低剂量CT扫描联合计算机辅助检测系统筛查肺结节的临床研究

目的 探讨低剂量胸部CT扫描联合计算机辅助检测肺结节(CAD)系统筛查肺癌高危人群肺内结节的临床价值和CAD系统对放射科医师的辅助作用.方法 选取219名具有肺癌高危因素体检者,行低剂量胸部CT平扫,由2名具有15年以上胸部诊断经验的放射科高年资医师独立阅读1.0 mm层厚重建图像,记录每例结节表现,两者意见一致后保留诊断结果作为金标准;应用CAD系统对上述图像进行结节识别处理并记录检出结果,另由2名具有5年影像诊断工作的放射科年轻医师阅读上述图像,记录诊断结果,然后应用CAD系统的输出结果再次阅读图像并记录诊断结果,根据金标准判断CAD系统检测肺结节的敏感性、假阳性率,应用X~2检验比较年轻医师应用CAD系统前后肺结节检测的能力.结果 219名体检者中最终确定有结节者104(47.5%)名,高年资医师共确定366个结节为真结节.在366个真结节中,CAD系统检测到271个(74.0%,CAD系统共检测到695个结节,假阳性结节424个);2名年轻医师未用CAD系统时分别检测到292(79.8%)和286个(78.1%)结节,应用CAD系统后分别检测到336(91.8%)和333个(91.0%)结节,年轻医师应用CAD系统前后肺结节检测的敏感性之间差异具有统计学意义(P<0.01).结论 CAD系统对肺门区或中心区的结节检测敏感性较年轻医师高,年轻医师对周围区、胸膜下结节、磨玻璃密度结节、≤4 mm结节的检测敏感性明显优于CAD系统,两者相互结合能够提高肺结节的检出率.     

计算机辅助检测系统在低剂量肺癌筛查结节检出中的应用价值

目的 评价计算机辅助检测系统(CAD)在64层CT低剂量肺癌筛查肺结节检出中的应用价值及其对放射科医师的辅助作用.方法 从2007年6月至2008年6月肺癌低剂量筛查数据库共578例中运用纯随机抽样方法抽取100例.低剂量CT扫描参数管电压120 kV,管电流30及40 mA或管电流调制技术,层厚1.25或1.00 mm.由2名胸部影像医师首先阅读胸部CT图像,再应用CAD系统按结节所在位置分为肺外野、肺内野两部分,并将结节检出阈值分别设定为3.0、4.0、5.0 mm进行分析.所有结节以2人达成一致作为真结节.分别分析医师双阅片及CAD系统检出结节的能力,并进行McNemar-Bowker检验.结果 在100例胸部低剂量CT中,共检出真结节257枚,直径为1.7 ～18.5 mm;分布在双肺外野191枚,双肺内野66枚.CAD系统肺结节检出率为91.1％( 234/257),漏诊率为8.9％( 23/257),在漏诊的23枚结节中,10枚为实性结节,直径为2.4～6.0 mm;13枚为非实性结节,直径为2.1～8.6 mm;分布在肺外野17枚,肺内野6枚.放射科医师阅片结节检出率为59.1％(152/257),漏诊率为40.9％( 105/257),漏诊结节中94枚为实性结节,10枚为部分实性结节,1枚为非实性结节,结节大小2.4～11.8 mm;分布在肺外野69枚,肺内野36枚.结论 低剂量螺旋CT肺癌筛查中CAD检出肺结节的能力明显高于医师双阅片,尤其是对肺内野病灶的检出.使用CAD作为辅助诊断时,对非实性结节漏诊率高.     

