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Objectives The individual course of disease in idiopathic pulmonary fibrosis (IPF) is highly variable. Assessment of disease activity and prospective estimation of disease progression might have the potential to improve therapy management and indicate the onset of treatment at an earlier stage. The aim of this study was to evaluate whether regional ventilation, lung perfusion, and late enhancement can serve as early imaging markers for disease progression in patients with IPF. Methods In this retrospective study, contrast-enhanced dual-energy CT scans of 32 patients in inspiration and delayed expiration were performed at two time points with a mean interval of 15.4 months. The pulmonary blood volume (PBV) images obtained in the arterial and delayed perfusion phase served as a surrogate for arterial lung perfusion and parenchymal late enhancement. The virtual non-contrast (VNC) images in inspiration and expiration were non-linearly registered to provide regional ventilation images. Image-derived parameters were correlated with longitudinal changes of lung function (FVC%, DLCO%), mean lung density in CT, and CT-derived lung volume. Results Regional ventilation and late enhancement at baseline preceded future change in lung volume (R - 0.474, p 0.006/R - 0.422, p 0.016, respectively) and mean lung density (R - 0.469, p 0.007/R - 0.402, p 0.022, respectively). Regional ventilation also correlated with a future change in FVC% (R - 0.398, p 0.024). Conclusion CT-derived functional parameters of regional ventilation and parenchymal late enhancement are potential early imaging markers for idiopathic pulmonary fibrosis progression. Key Points • Functional CT parameters at baseline (regional ventilation and late enhancement) correlate with future structural changes of the lung as measured with loss of lung volume and increase in lung density in serial CT scans of patients with idiopathic pulmonary fibrosis. • Functional CT parameter measurements in high-attenuation areas (- 600 to - 250 HU) are significantly different from normal-attenuation areas (- 950 to - 600 HU) of the lung. • Mean regional ventilation in functional CT correlates with a future change in forced vital capacity (FVC) in pulmonary function tests.

Background A categorical CT assessment scheme for suspicion of pulmonary involvement of coronavirus disease 2019 (COVID-19 provides a basis for gathering scientific evidence and improved communication with referring physicians. Purpose To introduce the COVID-19 Reporting and Data System (CO-RADS) for use in the standardized assessment of pulmonary involvement of COVID-19 on unenhanced chest CT images and to report its initial interobserver agreement and performance. Materials and Methods The Dutch Radiological Society developed CO-RADS based on other efforts for standardization, such as the Lung Imaging Reporting and Data System or Breast Imaging Reporting and Data System. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients with moderate to severe symptoms of COVID-19. The system was evaluated by using 105 chest CT scans of patients admitted to the hospital with clinical suspicion of COVID-19 and in whom reverse transcription-polymerase chain reaction (RT-PCR) was performed (mean, 62 years ± 16 [standard deviation]; 61 men, 53 with positive RT-PCR results). Eight observers used CO-RADS to assess the scans. Fleiss κ value was calculated, and scores of individual observers were compared with the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared with results from RT-PCR and clinical diagnosis of COVID-19. Results There was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss κ value was 0.47 (95% confidence interval [CI]: 0.45, 0.47), with the highest κ value for CO-RADS categories 1 (0.58, 95% CI: 0.54, 0.62) and 5 (0.68, 95% CI: 0.65, 0.72). The average AUC was 0.91 (95% CI: 0.85, 0.97) for predicting RT-PCR outcome and 0.95 (95% CI: 0.91, 0.99) for clinical diagnosis. The false-negative rate for CO-RADS 1 was nine of 161 cases (5.6%; 95% CI: 1.0%, 10%), and the false-positive rate for CO-RADS category 5 was one of 286 (0.3%; 95% CI: 0%, 1.0%). Conclusion The coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) is a categorical assessment scheme for pulmonary involvement of COVID-19 at unenhanced chest CT that performs very well in predicting COVID-19 in patients with moderate to severe symptoms and has substantial interobserver agreement, especially for categories 1 and 5. © RSNA, 2020 Online supplemental material is available for this article.

