodule shape/size changes with time. Each case is made up of a source and target scan, where the source scan consists of a nodule-free patient CT volume into which we inserted ten simulated lung nodules, and the target scan is the result of applying a known, physiologically based nonrigid transformation to the nodule-free source scan, into which we inserted modified versions of the corresponding nodules at the same, known locations. Five different modification strategies were used, one for each of the five cases: (1) nodules maintain size and shape, (2) nodules disappear, (3) nodules shrink uniformly by a factor of 2, (4) nodules grow uniformly by a factor of 2, and (5) nodules grow nonuniformly. We also matched 97 real nodules in pairs of scans (acquired at different times) from 12 patients and compared our registration to a radiologist's visual determination. In the simulation experiments, the mean absolute registration errors were 1.0+/-0.8 mm (s.d.), 1.1+/-0.7 mm (s.d.), 1.0+/-0.7 mm (s.d.), 1.0+/-0.6 mm (s.d.), and 1.1+/- 0.9 mm (s.d.) for the five cases, respectively. For the 97 nodule pairs in 12 patient scans, the mean absolute registration error was 1.4+/-0.8 mm (s.d.).     

基于尺寸自适应深度神经网络的胸部CT图像肺结节检测

使用计算机断层扫描(CT)筛查肺结节是早期肺癌诊断的重要手段.但由于肺结节在形状、大小和位置上有存在很大的差异,目前肺结节尤其是小结节的自动检测依然具有挑战性.为了实现高灵敏度的肺结节检测,提出一种新的计算机辅助检测系统,该系统采用两种新的策略:尺寸自适应候选检测(SACD)和尺寸自适应假阳性抑制(SAFPR).首先,...     

Phased searching with NEAT in a Time-Scaled Framework: Experiments on a computer-aided detection system for lung nodules

ObjectiveIn the field of computer-aided detection (CAD) systems for lung nodules in computed tomography (CT) scans, many image features are presented and many artificial neural network (ANN) classifiers with various structural topologies are analyzed; frequently, the classifier topologies are selected by trial-and-error experiments. To avoid these trial and error approaches, we present a novel classifier that evolves ANNs using genetic algorithms, called “Phased Searching with NEAT in a Time or Generation-Scaled Framework”, integrating feature selection with the classification task.Methods and materialsWe analyzed our method's performance on 360 CT scans from the public Lung Image Database Consortium database. We compare our method's performance with other more-established classifiers, namely regular NEAT, Feature-Deselective NEAT (FD-NEAT), fixed-topology ANNs, and support vector machines (SVMs) using ten-fold cross-validation experiments of all 360 scans.ResultsThe results show that the proposed “Phased Searching” method performs better and faster than regular NEAT, better than FD-NEAT, and achieves sensitivities at 3 and 4 false positives (FP) per scan that are comparable with the fixed-topology ANN and SVM classifiers, but with fewer input features. It achieves a detection sensitivity of 83.0 ± 9.7% with an average of 4 FP/scan, for nodules with a diameter greater than or equal to 3 mm. It also evolves networks with shorter evolution times and with lower complexities than regular NEAT (p = 0.026 and p < 0.001, respectively). Analysis on the average and best network complexities evolved by regular NEAT and by our approach shows that our approach searches for good solutions in lower dimensional search spaces, and evolves networks without superfluous structure.ConclusionsWe have presented a novel approach that combines feature selection with the evolution of ANN topology and weights. Compared with the original threshold-based Phased Searching method of Green, our method requires fewer parameters and converges to the optimal network complexity required for the classification task at hand. The results of the ten-fold cross-validation experiments also show that our proposed CAD system for lung nodule detection performs well with respect to other methods in the literature.     