计算机辅助检测系统在数字化胸片肺结节检出中的应用价值

目的评价计算机辅助检测（computer-aided detection，CAD）系统在数字化胸片肺结节检出中的应用价值及其对放射科医师的辅助作用。方法选取数字化胸片328例。由2名专家组医师应用IQQATM-Chest系统阅读具有结节样阴影的胸片，2人意见达成一致后标记结节的位置和大小并保存标记结果，将标记结果作为金标准来评估CAD系统检测肺结节的能力。由8名不同年资的放射科医师首先独立阅读具有结节样阴影的数字X线摄影（DR）胸片并保存诊断结果，然后再应用CAD系统阅读胸片，将最终结果存入CAD系统。应用受试者操作特征曲线（ROC）和配对t检验来分析放射科医师应用CAD系统前后在肺结节检测能力上的差异。结果在100例DR胸片中，金标准结节151个，CAD系统肺结节检测敏感性为78．1％（118．0／151），低年资放射科医师不用和应用CAD系统时，肺结节的检测敏感性分别为62．4％（94．2／151）和77．4％（116．8／151），ROC曲线下面积分别为0．769和0．836，二者之间的差异具有统计学意义（P〈0．01）；高年资放射科医师不用和应用CAD系统时，肺结节的检测敏感性分别为73．8％（111．5／151）和76．2％（115．0／151），ROC曲线下面积分别为0．820和0．827，二者之间的差异无统计学意义（P〉0．05）。结论CAD系统能够辅助放射科医师提高肺小结节的检测敏感性，对低年资医师的帮助更大。     

经结节体积的分析对低剂量CT中肺结节的计算机辅助诊断性能与双人阅片法的比较

目的通过对结节体积的分析,评估低剂量CT中肺结节的计算机辅助诊断(CAD)性能对双人阅片法的优越性。方法从NELSON肺癌筛查试验中随机抽取400例低剂量胸部CT检查的影像。经2名独立的诊断医师和CAD分别对CT影像进行评估。根据目前胸部放射诊断标准,对经诊断医师和(或)CAD标记的1667个结节进行评价。通过计算肺结节检测的敏感性和假阳性的数量、结节的特点和数量对性能进行评估。结果根据筛查方法,90.9%的结节不需进一步的评估,49.2%为小结节(<50mm3)。排出小结节后,CAD将假阳性检测率从3.7%降低到1.9%。151个结节需要进一步评估,与双人阅片比较,仅被CAD发现的结节有33个(21.9%),其中1人第2年被确诊为肺癌。对结节检测的敏度:双人阅片为78.1%,CAD为96.7%。共有69.7%未被诊断医师发现的结节为连接性结节,其中78.3%与血管相连。结论当排出小结节后,CAD的假阳性率低于双人阅片,并且有助于提高在肺癌筛查中肺结节检测的敏感性。     

计算机辅助检测系统在低剂量CT早期肺癌筛查中的应用研究

目的:评价计算机辅助检测(computed aided detection,CAD)系统在低剂量CT肺癌筛查中的应用价值。方法:120例受检者的胸部低剂量CT图像纳入本研究。采用CAD肺结节自动分析软件(方法 A)、横断面薄层图像结合MIP图像(方法 B)、方法 C(方法 A+B)。方法 B和C由低年资和中年资影像诊断科医师各1名独立完成。以2名高年资影像诊断医师应用方法 C检出的肺结节的一致意见为真结节参照标准。记录检出肺结节的大小、位置和密度,所得数据应用SPSS 16.0软件进行统计学分析。结果:2位高年资医师确定了178个真结节。方法 A检出真结节121个,敏感性为67.98%;低年资和中年资医师应用方法 B检出的真结节数及敏感性分别为97个(54.49%)、142个(79.78%);2位医师应用方法 C检出真结节数及敏感性分别为138个(77.53%)、170个(95.50%)。低年资和中年资医师应用方法 C检出肺结节的敏感性均高于方法B,且差异有统计学意义(P0.001)。低年资和中年资医师应用方法C检出的结节数与参照标准均具有较高的一致性(Kappa=0.718、0.930,P0.001),2位医师之间具有较高的一致性(Kappa=0.784,P0.001)。结论:CAD系统明显提高了影像诊断医师对肺结节的检出能力,具有较高的应用价值。由于CAD系统相对较高的假阴性,在实际工作中尚不能独立应用。     

深度学习辅助诊断系统在胸片的应用研究:气胸及肺结节检测

目的 评估基于深度学习的计算机辅助诊断系统(DL-CAD)的X线胸片气胸及肺结节检出效能,以及DL-CAD辅助对医师诊断效能的影响.方法 搜集经CT扫描证实的80例气胸及100例肺结节患者的X线胸片.将所有胸片上传至DL-CAD以及PACS系统.气胸及肺结节的胸片各由两名低年资放射医师(小于5年诊断经验)及两名高年资医...     

