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.     

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.     

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.     

Assessing the use of digital radiography and a real-time interactive pulmonary nodule analysis system for large population lung cancer screening

Rationale and objectives

To assess the use of chest digital radiograph (DR) assisted with a real-time interactive pulmonary nodule analysis system in large population lung cancer screening.

Materials and methods

346 DR/CR patient studies with corresponding CT images were selected from 12,500 patients screened for lung cancer from year 2007 to 2009. Two expert chest radiologists established CT-confirmed Gold Standard of nodules on DR/CR images with consensus. These cases were read by eight other chest radiologists (participating radiologists) first without using a real-time interactive pulmonary nodule analysis system and then re-read using the system. Performances of participating radiologists and the computer system were analyzed.

Results

The computer system achieved similar performance on DR and CR images, with a detection rate of 76% and an average FPs of 2.0 per image. Before and after using the computer-aided detection system, the nodule detection sensitivities of the participating radiologists were 62.3% and 77.3% respectively, and the A_z values increased from 0.794 to 0.831. Statistical analysis demonstrated statically significant improvement for the participating radiologists after using the computer analysis system with a P-value 0.05.

Conclusion

The computer system could help radiologists identify more lesions, especially small ones that are more likely to be overlooked on chest DR/CR images, and could help reduce inter-observer diagnostic variations, while its FPs were easy to recognize and dismiss. It is suggested that DR/CR assisted by the real-time interactive pulmonary nodule analysis system may be an effective means to screen large populations for lung cancer.     

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 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.     

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.     

The feasibility of low-dose CT for pulmonary metastasis in patients with primary gynecologic malignancy

PURPOSE: To assess the feasibility of low-dose CT (LDCT) in the detection of pulmonary metastases in patients with primary gynecologic malignancies and also to compare the performance of chest digital radiography (DR) and LDCT for their delectability of pulmonary metastases, with use of standard-dose CT (SDCT) as the reference standard. MATERIALS AND METHODS: Thirty female patients with primary gynecologic malignancies (age range, 20-76 years; mean age, 50 years) underwent DR, noncontrast LDCT and contrast-enhanced SDCT, which were performed within an interval of 2 weeks. We used lung nodule, mediastinal lymphadenopathy (>10 mm in the short axis) and pleural changes (including effusion, irregular thickening, or nodularity) as the cardinal imaging findings of lung metastases. A five-point scoring system was designed to indicate the probability of lung metastasis from primary gynecologic malignancies. The five-point scores of DR, LDCT, and SDCT were analyzed by receiver operating characteristic (ROC) curve. RESULTS: SDCT probability scores of +2 and -2 were set to indicate true positive and true negative for pulmonary nodule, mediastinal lymphadenopathy, and pleural effusion, respectively. All the areas under the ROC curve of LDCT appeared to be larger than those of DR[pulmonary nodule: 0.96 [95% confidence interval (CI): 0.92-1.01] vs. 0.74 [95% CI: 0.57-0.91], 0.82 [95% CI: 0.70-0.95] vs. 0.61 [95% CI: 0.50-0.77]; mediastinal lymphadenopathy: 0.98 [95% CI: 0.93-1.03] vs. 0.90 [95% CI: 0.79-1.01], 0.94 [95% CI: 0.82-1.06] vs. 0.66 [95% CI: 0.44-0.88]; and pleural effusion: 0.98 [95% CI: 0.93-1.03] vs. 0.56 [95% CI: 0.29-0.82], 0.90 [95% CI: 0.74-1.05] vs. 0.46 [95% CI: 0.23-0.68]]. CONCLUSION: The performance of LDCT were comparable to those of SDCT and superior to those of DR for detection of pulmonary nodule, mediastinal lymphadenopathy, and pleural effusion. By using LDCT, there was no need of intravenous contrast injection and less radiation exposure. We propose a protocol including standard-dose abdominal CT and low-dose chest CT for the initial and follow-up stagings of primary gynecologic malignancy. The use of chest DR is unnecessary.     

