The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined "truth"

RATIONALE AND OBJECTIVES: Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish "truth" for algorithm development, training, and testing. The integrity of this "truth," however, must be established before investigators commit to this "gold standard" as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the "truth" collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database. MATERIALS AND METHODS: One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the "blinded read phase"), radiologists independently identified and annotated lesions, assigning each to one of three categories: "nodule >or=3 mm," "nodule <3 mm," or "non-nodule >or=3 mm." For the second read (the "unblinded read phase"), the same radiologists independently evaluated the same CT scans, but with all of the annotations from the previously performed blinded reads presented; each radiologist could add to, edit, or delete their own marks; change the lesion category of their own marks; or leave their marks unchanged. The post-unblinded read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of identification of potential errors introduced during the complete image annotation process and correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional. RESULTS: A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process. CONCLUSIONS: The establishment of "truth" must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems.     

The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans

RATIONALE AND OBJECTIVES: The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on computed tomography (CT) scans and thereby to investigate variability in the establishment of the "truth" against which nodule-based studies are measured. MATERIALS AND METHODS: Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial "blinded read" phase, radiologists independently marked lesions they identified as "nodule >or=3 mm (diameter)," "nodule <3 mm," or "non-nodule >or=3 mm." During the subsequent "unblinded read" phase, the blinded read results of all four radiologists were revealed to each radiologist, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist's own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus. RESULTS: After the initial blinded read phase, 71 lesions received "nodule >or=3 mm" marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. After the unblinded reads, a total of 59 lesions were marked as "nodule >or=3 mm" by at least one radiologist. Twenty-seven (45.8%) of these lesions received such marks from all four radiologists, three (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist. CONCLUSION: The two-phase image annotation process yields improved agreement among radiologists in the interpretation of nodules >or=3 mm. Nevertheless, substantial variability remains across radiologists in the task of lung nodule identification.     

Image annotation for conveying automated lung nodule detection results to radiologists

RATIONALE AND OBJECTIVES: The author investigated the ability of automated techniques to convey the results of an automated lung nodule detection method for human visualization. MATERIALS AND METHODS: Automated nodule detection begins with gray-level thresholding techniques to create a segmented lung volume within which nodule candidates are identified. Morphologic and gray-level features are computed for each candidate. To distinguish between candidates that represent actual nodules and those that represent non-nodules, a rule-based scheme is combined with linear discriminant analysis. For output visualization, final detection results are represented as circles around computer-detected structures in a single section in which each structure appears. Consequently, an inappropriate choice of section could result in an actual nodule detected by the computer but not properly indicated to the radiologist, thus reducing the potential positive impact of that detection on the radiologist's decision-making process. RESULTS: The automated nodule detection method achieved 71% sensitivity with 0.5 false positives per section on 38 CT scans; however, when these results were converted to annotations on the images output for human visualization, only 91% of the computer-detected true-positive nodules received annotations that encompassed a portion of the actual nodule. Thus, the "effective sensitivity" of the automated detection method was reduced. CONCLUSION: The "effective sensitivity" of an automated lung nodule detection system considers the eventual human interaction with system output. Differences between reported computer sensitivity and "effective sensitivity" may be reduced through proper consideration of the assessment of "truth," of the manner in which computer results are scored, and of the complete segmentation of candidates for automated nodule detection.     

An analysis of early studies released by the Lung Imaging Database Consortium (LIDC)

