计算机辅助检测肺结节在数字化X线胸片肺癌筛查中的应用价值

目的 评价计算机辅助检测(CAD)肺结节系统在数字化X线胸片上肺癌筛查中的应用价值.方法 由1名放射科医师和CAD肺结节检测系统独立阅读100例连续的数字摄影(DR)X线胸片,CAD系统可以检出最长直径在5～15 mm的肺结节.由2名放射科专家(有15年胸部影像诊断经验)进行回顾性阅读,参照相应的CT图像,两人意见达成一致后标记真结节的个数和位置并保存标记结果,将标记结果作为金标准来比较放射科医师和CAD系统的肺结节检测敏感性和假阳性率.结果 放射科医师共检测到95个结节,CAD系统共检测到304个结节.在回顾性检查中2名放射科专家共标记134个真结节,其中放射科医师检测到82个(61.2%),CAD检测到105个(78.4%),CAD系统检测到而被放射科医师漏诊的结节35个,放射科医师检测到而CAD系统漏诊的结节10个.放射科医师应用CAD系统后检测到112个真结节,检测率提高到83.6%.放射科专家意见一致后认为CAD系统检出199个假阳性结节,平均每张胸片约2.0个.结论 在肺癌筛查中放射科医师和CAD系统必须联合应用才可以识别X线胸片中所有的结节.     

Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system

OBJECTIVE: The aim of this study was to evaluate the usefulness of a new commercially available computer-aided diagnosis (CAD) system with an automated method of detecting nodules due to lung cancers on chest radiograph. MATERIALS AND METHODS: For patients with cancer, 45 cases with solitary lung nodules up to 25 mm in diameter (nodule size range, 8-25 mm in diameter; mean, 18 mm; median, 20 mm) were used. For healthy patients, 45 cases were selected on the basis of confirmation on chest CT. All chest radiographs were obtained with a computed radiography system. The CAD output images were produced with a newly developed CAD system, which consisted of an image server including CAD software called EpiSight/XR. Eight radiologists (four board-certified radiologists and four radiology residents) participated in observer performance studies and interpreted both the original radiographs and CAD output images using a sequential testing method. The observers' performance was evaluated with receiver operating characteristic analysis. RESULTS: The average area under the curve value increased significantly from 0.924 without to 0.986 with CAD output images. Individually, the use of CAD output images was more beneficial to radiology residents than to board-certified radiologists. CONCLUSION: This CAD system for digital chest radiographs can assist radiologists and has the potential to improve the detection of lung nodules due to lung cancer.     

Clinical results of CsI-detector-based dual-exposure dual energy in chest radiography

The aim of this study was to compare the sensitivity and specificity of digital chest radiography alone with digital chest radiography combined with dual-energy chest radiography in the detection of small non-calcified pulmonary nodules. Standard and dual-energy radiographs were obtained with a flat-panel digital chest system. Four radiologists reviewed digital posteroanterior chest radiographs in random order either alone or in conjunction with dual-energy soft tissue and bone images. Twenty patients with a total of 59 pulmonary nodules (median 0.5 cm, range 0.3 – 2.5 cm) confirmed by computed tomography (HU 100) were included. A level of confidence for each diagnosis was documented using a rating scale of 1–5. Brunner and Langer's test was performed for statistical analysis. Subgroup analysis was performed for nodules greater than 1 cm, 1–0.5 cm, and <0.5 cm. For posteroanterior chest radiography, sensitivity was 33%, positive predictive value 83%, specificity 81%, and negative predictive value 30%. Review in conjunction with dual-energy images resulted in a sensitivity of 42%, positive predictive value 88%, specificity 85%, and negative predictive value 34%. The increase of nodule detection overall as well as for different size categories was significant (p<0.05). The increase of the confidence level rating was also significant (p<0.001). Dual energy added to standard posteroanterior chest radiography significantly improves the sensitivity, specificity, and confidence in detection of small non-calcified pulmonary nodules.     