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.     

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.     

Role of radiologists in CAD life-cycle

A modern CAD (computer-aided diagnosis) system development involves a multidisciplinary team whose members are experts in medical and technical fields. This study indicates the activities of medical experts at various stages of the CAD design, testing, and implementation. Those stages include a medical analysis of the diagnostic problem, data collection, image analysis, evaluation, and clinical verification. At each stage the physicians knowledge and experience are indispensable. The final implementation involves integration with the existing Picture Archiving and Communication System. The term CAD life-cycle describes an overall process of the design, testing, and implementation of a system that in its final form assists the radiologists in their daily clinical routine. Four CAD systems (applied to the bone age assessment, Multiple Sclerosis detection, lung nodule detection, and pneumothorax measurement) developed in our laboratory are given as examples of how consecutive stages are developed by the multidisciplinary team. Specific advantages of the CAD implementation that include the daily clinical routine as well as research and education activities are discussed.     

Lung nodule CAD software as a second reader: a multicenter study

RATIONALE AND OBJECTIVES: The purpose of this multicenter, multireader study was to evaluate the performance of computed tomography (CT) lung nodule computer-aided detection (CAD) software as a second reader. METHODS AND MATERIALS: The study involved 109 patients from four sites. The data were collected from a variety of multidetector CT scanners and had different scan parameters. Each chest CT scan was divided into four quadrants. A group of three expert thoracic radiologists identified nodules between 4 and 30 mm in maximum diameter within each quadrant. The standard of reference was established by a consensus read of these experienced radiologists. The cases were then interpreted by 10 other radiologist readers with varying degrees of experience, without and then with CAD software. These readers identified nodules and assigned an actionability rating to each quadrant before and after using CAD software. Receiver operating characteristic curves were used to measure the performance of the readers without and with CAD software. RESULTS: The average increase in area under the curve for the 10 readers with CAD software was 1.9% for a 95% confidence interval (0.8-8.0%). The area under the curve without CAD software was 86.7% and with CAD software was 88.7%. A nonsignificant correlation was observed between the improvement in sensitivity and experience of the radiologists. The readers also showed a greater improvement in patients with cancer as compared to those without cancer. CONCLUSIONS: In this multicenter trial, CAD software was shown to be effective as a second reader by improving the sensitivity of the radiologists in detecting pulmonary nodules.     

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.     

Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance

PURPOSE: To evaluate the effect of computer-aided diagnosis (CAD) on radiologists' detection of pulmonary nodules. MATERIALS AND METHODS: Fifty chest computed tomographic (CT) examination cases were used. The mean nodule size was 0.81 cm +/- 0.60 (SD) (range, 0.3-2.9 cm). Alternative free-response receiver operating characteristic (ROC) analysis with a continuous rating scale was used to compare the observers' performance in detecting nodules with and without use of CAD. Five board-certified radiologists and five radiology residents participated in an observer performance study. First they were asked to rate the probability of nodule presence without using CAD; then they were asked to rate the probability of nodule presence by using CAD. RESULTS: For all radiologists, the mean areas under the best-fit alternative free-response ROC curves (Az) without and with CAD were 0.64 +/- 0.08 and 0.67 +/- 0.09, respectively, indicating a significant difference (P <.01). For the five board-certified radiologists, the mean Az values without and with CAD were 0.63 +/- 0.08 and 0.66 +/- 0.09, respectively, indicating a significant difference (P <.01). For the five resident radiologists, the mean Az values without and with CAD were 0.66 +/- 0.04 and 0.68 +/- 0.04, respectively, indicating a significant difference (P =.02). At observer performance analyses, there were no significant differences in Az values obtained either without (P =.61) or with (P =.88) CAD between the board-certified radiologists and the residents. For all radiologists, in the detection of pulmonary nodules 1.0 cm in diameter or smaller, the mean Az values without and with CAD were 0.60 +/- 0.11 and 0.64 +/- 0.11, respectively, indicating a significant difference (P <.01). CONCLUSION: Use of the CAD system improved the board-certified radiologists' and residents' detection of pulmonary nodules at chest CT.     