Investigating the low-dose limits of multidetector CT in lung nodule surveillance

The purpose of this study was to evaluate the factors limiting nodule detection in thoracic computed tomography (CT) and to determine whether prior knowledge of nodule size and attenuation, available from a baseline CT study, influences the minimum radiation dose at which nodule surveillance CT scans can be performed while maintaining current levels of nodule detectability. Multiple nodules varying in attenuation (-509 to + 110 HU) and diameter (1.6 to 9.5 mm) were layered in random and ordered sequences within 2 lung cylinders made of Rando lung material and suspended within a custom-built CT phantom. Multiple CT scans were performed at varying kVp (120, 100, and 80), mA (200, 150, 100, 50, 20, and 10), and beam collimation (5, 2.5, and 1.25 mm) on a four-row multidetector scanner (Lightspeed, General Electric, Milwaukee, WI) using 0.8 s gantry rotation. The corresponding range of radiation dose over which images were acquired was 0.3-26.4 mGy. Nine observers independently performed three specific tasks, namely: (1) To detect a 3.2 mm nodule of 23 HU; (2) To detect 3.2 mm nodules of varying attenuation (-509 to -154 HU); and (3) To detect nodules varying in size (1.6-9 mm) and attenuation (-509 to 110 HU). A two-alternative forced-choice test was used in order to determine the limits of nodule detection in terms of the proportion of correct responses (Pcorr, related to the area under the ROC curve) as a summary metric of observer performance. The radiation dose levels for detection of 99% of nodules in each task were as follows: Task 1 (1 mGy); Task 2 (5 mGy); and Task 3 (7 mGy). The corresponding interobserver confidence limits were 1, 5, and 10 mGy for Tasks 1, 2, and 3, respectively. There was a fivefold increase in the radiation dose required for detection of lower-density nodules (Tasks 1 to 2). Absence of prior knowledge of the nodule size and density (Task 3) corresponds to a significant increase in the minimum required radiation dose. Significant image degradation and reduction in observer performance for all tasks occur at a dose of < or = 1 mGy. It is concluded that the size and attenuation of a nodule strongly influence the radiation dose required for confident evaluation with a minimum threshold value of 1-2 mGy (minimum dose CT). A prior knowledge of nodule size and attenuation is available from the baseline CT scan and is an important consideration in minimizing the radiation exposure required for nodule detection with surveillance CT.     

人工智能辅助胸部低剂量 CT 在肺部结节良、恶性诊断中的准确率研究

目的? 探讨人工智能辅助胸部低剂量 CT 在肺部结节良、恶性诊断中的应用。方法? 选取 2020 年 3 月至 2021 年 7 月在新乡同盟医院行胸部低剂量 CT 检查患者 83 例,根据阅片方式不同分为人工阅片组和人工智能辅助阅片组,观察两组肺部结节的诊断结果。结果? 人工智能辅助阅片诊断肺部结节的阳性率为 86.75%,高于人工阅片的阳性率 68.67%,差异具有统计学意义（x 2 =6.549,P=0.013）;Kappa 检验两种阅片方式的一致性较弱（Kappa 值 = 0.196）,P ＞ 0.05;人工智能辅助阅片对3～7mm直径肺部结节的检出率为93.44%,明显高于人工阅片的检出率85.25%,差异具有统计学意义（P＜0.05）,而两种阅片方式对 0～3mm、7～20mm 直径肺部结节的检出率差异无统计学意义（P ＞ 0.05）;以病理结果为“金标准”绘制 ROC 曲线,结果显示,人工阅片、人工智能辅助阅片诊断恶性肺部结节的 AUC 分别为 0.742（95%CI：0.514,0.921）、0.830（95%CI：0.701,1.00）,且两种阅片方式的特异度、灵敏度、阳性预测值、阴性预测值差异均无统计学意义（P ＞ 0.05）。结论? 人工智能辅助胸部低剂量 CT 能提高肺部结节良、恶性诊断的准确率,并能提高 3～7mm 直径肺部结节的检出率,但与人工阅片对肺部结节诊断的特异度、灵敏度、阳性预测值、阴性预测值基本一致。     