BackgroundA baseline computed tomography (CT) scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC risk and a high CD risk.MethodsParticipant demographics and quantitative CT measures of LC, cardiovascular disease and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting 5-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data were used to perform external validation (n=2287).ResultsOur final CD model outperformed an external pre-scan model (CD Risk Assessment Tool) in both the derivation (area under the curve (AUC) 0.744 (95% CI 0.727–0.761) and 0.677 (95% CI 0.658–0.695), respectively) and validation cohorts (AUC 0.744 (95% CI 0.652–0.835) and 0.725 (95% CI 0.633–0.816), respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096 (27%)) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287 (34%)) were 129 (versus 29) and 1.67 (versus 0.43).ConclusionsEvaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.

Purpose To microsimulate the effects of three additional annual CT screening rounds on lung cancer (LC) survival in the National Lung Screening Trial (NLST). Methods We used multiple imputation to model the effect of additional screening in the full NLST cohort on the time to LC diagnosis and on LC death in those participants who were diagnosed with LC by the end of NLST. Nodule growth models were derived from a Dutch in-vivo study. Microsimulations were repeated 500 times. The method was validated by simulating three rounds of CT screening in the original chest radiography (CXR) cohort. The times up to which the simulations remained within the 95 % confidence bands of the CT cohort’s original results were used to estimate the validity of the results in the CT cohort with three additional simulated screening rounds. Results Validation of the simulation approach on the CXR cohort resulted in a LC mortality reduction which remained well within the 95 % confidence intervals of the original CT cohort up to 6.5 years after the start of simulations. Simulating additional CT screening in the CT cohort led to LCs being diagnosed earlier than originally, resulting in a relative risk reduction in LC mortality of 11 % (95 % confidence bands, 7 %–14 %) at 6.5 years. This is equivalent to preventing 71 % (48 %–94 %) more LC deaths than the original CT cohort achieved in comparison to the original CXR cohort. Conclusion Three additional annual CT screening rounds in the NLST may have led to substantial further LC mortality reduction.

ObjectivesThe 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS).MethodsSubjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden.ResultsIn 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p< 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p= 0.0068) and an OR of 6.35 at 3 years (p= 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6–21.9% and a potential reduction of LDCT burden of 25.5–41%.ConclusionsNodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible.Key Points• Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT.• Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group.• Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5–41% reduction of LDCT burden, at the cost of 13.6–21.9% rate of delayed diagnosis.

An observer study with seven radiologists showed that using a dedicated CT lung screening viewer (with computer-aided detection, a segmentation algorithm, and automatic Lung Imaging Reporting and Data System [Lung-RADS] categorization) improved interobserver agreement for Lung-RADS categorization while substantially reducing the reading time. Purpose. To compare the inter- and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung cancer screening studies by using a dedicated CT lung screening viewer with integrated nodule detection and volumetric support with those achieved by using a standard picture archiving and communication system (PACS)-like viewer. Materials and Methods. Data were obtained from the National Lung Screening Trial (NLST). By using data recorded by NLST radiologists, scans were assigned to Lung-RADS categories. For each Lung-RADS category (1 or 2, 3, 4A, and 4B), 40 CT scans (20 baseline scans and 20 follow-up scans) were randomly selected for 160 participants (median age, 61 years; interquartile range, 58-66 years; 61 women) in total. Seven blinded observers independently read all CT scans twice in a randomized order with a 2-week washout period: once by using the standard PACS-like viewer and once by using the dedicated viewer. Observers were asked to assign a Lung-RADS category to each scan and indicate the risk-dominant nodule. Inter- and intraobserver agreement was analyzed by using Fleiss k values and Cohen weighted k values, respectively. Reading times were compared by using a Wilcoxon signed rank test. Results. The interobserver agreement was moderate for the standard viewer and substantial for the dedicated viewer, with Fleiss k values of 0.58 (95% CI: 0.55, 0.60) and 0.66 (95% CI: 0.64, 0.68), respectively. The intraobserver agreement was substantial, with a mean Cohen weighted k value of 0.67. The median reading time was significantly reduced from 160 seconds with the standard viewer to 86 seconds with the dedicated viewer (P < .001). Conclusion. Lung-RADS interobserver agreement increased from moderate to substantial when using the dedicated CT lung screening viewer. The median reading time was substantially reduced when scans were read by using the dedicated CT lung screening viewer.