计算机辅助检测系统在X线胸片中的应用进展

同其他影像检查(如CT、MRI)相比,胸片因其方便、经济,能够提供结节大小、生长速度等信息,仍是影像筛查的首选手段,但由于结节隐藏在心脏、肺门、大血管、骨组织及膈肌附近等解剖学死角区或因读片不仔细,而不能发现早期病变,造成诊断的准确率不高和漏诊率过高。如何提高胸片诊断的敏感性和特异性是放射科医师所面临的严峻问题,国外学者在胸片上应用了计算机辅助检测(computer-aided detection,CAD)设计系统,研究结果显示应用CAD系统可以提高病灶的检出率,甚至可以将平片上早期肺癌的发现率从15%提高到60%。随着数字化胸片的普及和CAD设…     

人工智能辅助软件可提升疲劳状态下放射科规培医师对肺结节的检测效能

目的:探讨在人工智能(AI)肺结节检测软件的辅助下能否提升疲劳状态的放射科规培医师对肺结节的检测效能。方法:搜集182例患者的1 mm薄层胸部CT图像,有一位放射科规培医师分别在3种模式下进行阅片:正常状态下独立阅片(A组)、疲劳状态下(即一天日常工作满8小时以上)独立阅片(B组)、疲劳状态下使用AI软件辅助阅片(C组),三种阅片模式均间隔洗脱期(2周),分别记录每次阅片时检出结节的位置、大小和数目。将3次肺结节检出结果与金标准(由2位从事胸部影像诊断超过8年的中级医师结合AI筛查结果分别作出诊断,再由1位从事胸部影像诊断超过15年的高级医师最终审核确定)进行比较,计算敏感度和(患者)人均假阳性(误诊)结节数来评价3种模式的检测效能。结果:经金标准确认1281个肺结节,A组检出真阳性结节592个、假阳结节297个,敏感度46.21%,人均误诊结节数为1.63;B组检出真阳性结节517个、假阳结节225个,敏感度40.36%,人均误诊结节数为1.24;C组检出真阳性结节995个、假阳结节165个,敏感度77.67%,人均误诊结节数为0.91。B组的敏感度和人均误诊结节数均较A组降低,差异均有统计学意义(P<0.05);C组的敏感度较B组提高,且人均误诊结节数降低,差异均有统计学意义(P<0.05);C组的敏感度较A组提高,人均误诊结节数降低,差异均有统计学意义(P<0.05)。结论:疲劳显著降低了放射科规培医师对肺结节的检测效能,但在AI软件辅助下能明显提高疲劳状态下放射科规培医师对肺结节的检出效能,甚至超过其正常状态下的水平。     

人工智能对肺结节检出及定性的效能评估

目的评估人工智能(AI )对肺结节的检出及定性的诊断效能。方法采用回顾性研究方法, 通过简单随机抽样选取2020—2021年河北中石油中心医院肺结节病例库中的355例患者[女性205例、男性150例, 年龄(55.1±12.2)岁]的肺部CT图像并导入AI系统。将AI与3名初级职称医师的诊断结果进行对比, 2名中级职称医师按照双盲原则对CT图像进行审核, 并以2名中级职称医师的一致性意见作为真结节诊断的参考标准, 比较AI与初级职称医师对肺结节检出的灵敏度。105例患者于术前CT引导下行穿刺组织病理学检查或肺组织切除术后组织病理学检查, 以组织病理学检查结果为"金标准", 比较AI与副主任医师对肺结节定性诊断的灵敏度、特异度、阳性预测值、阴性预测值、准确率。计数资料的组间比较采用卡方检验或Fisher精确概率检验。结果 355例患者的CT图像中共检出真结节1 072个, 其中AI共检出真结节1 063个, 漏诊9个, 其灵敏度为99.16%(1 063/1 072);初级职称医师共检出真结节1 009个, 漏诊63个, 其灵敏度为94.12%(1 009/1 072)。在肺结节检出方面...     