18 F-FDG PET/CT显像联合血清肿瘤标志物检测在肺单发结节诊断中的价值

目的 探讨18F-FDG PET/CT显像和血清肿瘤标志物(CEA、NSE、CFRA21-1)检测联合在肺单发结节诊断中的价值.方法 91例经病理确诊的肺单发结节患者进行18F-FDG PET/CT显像,同时取静脉血4mL分离取血清在低温冰箱保存,用放免法集中测量血肿瘤标志物CEA、NSE、CFRA21-1.结果 18 F-FDG PET/CT检查对肺单发结节恶性肿瘤检出的灵敏度为84.8%(56/66),对肺单发结节诊断的准确率为86.8%(79/91);18 F-FDG PET/CT显像联合血肿瘤标志物(CEA+NSE+CFRA21-1)检测对肺单发结节恶性肿瘤检出的灵敏度为93.9%(62/66),对肺单发结节诊断的准确率为95.6%(87/91),和18F-FDG PET/CT检查相比,灵敏度和准确率差异具有统计学意义(P均﹤0.05).结论 18 F-FDG PET/CT显像和血肿瘤标志物(CEA、NSE、CFRA21-1)检测联合能提高对肺单发结节诊断的灵敏度和准确性,有较高的临床应用价值.     

基于三维密集网络的肺结节检测方法

针对传统基于三维特征的肺结节检测方法存在小结节检出率不高且计算量大的问题,提出一种更为高效的基于三维密集网络的肺结节检测方法。首先将密集连接单元引入3D U-Net,构建适用于肺结节检测的3D Dense U-Net网络;由于3D Dense U-Net用密集连接块代替原始3D U-Net的普通卷积层,可最大化地保证层与层之间的信息流通,不仅能解决传统堆叠式网络所存在的特征冗余问题,而且能加快网络训练速度。同时,该网络保留U-Net的基本连接方式,以实现底层特征的复用,从而可以有效地获取候选结节。在此基础上,针对候选结节中包含假阳例的问题,为了更加有效地获取结节特征,提高网络对结节的鉴别能力,构造三维密集分类网络(3D DenseNet)进行假阳例的剔除。在天池医疗AI大赛数据集的测试中,检测肺结节总体敏感度94.3%,10 mm以下结节敏感度91.5%,假阳例率5.9%。 所提出的基于三维密集网络的肺结节检测方法对于小结节的检测更加灵敏,不仅能提高结节检出率,而且计算效率也有所提高。     

多层螺旋CT低剂量扫描鉴别诊断肺部小结节的价值

目的 探究多层螺旋CT低剂量扫描在肺部小结节鉴别诊断中的应用价值。方法 选取2016年2月~2018年10月我院收治的100例肺部小结节患者,所有患者均行多层螺旋CT低剂量扫描,分析多层螺旋CT低剂量扫描的灵敏度、特异度及准确度、对比良、恶性结节影像学特征及不同病变类型的CT值。结果 多层螺旋CT低剂量扫描在诊断肺部小结节中灵敏度为92.86%、特异度为88.64%、准确度为91.00%;相较于恶性结节,良性结节边缘清晰度、内部钙化检出率较高,边缘分叶状或不规则状、毛刺征,内部结构均匀检出率较低,差异有统计学意义（P＜0.05）;卫星灶在良性与恶性结节中的检出率比较,差异无统计学意义（P＞0.05）;良性结节30 s、90 s及180 s CT值均小于恶性结节,差异有统计学意义（P＜0.05）。结论 多层螺旋CT低剂量扫描在肺部小结节鉴别中具有较高的应用价值,可有效鉴别结节性质,且检查中辐射剂量较低,应用安全性高。     

Early Identification of Lung Fungal Infections in Chronic Granulomatous Disease (CGD) Using Multidetector Computer Tomography

Purpose

The purpose of this study is to evaluate the possibility of early detection of pulmonary fungal infections by lung CT scan in chronic granulomatous disease (CGD).

Methods

A retrospective study on 14 patients affected with CGD for a total of 18 infectious episodes was performed. Revision of clinical data and CT scan analysis before and after treatment was performed.

