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.     

The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation

RATIONALE AND OBJECTIVES: The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers. MATERIALS AND METHODS: Four radiologists reviewed each scan using the following process. In the first or "blinded" phase, each radiologist reviewed the CT scan independently. In the second or "unblinded" review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist's unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading. RESULTS: This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at http://ncia.nci.nih.gov or will be in the near future. CONCLUSIONS: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.     

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.     

Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility,Learning Curves,and Reading Times

Objective

To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT).

Materials and Methods

We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined.

Results

Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, ≤ 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p < 0.001). Area under the curve (AUC) values at the initial session were > 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed.

Conclusion

Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.     

Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database

Objectives

To benchmark the performance of state-of-the-art computer-aided detection (CAD) of pulmonary nodules using the largest publicly available annotated CT database (LIDC/IDRI), and to show that CAD finds lesions not identified by the LIDC’s four-fold double reading process.

Methods

The LIDC/IDRI database contains 888 thoracic CT scans with a section thickness of 2.5 mm or lower. We report performance of two commercial and one academic CAD system. The influence of presence of contrast, section thickness, and reconstruction kernel on CAD performance was assessed. Four radiologists independently analyzed the false positive CAD marks of the best CAD system.

Results

The updated commercial CAD system showed the best performance with a sensitivity of 82 % at an average of 3.1 false positive detections per scan. Forty-five false positive CAD marks were scored as nodules by all four radiologists in our study.

Conclusions

On the largest publicly available reference database for lung nodule detection in chest CT, the updated commercial CAD system locates the vast majority of pulmonary nodules at a low false positive rate. Potential for CAD is substantiated by the fact that it identifies pulmonary nodules that were not marked during the extensive four-fold LIDC annotation process.

Key Points

? CAD systems should be validated on public, heterogeneous databases. ? The LIDC/IDRI database is an excellent database for benchmarking nodule CAD. ? CAD can identify the majority of pulmonary nodules at a low false positive rate. ? CAD can identify nodules missed by an extensive two-stage annotation process.

     

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.     

Pulmonary nodules: experimental and clinical studies at low-dose CT.

PURPOSE: To compare the number of pulmonary nodules detected at helical low- and standard-dose computed tomography (CT) and to investigate the diagnostic value of low-dose CT with a radiation exposure equivalent to that used at chest radiography. MATERIALS AND METHODS: Two radiologists recorded pulmonary nodules at standard-dose (250 or 100 mA, pitch of 1; 200 mA, pitch of 2) or low-dose CT (50 or 25 mA, pitch of 1 or 2) in five postmortem specimens and 75 patients. Nodules were assessed by size (5 mm or smaller, 6-10 mm, or larger than 10 mm) and by diagnostic confidence ("definite nodule," "definite lesion, not classic nodule," or "questionable lesion, possibly representing a vessel") with the Wilcoxon signed rank test. Artifacts depicted at low-dose CT were recorded. RESULTS: There were no statistically significant differences in the number of nodules detected at standard- or low-dose CT except in nodules 5 mm or smaller that were assessed as definite nodules at standard- or low-dose CT (25 mA, pitch of 2) (472 vs 397, P < .05). Artifacts that possibly interfered with nodule detection were observed exclusively at CT with 25 mA and a pitch of 2. CONCLUSION: Pulmonary nodules were detected reliably at CT with 50 mA and pitch of 2 or with 25 mA and a pitch of 1. However, further reduction of the dose to that used at chest radiography was associated with a significant decrease in the number of nodules 5 mm or smaller that were detected, possibly due to artifacts.     

Automated detection of small pulmonary nodules in whole lung CT scans

RATIONALE AND OBJECTIVES: The objective of this work was to develop and evaluate a robust algorithm that automatically detects small solid pulmonary nodules in whole lung helical CT scans from a lung cancer screening study. MATERIALS AND METHODS: We developed a three-stage detection algorithm for both isolated and attached nodules. The algorithm consisted of nodule search space demarcation, nodule candidates' generation, and a sequential elimination of false positives. Isolated nodules are nodules that are surrounded by lung parenchyma, whereas attached nodules are connected to large, dense structures such as pleural and/or mediastinal surface. Two large well-documented whole lung CT scan databases (Databases A and B) were created to train and test the detection algorithm. Database A contains 250 sequentially selected scans with 2.5-mm slice thickness that were obtained at Weill Medical College of Cornell University. With equipment upgrade at this college, a second database, Database B, was created containing 250 scans with a 1.25-mm slice thickness. A total of 395 and 482 nodules were identified in Databases A and B, respectively. In both databases, the majority of the nodules were isolated, comprising 72.1% and 82.3% of nodules in Databases A and B, respectively. RESULTS: The detection algorithm was trained and tested on both Databases A and B. For isolated nodules with sizes 4 mm or larger, the algorithm achieved 94.0% sensitivity and 7.1 false positives per case (FPPC) for Database A (2.5 mm). Similarly, the algorithm achieved 91% sensitivity and 6.9 FPPC for Database B (1.25 mm). The algorithm achieved 92% sensitivity with 17.4 FPPC and 89% sensitivity with 5.5 FFPC for attached nodules with sizes 3 mm or larger in the Database A (2.5 mm) and Database B (1.25 mm), respectively. CONCLUSION: The developed algorithm achieved practical performance for automated detection of both isolated and the more challenging attached nodules. The automated system will be a useful tool to assist radiologists in identifying nodules from whole lung CT scans in a clinical setting.     