计算机辅助检测系统在检测胸片肺结节中的应用

目的 探讨计算机辅助检测系统(CAD)在检测肺结节中的应用价值.方法 随机选取有胸部CT检查且在前后10 d内行胸部后前位片检查的200例患者,由3位不同临床经验的放射科医师分别记录应用CAD前和应用CAD后直径在3～20 mm的结节个数,并以CT检查为金标准,对其检出敏感性进行统计学分析.结果 CT发现45例80个结节符合直径在3～20 mm,CAD准确率51.25%,放射科医师甲、乙、丙在使用CAD前准确率分别为40%、37.5%及26.25%,使用CAD后准确率分别为46.25%、46.25%及30.0%.在直径3～10 mm的结节中,CAD发现30个,放射科医师甲、乙、丙在使用CAD前分别发现12、13、7个,各位医师的准确率与CAD间有显著差异(P<0.05);使用CAD后,放射科医师对3～20 mm结节的准确率无明显提高(P>0.05).结论放射科医师应重视CAD提示的小结节,尤其是低年资医师和对直径3～10 mm的结节.     

Comparison of radiologist and CAD performance in the detection of CT-confirmed subtle pulmonary nodules on digital chest radiographs

OBJECTIVES: Detection of subtle pulmonary nodules on digital radiography is a challenging task for radiologists. The aim of this study was to evaluate the performance of a newly approved computer aided detection (CAD) system. MATERIALS AND METHODS: The sensitivity of 3 radiologists and of a CAD system for the detection of pulmonary nodules from 5 to 15 mm in size on digital chest radiography of 117 patients was compared. The reference standard was established by consensus reading of computed tomography scans by 2 experienced radiologists. Computed tomography scans and chest radiographs were performed within 4 weeks. Sixty-six pulmonary nodules from 42 patients, with a mean nodule diameter of 7.5 mm (standard deviation: 2.2 mm), were included in the statistical analysis. Seventy-five of the 117 patients did not have nodules from 5 to 15 mm of size. RESULTS: Two hundred and eighty-eight false-positive detections of the CAD system were found with an average of 2.5 false-positives per image. Sensitivity of the CAD system was 39.4% (95% confidence interval: 11.8%), when compared with 18.2% to 30.3% (95% confidence interval 9.3% to 11.1%) of the 3 radiologists. Substantial agreement for nodule detection ([kappa]N: 0.64-0.73) was found among the 3 radiologists, whereas only moderate agreement was found between the radiologists and the CAD performance ([kappa]N: 0.45-0.52). CONCLUSIONS: The CAD system's diagnostic sensitivity in detecting pulmonary nodules of 5 to 15 mm of size was superior to the 1 of radiologists. The CAD system may be used for assisting the radiologist in the detection of lung nodules on digital chest radiographs.     

Potential usefulness of computerized nodule detection in screening programs for lung cancer.

RATIONALE AND OBJECTIVE. To alert radiologists to possible nodule locations and subsequently to reduce the number of false-negative diagnoses, the authors are developing a computer-aided diagnostic (CAD) scheme for the detection of lung nodules in digital chest images. METHODS. A computer-vision scheme was applied to photofluorographic films obtained in a mass survey for detection of asymptomatic lung cancer in Japan. Ninety-five patients with abnormal test results who had primary and metastatic lung cancers and 103 patients with normal test results were included. RESULTS. The sensitivity of the computer output was comparable with that of physicians in this mass survey (62%). The computer detected approximately 40% of all nodules missed in the mass survey, but missed 17 true-positive results identified in the mass survey. The CAD scheme produced an average of 15 false-positive findings per image. CONCLUSION. If the number of false-positive results can be significantly reduced, computer-vision schemes such as this may have a role in lung cancer screening programs.     

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.     

Computer-Aided Detection of Malignant Lung Nodules on Chest Radiographs: Effect on Observers' Performance

Objective

To evaluate the effect of computer-aided detection (CAD) system on observer performance in the detection of malignant lung nodules on chest radiograph.