Results

The presence of lung nodules <30 mm was evaluated in 18 infectious episodes in 14 patients. A total of 125 nodules in 18 CT scans were identified. Identification of the infectious agent through biopsy and in vitro culture resulted positive only in 3/18 cases. The remaining cases received clinical/radiologic diagnosis of suspected pulmonary fungal infection. In all cases, the introduction of empirical antifungal treatment resulted in reduction in size or complete resolution of the pulmonary lung nodules in all patients affected with CGD.

Conclusions

Lung CT scan allows for early detection of pulmonary fungal infection in CGD. Pulmonary nodules (<30 mm), single or multiple, uni- or bilateral, with or without a halo sign may represent the first radiologic sign of pulmonary fungal infection in CGD.

     

Automatic polyp detection and measurement with computed tomographic colonography: A phantom study

Purpose

The purpose of this study is to assess the performance of computer-aided detection (CAD) software in detecting and measuring polyps for CT Colonography, based on an in vitro phantom study.

Material and methods

A colon phantom was constructed with a PVC pipe of 3.8 cm diameter. Nine simulated polyps of various sizes (3.2mm-25.4mm) were affixed inside the phantom that was placed in a water bath. The phantom was scanned on a 64-slice CT scanner with tube voltage of 120 kV and current of 205 mAs. Two separate scans were performed, with different slice thickness and reconstruction interval. The first scan (thin) had a slice thickness of 1mm and reconstruction interval 0.5mm. The second scan (thick) had a slice thickness of 2mm and reconstruction interval of 1mm. Images from both scans were processed using CT Colonography software that automatically segments the colon phantom and applies CAD that automatically highlights and provides the size (maximum and minimum diameters, volume) of each polyp. Two readers independently measured each polyp (two orthogonal diameters) using both 2D and 3D views. Readers’ manual measurements (diameters) and automatic measurements from CAD (diameters and volume) were compared to actual polyp sizes as measured by mechanical calipers.

Results

All polyps except the smallest (3.2mm) were detected by CAD. CAD achieved 100% sensitivity in detecting polyps ≥6mm. Mean errors in CAD automated volume measurements for thin and thick slice scans were 8.7% and 6.8%, respectively. Almost all CAD and manual readers’ 3D measurements overestimated the size of polyps to variable extent. Both over- and underestimation of polyp sizes were observed in the readers’ manual 2D measurements. Overall, Reader 1 (expert) had smaller mean error than Reader 2 (non-expert).

Conclusion

CAD provided accurate size measurements for all polyps, and results were comparable to the two readers'' manual measurements     

多层螺旋CT增强扫描鉴别肺癌的价值

目的 探讨多层螺旋CT增强扫描在肺癌鉴别诊断的价值。方法 选取2017年12月~2018年12月我院收治的90例肺部占位疾病患者,均行多层螺旋CT平扫、增强扫描,比较不同病理类型肺内病灶的平扫/增强扫描的CT值及强化增值、不同肺癌病理类型CT净增强值,及多层螺旋CT平扫、增强扫描诊断肺癌的准确性、敏感性及特异性。结果 肺癌、肺结核球、炎性假瘤平扫CT值比较,差异无统计意义（P＞0.05）;肺癌增强扫描CT值高于肺结核球,低于炎性假瘤,差异有统计学意义（P＜0.05）;肺癌强化增值高于肺结核球,低于炎性假瘤（P＜0.05）。鳞癌CT净增强值高于腺癌、小细胞癌,差异有统计学意义（P＜0.05）;腺癌CT净增强值高于小细胞癌,差异有统计学意义（P＜0.05）。增强扫描诊断肺癌的准确性为88.89%、敏感性为92.31%、特异性为84.21%,均高于平扫的67.78%、69.23%、65.79%,差异有统计学意义（P＜0.05）。结论 多层螺旋CT增强扫描可以帮助鉴别肺部占位疾病及不同病理类型肺癌,且诊断的准确性、敏感性及特异性均较高。