RATIONALE AND OBJECTIVES: To analyze radiologist lung nodule segmentations in the Lung Imaging Database Consortium (LIDC) database and to apply statistical tools to generate estimates of ground truth. This investigation expands on earlier work by considering a larger number of cases from the LIDC database, and results were generated on a per-nodule basis, as opposed to a per-case basis as was done previously. MATERIALS AND METHODS: We analyzed nodule data drawn from the 41 most recent computed tomography exams released by the LIDC. We combined radiologist segmentations for a given nodule using different consensus schemes: union, intersection, and simultaneous truth and performance level estimation (STAPLE). We also generated three-dimensional models of the manual segmentations using discrete marching cubes to visualize features of the data. RESULTS: Using the union as the consensus scheme produced the greatest number of nodule-positive voxels while using the intersection produced the fewest. Considering only nodules for which all readers agreed on nodule presence, STAPLE computed sensitivity averages for readers one, two, three, and four were 0.91, 0.83, 0.90, and 0.77, respectively. Specificity averages were 0.97, 0.98, 0.97, and 0.97. Considering cases for which there was disagreement about nodule presence, sensitivity results become 0.67, 0.74, 0.60, and 0.37. Specificity results in this case are 0.95, 0.95, 0.95, and 0.98. STAPLE-generated pmaps exhibited probability values tightly grouped below the 0.25 and above the 0.75 probability levels. Three-dimensional models of manually segmented nodules revealed step-artefacts in the segmentation data. CONCLUSIONS: Radiologists often disagree about nodule presence. Ideally, knowing each reader's sensitivity and specificity a priori is preferred for optimal STAPLE results. Knowing these values and developing manual segmentation tools and imaging protocols that mitigate unwanted segmentation features (such as step artefacts) can result in more accurate estimates of ground truth. Furthermore, a computer-aided detection algorithm's performance is a function of the ground truth estimate by which it is scored.     

The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements

RATIONALE AND OBJECTIVES: The goal was to investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, because the latter are always qualified with respect to a given size range of nodules. MATERIALS AND METHODS: This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium (LIDC) using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a unidimensional and two bidimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the interradiologist variation, while those with at least one marking were used to examine the difference between the metrics. RESULTS: The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high interobserver variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges for the three-dimensional, unidimensional, and two bidimensional size metrics, respectively (in mm): 0.49-1.25, 0.67-2.55, 0.78-2.11, and 0.96-2.69. Also, a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on unidimensional, and the two bidimensional size measurements of 10 mm were 7.32, 7.72, and 6.29 mm, respectively. CONCLUSIONS: The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets.     

Lung cancer screening with CT: Evaluation of radiologists and different computer assisted detection software (CAD) as first and second readers for lung nodule detection at different dose levels

Objectives

To find the best pairing of first and second reader at highest sensitivity for detecting lung nodules with CT at various dose levels.

Materials and methods

An anthropomorphic lung phantom and artificial lung nodules were used to simulate screening CT-examination at standard dose (100 mAs, 120 kVp) and 8 different low dose levels, using 120, 100 and 80 kVp combined with 100, 50 and 25 mAs. At each dose level 40 phantoms were randomly filled with 75 solid and 25 ground glass nodules (5–12 mm). Two radiologists and 3 different computer aided detection softwares (CAD) were paired to find the highest sensitivity.

Results

Sensitivities at standard dose were 92%, 90%, 84%, 79% and 73% for reader 1, 2, CAD1, CAD2, CAD3, respectively. Combined sensitivity for human readers 1 and 2 improved to 97%, (p1 = 0.063, p2 = 0.016). Highest sensitivities – between 97% and 99.0% – were achieved by combining any radiologist with any CAD at any dose level. Combining any two CADs, sensitivities between 85% and 88% were significantly lower than for radiologists combined with CAD (p < 0.03).

Conclusions

Combination of a human observer with any of the tested CAD systems provide optimal sensitivity for lung nodule detection even at reduced dose at 25 mAs/80 kVp.     

Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program(1)

RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate the performance of a fully automated lung nodule detection method in a large database of low-dose computed tomography (CT) scans from a lung cancer screening program. Because nodules demonstrate a spectrum of radiologic appearances, the performance of the automated method was evaluated on the basis of nodule malignancy status, size, subtlety, and radiographic opacity. MATERIALS AND METHODS: A database of 393 thick-section (10 mm) low-dose CT scans was collected. Automated lung nodule detection proceeds in two phases: gray-level thresholding for the initial identification of nodule candidates, followed by the application of a rule-based classifier and linear discriminant analysis to distinguish between candidates that correspond to actual lung nodules and candidates that correspond to non-nodules. Free-response receiver operating characteristic analysis was used to evaluate the performance of the method based on a jackknife training/testing approach. RESULTS: An overall nodule detection sensitivity of 70% (330 of 470) was attained with an average of 1.6 false-positive detections per section. At the same false-positive rate, 83% (57 of 69) of the malignant lung nodules in the database were detected. When the method was trained specifically for malignant nodules, a sensitivity of 80% (55 of 69) was attained with 0.85 false-positives per section. CONCLUSION: We have evaluated an automated lung nodule detection method with a large number of low-dose CT scans from a lung cancer screening program. An overall sensitivity of 80% for malignant nodules was achieved with 0.85 false-positive detections per section. Such a computerized lung nodule detection method is expected to become an important part of CT-based lung cancer screening programs.     

周围型肺癌阻塞性病变的CT表现(附18例分析)

目的:确定深呼吸时相CT扫描对周围型肺癌诊断敏感性。材料与方法:18例周围型肺癌(13例经病理证实,5例由其它方法证实)和12例良性肺肿块对照者(炎性假瘤和结核球等)均经常规CT扫描,除2例炎性假瘤患者外,其余所有病人并经肿块部位及其上下一层的深呼吸时相CT扫描。结果:周围型肺癌深呼吸时相CT扫描所见的阻塞性病变计有4例肺不张,12例阻塞性肺炎(按其帮位进而可分为远离肿块的和邻近肿块的阻塞性肺炎),1例阻塞性肺气肿,以及1例肺静脉癌栓。周围型肺癌的这些CT表现,在良性肺肿块对照者中不复被看到。结论:深呼吸时相CT扫描所见的远离肿块的阻塞性肺炎及肿块外侧的阻塞性肺气肿,可被看作是周围型肺癌的特有表现而且是其早期诊断的依据。     

Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features

RATIONALE AND OBJECTIVES: To investigate the utility of a computer-aided diagnosis (CAD) in the task of differentiating malignant nodules from benign nodules based on quantitative features extracted from volumetric thin section CT image data acquired before and after the injection of contrast media. MATERIALS AND METHODS: 35 volumetric CT datasets of solitary pulmonary nodules (SPN) with proven diagnoses (19 malignant/16 benign) were contoured by a thoracic radiologist. All studies had at least a baseline series obtained without contrast media and at least one series following an intravenous contrast injection at 45, 90, 180, and 360 seconds. Two separate contours were created for each nodule: one including only the solid portion and another including the ground-glass component, if any, of the nodule. For each contour 31 features were calculated that measured the attenuation, shape, and enhancement of the nodule due to the injection of contrast. These features were input into a feature selection step and three different classifiers to determine if the diagnosis could be predicted from the resulting feature vector. In addition, observer input was introduced to two of the classifiers as an a priori probability of malignancy and the resulting performance was compared. Training and testing was conducted in a resubstitution and leave-one-out fashion and performance was evaluated using ROC analysis. RESULTS: In a leave-one-out testing methodology, the classifiers achieved areas under the ROC curves AZ that ranged from 0.69 to 0.92. A classifier based on logistic regression performed the best with an AZ of 0.92 while a classifier based on quadratic discriminant analysis performed the poorest (AZ, 0.69). The AZ increased when using a priori observer input in most cases reaching a maximum of 0.95. CONCLUSION: Based on this initial work with a limited number of nodules in our dataset, it appears that CAD using volumetric and contrast-enhanced data has the potential to assist radiologists in the task of differentiating solitary pulmonary nodules and in the management of these patients. Further studies with an increased number of patients are required to validate these results.     