Evaluation of a real-time interactive pulmonary nodule analysis system on chest digital radiographic images: a prospective study

RATIONALE AND OBJECTIVES: We sought to assess the performance of a real-time interactive pulmonary nodule analysis system for evaluation of chest digital radiographic (DR) images in a routine clinical environment. MATERIALS AND METHODS: A real-time interactive pulmonary nodule analysis system for chest DR image softcopy reading (IQQA-Chest; EDDA Technology, Princeton Junction, NJ) was used in daily practice with a Picture Archiving and Communication System in a National Cancer Institute-designated cancer teaching hospital. Patients referred for follow-up of known cancer underwent digital chest radiography. Posteroanterior and lateral DR images were first read by resident radiologists along with experienced chest radiologists using a Picture Archiving and Communication System work station. The computer-assisted detection (CAD) program was subsequently applied to the posteroanterior DR images, and changes (if any) in diagnosis were recorded. For reference standard, a follow-up chest radiograph at least 6 months following the initial examination or a follow-up computed tomographic scan of the chest within 3 months was used to establish diagnostic accuracy. RESULTS: Of 324 DR examinations, follow-up imaging according to our parameters was available for 214 patients (67%). Lung nodules were found and subsequently confirmed in 35 patients (10%) without CAD. Using CAD, nodules were found and subsequently confirmed in 51 patients (15%), improving sensitivity from 63.8% (95% confidence interval [CI], 0.49%-0.76%) to 92.7% (95% CI, 0.82%-0.98%) (P < .0001, McNemar). Nodules were subsequently proved to be malignant in five of the 16 additional cases (31%). False-positive readings increased from three to six cases; specificity decreased from 98.1% (95% CI, 0.95%-0.99%) to 96.2% (95% CI, 0.92%-0.98%) (not significant). There were 153 true-negative cases (71.4%). CONCLUSIONS: This study suggests that the interpretation of chest radiographs for lung nodules can be improved using an automated CAD nodule detection system. This improvement in reader performance comes with a minimal number of false-positive interpretations.     

Subtle lung nodules: influence of local anatomic variations on detection

PURPOSE: To assess the influence of local anatomic noise on the detection of subtle lung nodules depicted on chest radiographs. MATERIALS AND METHODS: Six 8 x 8-cm lung regions were extracted from digital chest radiographs obtained in healthy subjects. Simulated nodules emulating the radiographic characteristics of subtle tissue-equivalent lesions 3.2-6.4 mm in diameter (equivalent to 0.1-0.4 mm in contrast-diameter product [CD]) were added to the images. On multiple renditions of each image, nodules were inserted at slightly different locations within 6 mm of the center; this process allowed different local background patterns to overlie the nodules. An observer detection study involving 15 experienced radiologists was performed. The authors performed analysis of variance and pairwise t test analyses to determine variations in nodule detectability related to nodule location and size on each image. RESULTS: Results indicated a strong correlation between nodule size and observer detection score and significant variation in nodule detectability as a function of location. Changes in nodule position caused observer score variations that were equivalent to the variation caused by an up to 185% change in nodule CD (78% average over all six images), an up to 68% change in diameter (32% average), and an up to 28% change in area under the receiver operating characteristic curve (Az) (14% average). CONCLUSION: Local anatomic variations surrounding and overlying a subtle lung nodule on a chest radiograph that are created by the projection of anatomic features in the thorax, such as ribs and pulmonary vessels, can greatly influence the detection of nodules, altering the Az by as much as 28%.     

Temporal subtraction for detection of solitary pulmonary nodules on chest radiographs: evaluation of a commercially available computer-aided diagnosis system

PURPOSE: To evaluate the usefulness of a commercially available computer-aided diagnosis (CAD) system that incorporates temporal subtraction for the detection of solitary pulmonary nodules on chest radiographs by readers with different levels of experience. MATERIALS AND METHODS: Sixty pairs of chest radiographs in 30 patients with newly detected solitary pulmonary nodules and 30 normal cases, all confirmed with serial chest computed tomography (CT), were obtained from screen-film or digital radiographic systems and were digitized (spatial resolution, 0.171 mm/pixel). Temporal subtraction images were produced with an iterative image-warping technique. Five chest radiologists and five residents evaluated both image sets for solitary nodules: set A, current and prior radiographs with temporal subtraction images, and set B, current and prior radiographs only. Assessment was performed with receiver operating characteristic (ROC) analysis of the images on a monitor (pixel size, 1,280 x 1,024) equipped with the system. The reading time needed by each reader was recorded in each case. RESULTS: For the chest radiologists, no statistically significant difference was found between set A (area under the ROC curve [A(z)] = 0.934) and set B (A(z) = 0.964). For the residents, however, observer performance in set A (A(z) = 0.907) was superior to that in set B (A(z) = 0.855) (P <.05). For both groups, the mean reading time per case for set A (chest radiologists, 16.7 seconds; residents, 15.7 seconds) was significantly (P <.05) shorter than that for set B (chest radiologists, 20.4 seconds; residents, 26.2 seconds). CONCLUSION: For the detection of solitary pulmonary nodules, the CAD system with temporal subtraction can promote efficiency for established chest radiologists and improvement in accuracy for less experienced readers.     