Usefulness of the CAD System for Detecting Pulmonary Nodule in Real Clinical Practice

Objective

We wanted to evaluate the usefulness of the computer-aided detection (CAD) system for detecting pulmonary nodules in real clinical practice by using the CT images.

Materials and Methods

Our Institutional Review Board approved our retrospective study with a waiver of informed consent. This study included 166 CT examinations that were performed for the evaluation of pulmonary metastasis in 166 patients with colorectal cancer. All the CT examinations were interpreted by radiologists and they were also evaluated by the CAD system. All the nodules detected by the CAD system were evaluated with regard to whether or not they were true nodules, and they were classified into micronodules (MN, diameter < 4 mm) and significant nodules (SN, 4 ≤ diameter ≤ 10 mm). The radiologic reports and CAD results were compared.

Results

The CAD system helped detect 426 nodules; 115 (27%) of the 426 nodules were classified as true nodules and 35 (30%) of the 115 nodules were SNs, and 83 (72%) of the 115 were not mentioned in the radiologists'' reports and three (4%) of the 83 nodules were non-calcified SNs. One of three non-calcified SNs was confirmed as a metastatic nodule. According to the radiologists'' reports, 60 true nodules were detected, and 28 of the 60 were not detected by the CAD system.

Conclusion

Although the CAD system missed many SNs that are detected by radiologists, it helps detect additional nodules that are missed by the radiologists in real clinical practice. Therefore, the CAD system can be useful to support a radiologist''s detection performance.     

On the choice of acceptance radius in free-response observer performance studies

Objectives

Choosing an acceptance radius or proximity criterion is necessary to analyse free-response receiver operating characteristic (FROC) observer performance data. This is currently subjective, with little guidance in the literature about what is an appropriate acceptance radius. We evaluated varying acceptance radii in a nodule detection task in chest radiography and suggest guidelines for determining an acceptance radius.

Methods

80 chest radiographs were chosen, half of which contained nodules. We determined each nodule''s centre. 21 radiologists read the images. We created acceptance radii bins of <5 pixels, <10 pixels, <20 pixels and onwards up to <200 and 200+ pixels. We counted lesion localisations in each bin and visually compared marks with the borders of nodules.

Results

Most reader marks were tightly clustered around nodule centres, with tighter clustering for smaller than for larger nodules. At least 70% of readers'' marks were placed within <10 pixels for small nodules, <20 pixels for medium nodules and <30 pixels for large nodules. Of 72 inspected marks that were less than 50 pixels from the centre of a nodule, only 1 fell outside the border of a nodule.

Conclusion

The acceptance radius should be based on the larger nodule sizes. For our data, an acceptance radius of 50 pixels would have captured all but 2 reader marks within the borders of a nodule, while excluding only 1 true-positive mark. The choice of an acceptance radius for FROC analysis of observer performance studies should be based on the size of larger abnormalities.Observer performance studies are often used to evaluate imaging systems in medicine. These studies are usually organised so that observers search for a particular abnormality in a set of images, indicate whether the abnormality is present and then do the same thing again on another occasion under different circumstances. There is a variety of ways in which to judge the accuracy of the observers'' responses. Perhaps the most widely used method is one in which observers indicate the presence or absence of the searched-for lesion and the level of confidence with which they have identified or excluded the lesion. From this information, a receiver operating characteristic (ROC) curve is generated [1-5]. The free-response ROC (FROC) method is an alternative approach to analysis that uses lesion location information, and thus more closely mimicks those actual clinical practices that involve a challenging search and yields a higher statistical power than the ROC method [6,7]. Free-response methods also allow separate analysis of success in finding more than one abnormality per image [7]. In the free-response method, the observer locates each lesion, marks it and assigns a confidence rating to each marked lesion. This method is intended to avoid counting a response as correct in situations in which the observer, although correctly indicating the presence of an abnormality in an image actually containing the abnormality, was led astray by a false-positive and did not perceive the true lesion.As the first step in analysing the data from the free-response method, each marked lesion must be scored as either a lesion localisation (LL) or a non-lesion localisation (NLL). An LL is a mark that is close enough to the real lesion to convince the investigator that the reader saw and identified the real lesion. All the other marks that are too far from the real lesion to be scored as LLs are scored as NLLs. What defines “close enough”, though, is an arbitrary decision of the investigator, and to our knowledge there has been only one investigation aimed at defining how close a mark should be to a lesion to be considered an LL [8]. One method used to determine if a mark should be scored as an LL is to select an “acceptance radius”. If the mark falls within the circle whose centre corresponds to the centre of the lesion as predetermined by the investigator and whose radius is the acceptance radius (the acceptance circle), the mark is scored as an LL. Any mark outside of the acceptance circle is scored as an NLL [9]. The purpose of this study was to evaluate the effects of varying the acceptance radius for a nodule detection task in chest radiography in order to suggest guidelines for determining the acceptance radius.     