Initial Outcomes of a Lung Cancer Screening Program in an Integrated Community Health System

PurposeLung cancer screening with low-dose CT (LDCT) demonstrated reduced mortality in the National Lung Screening Trial, yet there is debate as to whether the reported efficacy can translate into comparable effectiveness with community-based screening. The authors’ purpose is to report the baseline patient characteristics and malignancy rate in the first 18 months after implementing a lung cancer screening program in an integrated community health system.MethodsPatients were screened at 1 of 10 participating community-based centers within a 22-hospital system from 2013 to 2015. LDCT examinations were interpreted by 1 of 20 radiologists using structured reporting and an internally developed tracking system. Manual chart review was performed to ascertain the malignancy detection rate.ResultsA total of 357 patients were screened with LDCT. Of these, 80 patients were ineligible and 3 declined enrollment. The remaining 274 patients satisfied accepted screening criteria and were enrolled in the program. Malignancy was detected in a total of 11 enrollees (4.0%), 8 with lung cancer and 3 with extrapulmonary primary malignancies. Three patients (1.1%) were diagnosed with early-stage lung cancer and received definitive therapy.ConclusionsEarly-stage lung cancer was detected with LDCT screening in an integrated community health system at a rate similar to other trials.     

Computer-aided lung nodule detection in CT: results of large-scale observer test

RATIONALE AND OBJECTIVES: The objective is to study the incremental effects of using a computer-aided lung nodule detection (CAD) system on the performance of a large pool of observers. MATERIALS AND METHODS: A set of eight thin-section computed tomographic data sets with limited longitudinal coverage, containing a total of 22 lung nodules, was analyzed by using the automated nodule detection system. When applied to all eight cases, the CAD system alone achieved a detection rate of 86.4%, with 2.64 false-positive results per case. This study included 202 observers at a national radiology meeting: 39 thoracic radiologists, 95 non-thoracic radiologists, and 68 non-radiologists. Each participant read from one to eight cases in random order, first without and then with CAD system output available. Observer performance in nodule detection was measured before and after CAD was made available. Differences in performance of groups of observers before and after CAD were tabulated by mean, median, and SD in detection rate and number of false-positive results and tested by using nonparametric methods. RESULTS: In an analysis involving only the first randomly selected case read by all 202 participants, there were statistically significant increases in nodule detection rates and numbers of false-positive results for all types of observers. There was a significant difference in detection rates between radiologists and non-radiologists before CAD, but after CAD, there was no significant difference in detection rates between these observer types. In a second analysis involving 13 participants who read all eight cases, mean detection rates were 64.0% before CAD and 81.9% after CAD. Mean numbers of false-positive results were 0.144 per case before CAD and 0.173 after CAD. CONCLUSION: In a large observer study, use of a CAD system for nodule detection resulted in an incremental increase in detection rate, but also led to an increase in number of false-positive results. Also, CAD appears to be an equalizer of detection rates between observers of different levels of experience.     

Role of baseline nodule density and changes in density and nodule features in the discrimination between benign and malignant solid indeterminate pulmonary nodules

Purpose

To retrospectively evaluate whether baseline nodule density or changes in density or nodule features could be used to discriminate between benign and malignant solid indeterminate nodules.

Materials and methods

Solid indeterminate nodules between 50 and 500 mm³ (4.6–9.8 mm) were assessed at 3 and 12 months after baseline lung cancer screening (NELSON study). Nodules were classified based on morphology (spherical or non-spherical), shape (round, polygonal or irregular) and margin (smooth, lobulated, spiculated or irregular). The mean CT density of the nodule was automatically generated in Hounsfield units (HU) by the Lungcare^© software.