VolumeRAD容积成像在肺结节诊断中的应用

目的探讨VolumeRAD容积成像检测肺结节的价值。方法39例，男21例，女18例，平均年龄（53．8±9．7）岁。用CT进行随访的肺结节患者进行了额外的PA胸部平片检查和VolumeRAD容积成像。三名胸部x线诊断医师在PACS工作站上独立对胸部平片和VolumeRAD图像进行分析，结节依据清晰度被分为“清晰可见”，“不确定”，和“不可见”，依据大小分为：A组（直径〈5mm），B组（直径5～10mm），C组（直径〉10mm）。胸部CT作为参考标准，结节“清晰可见”为真阳性，计算胸部平片和VolumeRAD成像对肺结节的诊断灵敏度。结果CT上共显示结节247个，胸部平片和VolumeRAD成像对肺结节诊断的灵敏度分别为（69．90±1．30）％和（24．02±1．17）Y00（P〈0．05），在各组间和总数间差别均有统计学意义。结论与PA胸片比较，VolumeRAD容积成像具有提高已知肺小结节检测性能的潜力。     

不同大小和密度肺结节DR摄影:使用和不使用计算机辅助检测的比较

目的:评价DR摄影与计算机辅助检测对肺结节的大小和密度诊断测量的相关性.方法:选取177例(186个结节)经CT证实存在肺小结节的DR胸片和180例正常DR胸片,结节直径5～20mm.应用受试者操作特性曲线(ROC)分析未使用和使用CAD系统对不同大小(D)和密度肺结节检测结果的差异.结果:对于诊断3组不同大小(5 m...     

ROC Analysis of Detection of Metastatic Pulmonary Nodules on Digital Chest Radiographs with Temporal Subtraction

RATIONALE AND OBJECTIVES: The authors' purpose was to evaluate the effect of temporal subtraction on digital chest radiographs in the detection of metastatic pulmonary nodules. MATERIALS AND METHODS: The study included 21 cases with metastatic pulmonary nodule and 21 cases without metastatic nodule. Eleven radiologists, including eight residents and three certified radiologists, provided their confidence levels for the presence or absence of pulmonary nodules without and with temporal subtraction. Their performances without and with temporal subtraction were evaluated by means of receiver operating characteristic analysis with both independent and sequential tests. RESULTS: For the independent test, the radiologists' Az (area under the receiver operating characteristic curve) values were 0.871 without and 0.954 with temporal subtraction, compared with 0.882 and 0.955, respectively, for the sequential test. Diagnosis accuracy was significantly improved with the use of temporal subtraction. There was no significant difference in Az values between the independent and sequential tests. CONCLUSION: Temporal subtraction is useful in the detection of metastatic pulmonary nodules, and this technique augments the value of digital chest radiography.     

Computer-aided detection of pulmonary nodules: influence of nodule characteristics on detection performance

AIM: To evaluate prospectively the influence of pulmonary nodule characteristics on detection performances of a computer-aided diagnosis (CAD) tool and experienced chest radiologists using multislice CT (MSCT). MATERIALS AND METHODS: MSCT scans of 20 consecutive patients were evaluated by a CAD system and two independent chest radiologists for presence of pulmonary nodules. Nodule size, position, margin, matrix characteristics, vascular and pleural attachments and reader confidence were recorded and data compared with an independent standard of reference. Statistical analysis for predictors influencing nodule detection or reader performance included chi-squared, retrograde stepwise conditional logistic regression with odds ratios and nodule detection proportion estimates (DPE), and ROC analysis. RESULTS: For 135 nodules, detection rates for CAD and readers were 76.3, 52.6 and 52.6%, respectively; false-positive rates were 0.55, 0.25 and 0.15 per examination, respectively. In consensus with CAD the reader detection rate increased to 93.3%, and the false-positive rate dropped to 0.1/scan. DPEs for nodules < or = 5 mm were significantly higher for ICAD than for the readers (p < 0.05). Absence of vascular attachment was the only significant predictor of nodule detection by CAD (p = 0.0006-0.008). There were no predictors of nodule detection for reader consensus with CAD. In contrast, vascular attachment predicted nodule detection by the readers (p = 0.0001-0.003). Reader sensitivity was higher for nodules with vascular attachment than for unattached nodules (sensitivities 0.768 and 0.369; 95% confidence intervals = 0.651-0.861 and 0.253-0.498, respectively). CONCLUSION: CAD increases nodule detection rates, decreases false-positive rates and compensates for deficient reader performance in detection of smallest lesions and of nodules without vascular attachment.     