Pulmonary nodule detection on MDCT images: evaluation of diagnostic performance using thin axial images,maximum intensity projections,and computer-assisted detection

This study aimed at evaluating the diagnostic benefits of maximum intensity projections (MIP) and a commercially available computed-assisted detection system (CAD) for the detection of pulmonary nodules on MDCT as compared with standard 1-mm images on lung cancer screening material. Thirty subjects were randomly selected from our database. Three radiologists independently reviewed three types of images: axial 1-mm images, axial MIP slabs, and CAD system detections. Two independent experienced chest radiologists decided which were true-positive nodules. Two hundred eighty-five nodules ≥1 mm were identified as true-positive by consensus of two independent chest radiologists. The detection rates of the three independent observers with 1-mm axial images were 22 ± 4.8%, 30 ± 5.3%, and 47 ± 2.8%; with MIP: 33 ± 5.4%, 39 ± 5.7%, and 45 ± 5.8%; and with CAD: 35 ± 5.6%, 36 ± 5.6%, and 36 ± 5.6%. There was a reading technique effect on the observers’ sensitivity for nodule detection: sensitivities with MIP were higher than with 1-mm images or CAD for all nodules (F-values = 0.046). For nodules ≥3 mm, readers’ sensitivities were higher with 1-mm images or MIP than with CAD (p < 0.0001). CAD was the most and MIP the less time-consuming technique (p < 0.0001). MIP and CAD reduced the number of overlooked small nodules. As MIP is more sensitive and less time consuming than the CAD we used, we recommend viewing MIP and 1-mm images for the detection of pulmonary nodules. This study was presented at the ECR 2006.     

肋骨抑制成像在胸部平片肺结节检出中的应用

目的 评价肋骨抑制成像技术在胸部平片检出肺结节中的价值.方法 回顾性分析141例胸部后前位X线片,其中95例有单发肺结节,作为研究组,46例无肺结节作为对照组.2名高年资与2名低年资放射科医师分别独立阅读所有胸部后前位X线片和经肋骨抑制成像技术处理后的胸部后前位X线片.阅读、记录结节的部位、大小并对结节存在的肯定度进行评分.观察的结果采用受试者工作特征(ROC)曲线进行分析.结果 肺结节的平均直径为(1.9±1)cm,直径范围是0.9~2.9 cm.胸部后前位X线片ROC曲线下面积(AUC)为0.844,肋骨抑制成像技术处理后的胸部后前位X线片AUC为0.873,两者有统计学差异(P<0.01).结论 肋骨抑制成像技术可以显著提高放射科医师对胸部后前位X片中肺结节的检出率.     

数字化双能量减影技术在胸部结节性病变诊断中的应用价值

目的探讨数字化双能量减影(dual-energy subtraction,DES)摄片技术在胸部结节性病变临床诊断中的优势。资料与方法搜集在本院行DES胸部摄影和CT检查,发现胸部结节的36例患者及证实无胸部结节的16例患者的资料,由两名高年资放射科医师对其普通数字化摄影(DR)图像与DES图像采用双盲法进行分析,评价两者对胸部结节的显示情况。结果DES软组织图像比普通DR图像检出更多的结节,同时对结节边缘的显示更清晰,有利于定位定性诊断。结论DES能将骨与软组织单独分开显示,分别得出标准影像、软组织影像和骨组织影像,有效地去除胸廓骨组织影的遮挡影响,提高肺结节性病变的可视性,是对DR图像诊断胸部结节的有效补充。     

Radiologist Variation in the Rates of Follow-up Imaging Recommendations Made for Pulmonary Nodules