ObjectivesCombined assessment of cardiovascular disease (CVD), COPD and lung cancer may improve the effectiveness of lung cancer screening in smokers. The aims were to derive and assess risk models for predicting lung cancer incidence, CVD mortality and COPD mortality by combining quantitative computed tomography (CT) measures from each disease, and to quantify the added predictive benefit of self-reported patient characteristics given the availability of a CT scan.MethodsA survey model (patient characteristics only), CT model (CT information only) and final model (all variables) were derived for each outcome using parsimonious Cox regression on a sample from the National Lung Screening Trial (n=15 000). Validation was performed using Multicentric Italian Lung Detection data (n=2287). Time-dependent measures of model discrimination and calibration are reported.ResultsAge, mean lung density, emphysema score, bronchial wall thickness and aorta calcium volume are variables that contributed to all final models. Nodule features were crucial for lung cancer incidence predictions but did not contribute to CVD and COPD mortality prediction. In the derivation cohort, the lung cancer incidence CT model had a 5-year area under the receiver operating characteristic curve of 82.5% (95% CI 80.9–84.0%), significantly inferior to that of the final model (84.0%, 82.6–85.5%). However, the addition of patient characteristics did not improve the lung cancer incidence model performance in the validation cohort (CT model 80.1%, 74.2–86.0%; final model 79.9%, 73.9–85.8%). Similarly, the final CVD mortality model outperformed the other two models in the derivation cohort (survey model 74.9%, 72.7–77.1%; CT model 76.3%, 74.1–78.5%; final model 79.1%, 77.0–81.2%), but not the validation cohort (survey model 74.8%, 62.2–87.5%; CT model 72.1%, 61.1–83.2%; final model 72.2%, 60.4–84.0%). Combining patient characteristics and CT measures provided the largest increase in accuracy for the COPD mortality final model (92.3%, 90.1–94.5%) compared to either other model individually (survey model 87.5%, 84.3–90.6%; CT model 87.9%, 84.8–91.0%), but no external validation was performed due to a very low event frequency.ConclusionsCT measures of CVD and COPD provides small but reproducible improvements to nodule-based lung cancer risk prediction accuracy from 3 years onwards. Self-reported patient characteristics may not be of added predictive value when CT information is available.

Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years ± 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. © RSNA, 2020

Purpose: To study interreader variability for classifying pulmonary opacities at CT as perifissural nodules (PFNs) and determine how reliably radiologists differentiate PFNs from malignancies. Materials and Methods: CT studies were obtained retrospectively from the National Lung Screening Trial (2002-2009). Nodules were eligible for the study if they were noncalcified, solid, within the size range of 5 to 10 mm, and scanned with a section thickness of 2 mm or less. Six radiologists classified 359 nodules in a cancer-enriched data set as PFN, non-PFN, or not applicable. Nodules classified as not applicable by at least three radiologists were excluded, leaving 316 nodules for post-hoc statistical analysis. Results: The study group contained 22.2% cancers (70 of 316). The median proportion of nodules classified as PFNs was 45.6% (144 of 316). All six radiologists uniformly classified 17.7% (56 of 316) of the nodules as PFNs. The Fleiss k was 0.50. Compared with non-PFNs, nodules classified as PFNs were smaller and more often located in the lower lobes and attached to a fissure (P , .001). Thirteen (18.6%) of 70 cancers were misclassified 21 times as PFNs. Individual readers' misclassification rates ranged from 0% (0 of 125) to 4.9% (eight of 163). Of 13 misclassified malignancies, 11 were in the upper lobes and two were attached to a fissure. Conclusion: There was moderate interreader agreement when classifying nodules as perifissural nodules. Less than 2.5% of perifissural nodule classifications were misclassified lung cancers (21 of 865) in this cancer-enriched study. Allowing nodules classified as perifissural nodules to be omitted from additional follow-up in a screening setting could substantially reduce the number of unnecessary scans; excluding perifissural nodules in the upper lobes would greatly decrease the misclassification rate.