Results

From April 2004 to July 2006, 7310 participants underwent baseline screening. In 312 participants 372 solid purely intra-parenchymal nodules were found. Of them, 16 (4%) were malignant. Benign nodules were 82.8 mm³ (5.4 mm) and malignant nodules 274.5 mm³ (8.1 mm) (p = 0.000). Baseline CT density for benign nodules was 42.7 HU and for malignant nodules −2.2 HU (p = ns). The median change in density for benign nodules was −0.1 HU and for malignant nodules 12.8 HU (p < 0.05). Compared to benign nodules, malignant nodules were more often non-spherical, irregular, lobulated or spiculated at baseline, 3-month and 1-year follow-up (p < 0.0001). In the majority of the benign and malignant nodules there was no change in morphology, shape and margin during 1 year of follow-up (p = ns).

Conclusion

Baseline nodule density and changes in nodule features cannot be used to discriminate between benign and malignant solid indeterminate pulmonary nodules, but an increase in density is suggestive for malignancy and requires a shorter follow-up or a biopsy.     

The Lung Reporting and Data System (LU-RADS): A Proposal for Computed Tomography Screening

Despite the positive outcome of the recent randomized trial of computed tomography (CT) screening for lung cancer, substantial implementation challenges remain, including the clear reporting of relative risk and suggested workup of screen-detected nodules. Based on current literature, we propose a 6-level Lung-Reporting and Data System (LU-RADS) that classifies screening CTs by the nodule with the highest malignancy risk. As the LU-RADS level increases, the risk of malignancy increases. The LU-RADS level is linked directly to suggested follow-up pathways. Compared with current narrative reporting, this structure should improve communication with patients and clinicians, and provide a data collection framework to facilitate screening program evaluation and radiologist training. In overview, category 1 includes CTs with no nodules and returns the subject to routine screening. Category 2 scans harbor minimal risk, including <5 mm, perifissural, or long-term stable nodules that require no further workup before the next routine screening CT. Category 3 scans contain indeterminate nodules and require CT follow up with the interval dependent on nodule size (small [5-9 mm] or large [≥10 mm] and possibly transient). Category 4 scans are suspicious and are subdivided into 4A, low risk of malignancy; 4B, likely low-grade adenocarcinoma; and 4C, likely malignant. The 4B and 4C nodules have a high likelihood of neoplasm simply based on screening CT features, even if positron emission tomography, needle biopsy, and/or bronchoscopy are negative. Category 5 nodules demonstrate frankly malignant behavior on screening CT, and category 6 scans contain tissue-proven malignancies.     

Usefulness of computerized method for lung nodule detection on digital chest radiographs using similar subtraction images from different patients

Purpose

The purpose of this study is to evaluate the usefulness of a novel computerized method to select automatically the similar chest radiograph for image subtraction in the patients who have no previous chest radiographs and to assist the radiologists’ interpretation by presenting the “similar subtraction image” from different patients.

Materials and methods

Institutional review board approval was obtained, and the requirement for informed patient consent was waived. A large database of approximately 15,000 normal chest radiographs was used for searching similar images of different patients. One hundred images of candidates were selected according to two clinical parameters and similarity of the lung field in the target image. We used the correlation value of chest region in the 100 images for searching the most similar image. The similar subtraction images were obtained by subtracting the similar image selected from the target image. Thirty cases with lung nodules and 30 cases without lung nodules were used for an observer performance test. Four attending radiologists and four radiology residents participated in this observer performance test.

Results

The AUC for all radiologists increased significantly from 0.925 to 0.974 with the CAD (P = .004). When the computer output images were available, the average AUC for the residents was more improved (0.960 vs. 0.890) than for the attending radiologists (0.987 vs. 0.960).

Conclusion

The novel computerized method for lung nodule detection using similar subtraction images from different patients would be useful to detect lung nodules on digital chest radiographs, especially for less experienced readers.     