ObjectiveDetermine whether differences exist in rates of follow-up recommendations made for pulmonary nodules after accounting for multiple patient and radiologist factors.MethodsThis Institutional Review Board–approved, retrospective study was performed at an urban academic quaternary care hospital. We analyzed 142,001 chest and abdominal CT reports from January 1, 2016, to December 31, 2018, from abdominal, thoracic, and emergency radiology subspecialty divisions. A previously validated natural language processing (NLP) tool identified 24,512 reports documenting pulmonary nodule(s), excluding reports NLP-positive for lung cancer. A second validated NLP tool identified reports with follow-up recommendations specifically for pulmonary nodules. Multivariable logistic regression was used to determine the likelihood of pulmonary nodule follow-up recommendation. Interradiologist variability was quantified within subspecialty divisions.ResultsNLP classified 4,939 of 24,512 (20.1%) reports as having a follow-up recommendation for pulmonary nodule. Male patients comprised 45.3% (11,097) of the patient cohort; average patient age was 61.4 years (±14.1 years). The majority of reports were from outpatient studies (62.7%, 15,376 of 24,512), were chest CTs (75.9%, 18,615 of 24,512), and were interpreted by thoracic radiologists (63.7%, 15,614 of 24,512). In multivariable analysis, studies for male patients (odds ratio [OR]: 0.9 [0.8-0.9]) and abdominal CTs (OR: 0.6 [0.6-0.7] compared with chest CT) were less likely to have a pulmonary nodule follow-up recommendation. Older patients had higher rates of follow-up recommendation (OR: 1.01 for each additional year). Division-level analysis showed up to 4.3-fold difference between radiologists in the probability of making a follow-up recommendation for a pulmonary nodule.DiscussionSignificant differences exist in the probability of making a follow-up recommendation for pulmonary nodules among radiologists within the same subspecialty division.     

Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy

OBJECTIVE: The purpose of our study was to evaluate whether a computer-aided diagnosis (CAD) scheme can assist radiologists in distinguishing small benign from malignant lung nodules on high-resolution CT (HRCT). MATERIALS AND METHODS: We developed an automated computerized scheme for determining the likelihood of malignancy of lung nodules on multiple HRCT slices; the likelihood estimate was obtained from various objective features of the nodules using linear discriminant analysis. The data set used in this observer study consisted of 28 primary lung cancers (6-20 mm) and 28 benign nodules. Cancer cases included nodules with pure ground-glass opacity, mixed ground-glass opacity, and solid opacity. Benign nodules were selected by matching their size and pattern to the malignant nodules. Consecutive region-of-interest images for each nodule on HRCT were displayed for interpretation in stacked mode on a cathode ray tube monitor. The images were presented to 16 radiologists-first without and then with the computer output-who were asked to indicate their confidence level regarding the malignancy of a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: The area under the ROC curve (Az value) of the CAD scheme alone was 0.831 for distinguishing benign from malignant nodules. The average Az value for radiologists was improved with the aid of the CAD scheme from 0.785 to 0.853 by a statistically significant level (p = 0.016). The radiologists' diagnostic performance with the CAD scheme was more accurate than that of the CAD scheme alone (p < 0.05) and also that of radiologists alone. CONCLUSION: CAD has the potential to improve radiologists' diagnostic accuracy in distinguishing small benign nodules from malignant ones on HRCT.     

Pulmonary nodules: detection in 1 and 2 cm full lung linear tomography

Pulmonary nodule detection was evaluated in full lung linear tomography at 1 and 2 cm intervals. Three radiologists independently reviewed 1 and 2 tomograms on 26 patients with 39 pulmonary nodules. Decisions made in each case included: (1) no nodule; (2) definite nodule(s); and (3) suspect nodule(s). The presence of nodules was determined by surgery, radiographic follow-up, or observer consensus. A significantly greater number of nodules was detected by all reviewers on the 1 cm tomograms. Of the 39 nodules, 72%-97% were detected as definite and 82%-100% were identified as definite or suspect. Factors relating to nodule detectability and observer performance are discussed. It is recommended that full lung linear tomography be performed at 1 cm intervals.