Materials and Methods

Two hundred chest radiographs (100 normal and 100 abnormal with malignant solitary lung nodules) were evaluated. With CT and histological confirmation serving as a reference, the mean nodule size was 15.4 mm (range, 7-20 mm). Five chest radiologists and five radiology residents independently interpreted both the original radiographs and CAD output images using the sequential testing method. The performances of the observers for the detection of malignant nodules with and without CAD were compared using the jackknife free-response receiver operating characteristic analysis.

Results

Fifty-nine nodules were detected by the CAD system with a false positive rate of 1.9 nodules per case. The detection of malignant lung nodules significantly increased from 0.90 to 0.92 for a group of observers, excluding one first-year resident (p = 0.04). When lowering the confidence score was not allowed, the average figure of merit also increased from 0.90 to 0.91 (p = 0.04) for all observers after a CAD review. On average, the sensitivities with and without CAD were 87% and 84%, respectively; the false positive rates per case with and without CAD were 0.19 and 0.17, respectively. The number of additional malignancies detected following true positive CAD marks ranged from zero to seven for the various observers.

Conclusion

The CAD system may help improve observer performance in detecting malignant lung nodules on chest radiographs and contribute to a decrease in missed lung cancer.     

Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs ROC analysis of radiologists' performance

RATIONALE AND OBJECTIVES: The aim of the study is to investigate the effect of a computer-aided diagnostic (CAD) scheme on radiologist performance in the detection of lung cancers on chest radiographs. MATERIALS AND METHODS: We combined two independent CAD schemes for the detection and classification of lung nodules into one new CAD scheme by use of a database of 150 chest images, including 108 cases with solitary pulmonary nodules and 42 cases without nodules. For the observer study, we selected 48 chest images, including 24 lung cancers, 12 benign nodules, and 12 cases without nodules, from the database to investigate radiologist performance in the detection of lung cancers. Nine radiologists participated in a receiver operating characteristic (ROC) study in which cases were interpreted first without and then with computer output, which indicated locations of possible lung nodules, together with a five-color scale illustrating the computer-estimated likelihood of malignancy of the detected nodules. RESULTS: Performance of the CAD scheme indicated that sensitivity in detecting lung nodules was 80.6%, with 1.2 false-positive results per image, and sensitivity and specificity for classification of nodules by use of the same database for training and testing the CAD scheme were 87.7% and 66.7%, respectively. Average area under the ROC curve value for detection of lung cancers improved significantly (P = .008) from without (0.724) to with CAD (0.778). CONCLUSION: This type of CAD scheme, which includes two functions, namely detection and classification, can improve radiologist accuracy in the diagnosis of lung cancer.     

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.     

Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume

Objective

To evaluate performance of computer-aided detection (CAD) beyond double reading for pulmonary nodules on low-dose computed tomography (CT) by nodule volume.

Methods

A total of 400 low-dose chest CT examinations were randomly selected from the NELSON lung cancer screening trial. CTs were evaluated by two independent readers and processed by CAD. A total of 1,667 findings marked by readers and/or CAD were evaluated by a consensus panel of expert chest radiologists. Performance was evaluated by calculating sensitivity of pulmonary nodule detection and number of false positives, by nodule characteristics and volume.

Results

According to the screening protocol, 90.9?% of the findings could be excluded from further evaluation, 49.2?% being small nodules (less than 50?mm³). Excluding small nodules reduced false-positive detections by CAD from 3.7 to 1.9 per examination. Of 151 findings that needed further evaluation, 33 (21.9?%) were detected by CAD only, one of them being diagnosed as lung cancer the following year. The sensitivity of nodule detection was 78.1?% for double reading and 96.7?% for CAD. A total of 69.7?% of nodules undetected by readers were attached nodules of which 78.3?% were vessel-attached.

Conclusions

CAD is valuable in lung cancer screening to improve sensitivity of pulmonary nodule detection beyond double reading, at a low false-positive rate when excluding small nodules.

Key Points

? Computer-aided detection (CAD) has known advantages for computed tomography (CT). ? Combined CAD/nodule size cut-off parameters assist CT lung cancer screening. ? This combination improves the sensitivity of pulmonary nodule detection by CT. ? It increases the positive predictive value for cancer detection.     

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.