Lung Nodule Detection on Chest CT: Evaluation of a Computer-Aided Detection (CAD) System

Objective

To evaluate the capacity of a computer-aided detection (CAD) system to detect lung nodules in clinical chest CT.

Materials and Methods

A total of 210 consecutive clinical chest CT scans and their reports were reviewed by two chest radiologists and 70 were selected (33 without nodules and 37 with 1-6 nodules, 4-15.4 mm in diameter). The CAD system (ImageChecker® CT LN-1000) developed by R2 Technology, Inc. (Sunnyvale, CA) was used. Its algorithm was designed to detect nodules with a diameter of 4-20 mm. The two chest radiologists working with the CAD system detected a total of 78 nodules. These 78 nodules form the database for this study. Four independent observers interpreted the studies with and without the CAD system.

Results

The detection rates of the four independent observers without CAD were 81% (63/78), 85% (66/78), 83% (65/78), and 83% (65/78), respectively. With CAD their rates were 87% (68/78), 85% (66/78), 86% (67/78), and 85% (66/78), respectively. The differences between these two sets of detection rates did not reach statistical significance. In addition, CAD detected eight nodules that were not mentioned in the original clinical radiology reports. The CAD system produced 1.56 false-positive nodules per CT study. The four test observers had 0, 0.1, 0.17, and 0.26 false-positive results per study without CAD and 0.07, 0.2, 0.23, and 0.39 with CAD, respectively.

Conclusion

The CAD system can assist radiologists in detecting pulmonary nodules in chest CT, but with a potential increase in their false positive rates. Technological improvements to the system could increase the sensitivity and specificity for the detection of pulmonary nodules and reduce these false-positive results.     

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.     

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.     

Ultra-low-dose MDCT of the chest: influence on automated lung nodule detection.

OBJECTIVE: To evaluate the relationship between CT dose and the performance of a computer-aided diagnosis (CAD) system, and to determine how best to minimize patient exposure to ionizing radiation while maintaining sufficient image quality for automated lung nodule detection, by the use of lung cancer screening CT. MATERIALS AND METHODS: Twenty-five asymptomatic volunteers participated in the study. Each volunteer underwent a low-dose CT scan without contrast enhancement (multidetector CT with 16 detector rows, 1.25 mm section thickness, 120 kVp, beam pitch 1.35, 0.6 second rotation time, with 1.25 mm thickness reconstruction at 1.25 mm intervals) using four different amperages 32, 16, 8, and 4 mAs. All series were analyzed using a commercially available CAD system for automatic lung nodule detection and the results were reviewed by a consensus reading by two radiologists. The McNemar test and Kappa analysis were used to compare differences in terms of the abilities to detect pulmonary nodules. RESULTS: A total of 78 non-calcified true nodules were visualized in the 25 study subjects. The sensitivities for nodule detection were as follows: 72% at 32 mAs, 64% at 16 mAs, 59% at 8 mAs, and 40% at 4 mAs. Although the overall nodule-detecting performance was best at 32 mAs, no significant difference in nodule detectability was observed between scans at 16 mAs or 8 mAs versus 32 mAs. However, scans performed at 4 mAs were significantly inferior to those performed at 32 mAs (p < 0.001). CONCLUSION: Reducing the radiation dose (i.e. reducing the amperage) lowers lung nodule detectability by CAD. However, relatively low dose scans were found to be acceptable and to cause no significant reduction in nodule detectability versus usual low-dose CT.     

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.     

Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study

Purpose

To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry.

Materials and methods

CT scans were performed on a chest phantom containing various nodules (10 and 12 mm; +100, −630 and −800 HU) at 120 kVp with tube current–time settings of 10, 20, 50, and 100 mAs. Each CT was reconstructed using filtered back projection (FBP), iDose⁴ and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations.

Results

Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p > 0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose⁴ at all radiation dose settings (p < 0.05).