双能量数字减影胸片对肺内小结节检出的意义

目的探讨双能量数字减影胸片对肺内小结节检出的意义。方法27例病理证实恶性肿瘤伴肺内转移的患者,分别行增强CT扫描、双能量数字减影胸片与常规DR胸片。使用柯达质量控制检测仪比较双能量数字减影DR与常规DR胸片的图像质量。再以CT扫描结果为金标准,由2位高年资放射科医师采用双盲法对双能量数字减影胸片与常规DR胸片进行分析,比较两者对肺内转移瘤的检出有无显著差异。结果双能量数字减影DR与常规DR图像在噪声上(均匀度)无差异,但清晰度稍差。双能量数字化减影的胸片对肺内小结节的检出率为91.2%;而常规DR胸片对肺转移瘤的检出率为85.0%,两者之间有显著差异(P<0.05)。结论双能量数字减影技术可减少肺野内骨骼及其它钙化影响,对肺内结节的检出能力高于常规DR胸片。     

Digital synthesis of lung nodules

Studies evaluating observer accuracy and visual perception of pulmonary nodules usually are based upon test films obtained from clinical practice in patients with proven pulmonary nodules. Unfortunately, such nodules do not always occur in the optimal size and location to facilitate testing. Such studies would be enhanced by the ability to place nodules of desired size and location on chest radiographs. This report describes a method of placing a computer-generated (synthesized) nodule on a digitized chest radiograph. To demonstrate the similarity of these synthesized nodules to real nodules, each digitized radiograph with a computer-generated nodule was paired with a digitized chest radiograph of a patient with a clinically proven pulmonary nodule. A total of 22 pairs of chest radiographs were then shown to 13 radiologists, who were asked to distinguish the synthesized nodule from the real nodule. With this two alternative forced-choice test, the radiologists were only able to distinguish the synthesized nodule in 51% of the cases, strongly suggesting that computer generated nodules may be used to simulate real pulmonary nodules in future tests of nodule detection.     

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.     

The potential contribution of a computer-aided detection system for lung nodule detection in multidetector row computed tomography

RATIONALE AND OBJECTIVES: We sought to evaluate the potential benefits of a computer-aided detection (CAD) system for detecting lung nodules in multidetector row CT (MDCT) scans. METHODS: A CAD system was developed for detecting lung nodules on MDCT scans and was applied to the data obtained from 15 patients. Two chest radiologists in consensus established the reference standard. The nodules were categorized according to their size and their relationship to the surrounding structures (nodule type). The differences in the sensitivities between an experienced chest radiologist and a CAD system without user interaction were evaluated using a chi2 analysis. The differences in the sensitivities also were compared in terms of the nodule size and the nodule type. RESULTS: A total of 309 nodules were identified as the reference standard. The sensitivity of a CAD system (81%) was not significantly different from that of a radiologist (85%; P > 0.05). The sensitivities of the CAD system for detecting nodules < or = 5 mm in diameter as well as detecting isolated nodules were higher than those of a radiologist (83% vs. 75%, P > 0.05; 93% vs. 76%, P < 0.001). The sensitivities of a radiologist for detecting nodules >5 mm and the nodules attached to other structures were higher than those of a CAD system (98% vs. 79%, P < 0.001; 91% vs. 71%, P < 0.001). There were 28.8 false-positive results of CAD per CT study. CONCLUSION: The CAD system developed in this study performed the nodule detection task in different ways to that of a radiologist in terms of the nodule size and the nodule type, which suggests that the CAD system can play a complementary role to a radiologist in detecting nodules from large CT data sets.     