CsI-detector-based dual-exposure dual energy in chest radiography for lung nodule detection: results of an international multicenter trial

To assess both sensitivity and specificity of digital chest radiography alone and in conjunction with dual-exposure dual-energy chest radiography for the detection and classification of pulmonary nodules. One hundred patients with a total of 149 lung nodules (3-45 mm; median, 11 mm) confirmed by CT were included in this study. Dual-exposure dual-energy chest radiographies of each patient were obtained using a CsI detector system. Experienced board-certified chest radiologists from four different medical centers in Europe reviewed standard chest radiographs alone and in conjunction with dual-energy images blinded and in random order. The reviewers rated the probability of presence, calcification and malignancy of all lung nodules on a five-point rating scale. Lesions detected were identified by applying a specific coordinate system to enable precise verification by the study leader. A receiver-operating characteristic (ROC) analysis was performed. In addition to the 149 true-positive CT proven lesions, 236 false-positive lung nodules were described in digital chest radiographies in conjunction with dual-energy chest radiographies. The cumulative sensitivity of chest radiography in conjunction with dual energy was 43%, specificity was 55%. For digital radiography alone, sensitivity was 35% and specifity was 83%. For the dual energy system, positive predictive value was 58%, and negative predictive value was 66% compared to the digital radiography with a positive predictive value of 59% and a negative predictive value of 65%. Areas under the curve in a ROC analysis resulted in 0.631 (95% confidence interval =0.61 to 0.65) for radiography with dual energy and 0.602 (95% confidence interval =0.58 to 0.63) for digital radiography alone. This difference was not statistically significant. For the detection of lesion calcification or the determination of malignancy, ROC analysis also failed to show significant differences. CsI-based flat-panel dual-exposure dual-energy imaging added to standard chest radiography did not show statistically significant improvement for the detection of pulmonary nodules, nor the identification of calcifications, nor the determination of malignancy.     

Potential contribution of multiplanar reconstruction (MPR) to computer-aided detection of lung nodules on MDCT

Purpose

To evaluate potential benefits of using multiplanar reconstruction (MPR) in computer-aided detection (CAD) of lung nodules on multidetector computed tomography (MDCT).

Materials and methods

MDCT datasets of 60 patients with suspected lung nodules were retrospectively collected. Using “second-read” CAD, two radiologists (Readers 1 and 2) independently interpreted these datasets for the detection of non-calcified nodules (≥4 mm) with concomitant confidence rating. They did this task twice, first without MPR (using only axial images), and then 4 weeks later with MPR (using also coronal and sagittal MPR images), where the total reading time per dataset, including the time taken to assess the detection results of CAD software (CAD assessment time), was recorded. The total reading time and CAD assessment time without MPR and those with MPR were statistically compared for each reader. The radiologists’ performance for detecting nodules without MPR and the performance with MPR were compared using jackknife free-response receiver operating characteristic (JAFROC) analysis.

Results

Compared to the CAD assessment time without MPR (mean, 69 s and 57 s for Readers 1 and 2), the CAD assessment time with MPR (mean, 46 s and 45 s for Readers 1 and 2) was significantly reduced (P < 0.001). For Reader 1, the total reading time was also significantly shorter in the case with MPR. There was no significant difference between the detection performances without MPR and with MPR.

Conclusion

The use of MPR has the potential to improve the workflow in CAD of lung nodules on MDCT.     

原发性囊腔型肺癌的CT诊断13例诊断分析

目的探讨原发性囊腔型肺癌的CT表现,以提高对本病的诊断准确率。方法回顾性分析经病理证实的13例原发性囊腔型肺癌的CT表现。结果13例囊腔型肺癌均为单发病例,左肺5例,右肺8例,其中10例腺癌,2例鳞癌,1例小细胞肺癌。病灶边缘分叶、毛刺分别占61.5%、76.9%;囊内壁毛糙、厚薄不均分别占84.6%、92.3%;囊内分隔占61.5%;囊腔内壁结节占53.8%;病变周围胸膜凹陷和血管集束分别占69.2%、84.6%。8例增强检查病例中6例轻度强化,2例中度强化。动态随访病例中,1例壁结节增大,1例囊腔增大,2例囊腔及实性成分均增大。结论囊腔内壁毛糙,囊内分隔及壁结节是有助于囊腔型肺癌诊断的特征性征象,有助于其鉴别诊断。