Conclusion

Semi-automated nodule volumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility.     

Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader

The purpose of this study was to compare sensitivity for detection of pulmonary nodules in MDCT scans and reading time of radiologists when using CAD as the second reader (SR) respectively concurrent reader (CR). Four radiologists analyzed 50 chest MDCT scans chosen from clinical routine two times and marked all detected pulmonary nodules: first with CAD as CR (display of CAD results immediately in the reading session) and later (median 14 weeks) with CAD as SR (display of CAD markers after completion of first reading without CAD). A Siemens LungCAD prototype was used. Sensitivities for detection of nodules and reading times were recorded. Sensitivity of reading with CAD as SR was significantly higher than reading without CAD (p < 0.001) and CAD as CR (p < 0.001). For nodule size of 1.75 mm or above no significant sensitivity difference between CAD as CR and reading without CAD was observed; e.g., for nodules above 4 mm sensitivity was 68% without CAD, 68% with CAD as CR (p = 0.45) and 75% with CAD as SR (p < 0.001). Reading time was significantly shorter for CR (274 s) compared to reading without CAD (294 s; p = 0.04) and SR (337 s; p < 0.001). In our study CAD could either speed up reading of chest CT cases for pulmonary nodules without relevant loss of sensitivity when used as CR, or it increased sensitivity at the cost of longer reading times when used as SR.     

Multiple pulmonary nodules in AIDS: usefulness of CT in distinguishing among potential causes

PURPOSE: To determine whether the computed tomographic (CT) appearances of multiple pulmonary nodules in patients with acquired immunodeficiency syndrome (AIDS) can help differentiate the potential infectious and neoplastic causes. MATERIALS AND METHODS: The thoracic CT scans obtained in 60 patients with AIDS and multiple pulmonary nodules were reviewed retrospectively by two thoracic radiologists who were blinded to clinical and pathologic data. The scans were evaluated for nodule size, distribution, and morphologic characteristics. CT findings were correlated with final diagnoses. RESULTS: Thirty-six (84%) of 43 patients with opportunistic infection had a predominance of nodules smaller than 1 cm in diameter, whereas 14 (82%) of 17 patients with a neoplasm had a predominance of nodules larger than 1 cm (P <.001). Of the 43 patients with opportunistic infection, 28 (65%) had a centrilobular distribution of nodules; only one (6%) of 17 patients with a neoplasm had this distribution (P <.001). Seven (88%) of eight patients with a peribronchovascular distribution had Kaposi sarcoma (P <.001). CONCLUSION: In patients with AIDS who have multiple pulmonary nodules at CT, nodule size and distribution are useful in the differentiation of potential causes. Nodules smaller than 1 cm, especially those with a centrilobular distribution, are typically infectious. Nodules larger than 1 cm are often neoplastic. A peribronchovascular distribution is suggestive of Kaposi sarcoma.     

Accuracy of transmission CT and FDG-PET in the detection of small pulmonary nodules with integrated PET/CT

Purpose The purpose of this study was to determine the accuracy of detection of small pulmonary nodules on quiet breathing attenuation correction CT (CTAC) and FDG-PET when performing integrated PET/CT, as compared with a diagnostic inspiratory CT scan acquired in the same imaging session.Methods PET/CT scans of 107 patients with a history of carcinoma (54 male and 53 female, mean age 57.3 years) were analyzed. All patients received an integrated PET/CT scan including a CTAC acquired during quiet respiration and a contrast-enhanced CT acquired during inspiration in the same session. Breathing CTAC scans were reviewed by two thoracic radiologists for the presence of pulmonary nodules. FDG-PET scans were reviewed to determine accuracy of nodule detection. Diagnostic CT was used as the gold standard to confirm or refute the presence of nodules.Results On the CTAC scans 200 nodules were detected, of which 183 were true positive (TP) and 17, false positive. There were 109 false negatives (FN). Overall, 51 (48%) patients had a false interpretation, including 19 in whom CT was interpreted as normal for lung nodules. The average size of the nodules missed was 3.8±2 mm (range 2–12 mm). None of the nodules missed on the CTAC scans were detected by PET. In the right lung there were 20 TP, 42 true negative (TN), 11 FP, and 34 FN interpretations with a sensitivity in nodule detection of 37% (CI 24–51%) and a specificity of 79% (CI 66–89%). In the left lungs there were 16 TP, 65 TN, 3 FP, and 23 FN interpretations, with a sensitivity of 41% (CI 26–58%) and a specificity of 96% (CI 88–99%).Conclusion The detection of small pulmonary nodules by breathing CTAC and FDG-PET is relatively poor. Therefore an additional diagnostic thoracic CT scan obtained during suspended inspiration is recommended for thorough evaluation of those patients in whom detection of pulmonary metastases is necessary for management.