Neural network based detection of pulmonary nodules on chest radiographs

PURPOSE: We investigated the capabilities of an artificial neural network-based Computer-Aided Diagnosis (CAD) system in improving early detection of pulmonary nodules on chest radiographs. MATERIAL AND METHODS: We used a data-set of 145 digitized chest films. Two different radiologists read the radiographs to detect the sites of possible nodules. The system uses two neural networks trained on a training-set of 100 radiographs selected from the data-set. The first network is used to focus attention on the sites of potential nodules while the second calculates the likeliness of nodule presence in ROIs. The clinical test was performed on 45 more radiographs from the training-set, but different from those in the data-set, which were positive for both benign and malignant nodules. These latter plain films showed 65 nodular lesions which differed by shape and acquisition technique. RESULTS: Sensitivity was 89% in all radiographs while specificity, evaluated by ROI, and accuracy, were 98%. CONCLUSIONS: There are potential limitations in nodule detection on plain radiographs. Some of them are operator-dependent, such as nonsystematic investigation, lesion underestimation, and poor reading, and some are technique-dependent, such as X-ray beam/tube, low voltage, patient positioning, focus-film distance and development process. CADs may contribute to improving detection of pulmonary nodules because the false-negative rate is decreased and sensitivity consequently increased. The high sensitivity and specificity rates of neural networks encourage further trials on wider data-sets to help the radiologist in the early detection of pulmonary nodules.     

Effect of high sensitivity in a computerized scheme for detecting extremely subtle solitary pulmonary nodules in chest radiographs: observer performance study

RATIONALES AND OBJECTIVES: This study investigated the effect of a high sensitivity in computer-aided diagnosis (CAD) for detecting lung nodules in chest radiographs when extremely subtle cases were presented to radiologists. MATERIAL AND METHODS: The chest radiographs used in this study consisted of 36 normal images and 54 abnormals containing solitary lung nodules, of which 25 were extremely subtle and 29 were very subtle. Receiver operating characteristic analysis for detecting lung nodules was performed without and with CAD. The levels of CAD output were simulated with a hypothetical ideal performance of 100% sensitivity, but with three or four false positives per image. Six radiologists participated in an observer study in which cases were interpreted first without and then with the use of CAD. RESULTS: The average A(z) values for radiologists without and with CAD were 0.682 and 0.808, respectively. The performance of radiologists was improved significantly when high sensitivity was used (P = .0003). However, the radiologists were not able to recognize some extremely subtle nodules (5 of 54 nodules by all radiologists), even with the correct CAD output; these nodules were then considered as non-actionable. None of 306 computer-false positives was incorrectly regarded as a nodule by all radiologists, but 63 false positives were incorrectly identified by one or more radiologists. CONCLUSION: The accuracy of radiologists in the detection of some extremely subtle solitary pulmonary nodules can be improved significantly when the sensitivity of a CAD scheme can be made to be at an extremely high level. However, all of the six radiologists failed to identify some nodules (about 10%), even with the correct output of the CAD.     

Improved pulmonary nodule detection with scanning equalization radiography

The potential for improved pulmonary nodule detection with scanning equalization radiography (SER) was evaluated by means of observer performance testing during the interpretation of posteroanterior conventional radiographs and SER images of an anthropomorphic chest phantom with simulated nodules. A test set of 200 conventional and 200 SER radiographs of phantoms containing either one nodule or none was interpreted by four radiologists attempting to detect a nodule and indicate a confidence value. Their ability to detect nodules positioned over the lung was slightly improved with SER compared with conventional radiography (sensitivity, .56 vs .70); for nodules over the mediastinum or diaphragmatic areas, it was much improved (sensitivity, .29 vs .64). The results were also analyzed with receiver-operating characteristic methods, which revealed a significant improvement in lesion detect-ability over the thicker body parts with SER images. The capability of equalized chest radiographs to provide improved lesion detectability suggests that SER may set a new standard for film-based chest radiography and have a large clinical application.     

Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection

PURPOSE: To compare the performance of radiologists and of a computer-aided detection (CAD) algorithm for pulmonary nodule detection on thin-section thoracic computed tomographic (CT) scans. MATERIALS AND METHODS: The study was approved by the institutional review board. The requirement of informed consent was waived. Twenty outpatients (age range, 15-91 years; mean, 64 years) were examined with chest CT (multi-detector row scanner, four detector rows, 1.25-mm section thickness, and 0.6-mm interval) for pulmonary nodules. Three radiologists independently analyzed CT scans, recorded the locus of each nodule candidate, and assigned each a confidence score. A CAD algorithm with parameters chosen by using cross validation was applied to the 20 scans. The reference standard was established by two experienced thoracic radiologists in consensus, with blind review of all nodule candidates and free search for additional nodules at a dedicated workstation for three-dimensional image analysis. True-positive (TP) and false-positive (FP) results and confidence levels were used to generate free-response receiver operating characteristic (ROC) plots. Double-reading performance was determined on the basis of TP detections by either reader. RESULTS: The 20 scans showed 195 noncalcified nodules with a diameter of 3 mm or more (reference reading). Area under the alternative free-response ROC curve was 0.54, 0.48, 0.55, and 0.36 for CAD and readers 1-3, respectively. Differences between reader 3 and CAD and between readers 2 and 3 were significant (P < .05); those between CAD and readers 1 and 2 were not significant. Mean sensitivity for individual readings was 50% (range, 41%-60%); double reading resulted in increase to 63% (range, 56%-67%). With CAD used at a threshold allowing only three FP detections per CT scan, mean sensitivity was increased to 76% (range, 73%-78%). CAD complemented individual readers by detecting additional nodules more effectively than did a second reader; CAD-reader weighted kappa values were significantly lower than reader-reader weighted kappa values (Wilcoxon rank sum test, P < .05). CONCLUSION: With CAD used at a level allowing only three FP detections per CT scan, sensitivity was substantially higher than with conventional double reading.     

数字化体层融合在肺结节探查中的初步应用

目的 探讨数字化体层融合在肺结节探查中的应用价值.方法 30例疑有肺内结节的患者,均先后行胸部X线平片、体层融合和CT检查.将上述影像资料传至后处理工作站,由2名具有3年以上胸部影像诊断经验的医师分别进行双盲法阅片,观察每例患者肺部结节的数量,每个肺结节的部位、大小.然后,2名阅片者再共同阅片,使单独阅片不一致的结果得到统一.以CT结果作为标准,分别计算胸部X线平片和体层融合探查肺结节的敏感性.采用配对四格表资料的X~2检验比较两者的差异性.结果 30例患者,胸部X线平片检查9例阴性,21例阳性,共发现肺结节40个.体层融合检查4例阴性,26例阳性,共发现肺结节89个.CT检查3例阴性,27例阳性,共发现肺结节102个.以CT作为标准对照,胸部X线平片探查肺结节的敏感性为27.4%(28/102),体层融合的敏感性为87.2%(89/102),差异有统计学意义(X~2=4.35,P<0.05).结论 数字化体层融合可显著提高肺结节探查的敏感性,可以作为胸部X线平片良好和必要的补充.     

Selenium-based digital radiography versus high-resolution storage phosphor radiography in the detection of solitary pulmonary nodules without calcification: receiver operating characteristic curve analysis

OBJECTIVE: The purpose of this study was to compare selenium-based digital radiography with high-resolution storage phosphor radiography for the detection of solitary pulmonary nodules without calcification. MATERIALS AND METHODS: One hundred twenty-four patients underwent selenium-based digital radiography, high-resolution storage phosphor radiography, and chest CT for evaluation of pulmonary nodules. Thirty-one patients with pulmonary nodules smaller than 3 cm in diameter and 40 patients with normal lungs were selected for receiver operating characteristic curve analysis. Five board-certified radiologists who were unaware of the CT results independently reviewed each of the hard copies of selenium-based digital radiography and storage phosphor radiography, identified pulmonary nodules, and graded their confidence for the presence of each nodule. For each radiologist, we calculated the areas under the receiver operating characteristic curve (AUC) for selenium-based digital radiography and storage phosphor radiography. RESULTS: The average performance of selenium-based digital radiography (AUC = 0.72) was higher than that of high-resolution storage phosphor radiography (AUC = 0.64), which is statistically significant (p <0.05). CONCLUSION: Our results indicate that selenium-based digital radiography is superior to high-resolution storage phosphor radiography for detecting solitary pulmonary nodules without calcification.