Objective To compare three different reconstruction techniques of CT data for the detection of pulmonary nodules in children under 13 years. Secondly to assess the prevalence of perifissural nodular opacities. Materials and methods The study consisted of chest CTs of 31 children (median age 6.9 years, range 2.1–12.7), of whom 17 had known extra-thoracic malignancies. Four observers assessed three techniques for the presence of nodules: axial 5 mm maximum intensity projections (MIPs) used in conjunction with 1 mm slices (mode A), 1 mm slices alone (mode B) and 3 mm slices (mode C). All modes were available in 3D. Per mode sensitivities were determined above a certain threshold of reader agreement. Confidence level and reader agreement for identification of an opacity as nodule served as surrogate for quality of nodule characterization. Results 103 nodules (median size 2.0 mm) were detected. Mode A yielded the highest interreader agreement (κ 0.336) and a superior sensitivity (71%, p = 0.003) compared to mode B and C (κ 0.218, sensitivity 57% and κ 0.247, sensitivity 56%, respectively). Mode B provided the highest confidence level and interreader agreement with respect to nodule identification (mean 4.3/5, κw 0.508). Double reading improved and evened interreader agreement for all modes (κ 0.450), mode A maintained the highest sensitivity (89.1%, p = 0.05–0.08). A median of 1 intrapulmonary lymph node/patient was seen in children with and without malignancy. Conclusion MIP improves the detection of pulmonary nodules in chest CTs of children, but overall interreader agreement is only fair. Double reading represents a powerful tool to increase diagnostic reliability in chest CTs of children with a malignancy. Nodule characterization is best with 1 mm slices. Intrapulmonary lymph nodes occur in children with and without malignancy.

These recommendations for measuring pulmonary nodules at computed tomography (CT) are a statement from the Fleischner Society and, as such, incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. The recommendations address nodule size measurements at CT, which is a topic of importance, given that all available guidelines for nodule management are essentially based on nodule size or changes thereof. The recommendations are organized according to practical questions that commonly arise when nodules are measured in routine clinical practice and are, together with their answers, summarized in a table. The recommendations include technical requirements for accurate nodule measurement, directions on how to accurately measure the size of nodules at the workstation, and directions on how to report nodule size and changes in size. The recommendations are designed to provide practical advice based on the available evidence from the literature; however, areas of uncertainty are also discussed, and topics needing future research are highlighted. © RSNA, 2017 Online supplemental material is available for this article.

Contains fulltext : 181941.pdf (Publisher’s version ) (Open Access) OBJECTIVES: To compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from benign pulmonary nodules on baseline screening CT scans and the impact diameter measurement methods have on performances. METHODS: From the Danish Lung Cancer Screening Trial database, 64 CTs with malignant nodules and 549 baseline CTs with benign nodules were included. Performance of the systems was evaluated applying the system's original diameter definitions: D(longest-C) (PanCan), D(meanAxial) (NCCN), both obtained from axial sections, and D(mean3D) (Lung-RADS). Subsequently all diameter definitions were applied uniformly to all systems. Areas under the ROC curves (AUC) were used to evaluate risk discrimination. RESULTS: PanCan performed superiorly to Lung-RADS and NCCN (AUC 0.874 vs. 0.813, p = 0.003; 0.874 vs. 0.836, p = 0.010), using the original diameter specifications. When uniformly applying D(longest-C), D(mean3D) and D(meanAxial), PanCan remained superior to Lung-RADS (p < 0.001 - p = 0.001) and NCCN (p < 0.001 - p = 0.016). Diameter definition significantly influenced NCCN's performance with D(longest-C) being the worst (D(longest-C) vs. D(mean3D), p = 0.005; D(longest-C) vs. D(meanAxial), p = 0.016). CONCLUSIONS: Without follow-up information, the PanCan model performs significantly superiorly to Lung-RADS and the 1.2016 NCCN guidelines for discriminating benign from malignant nodules. The NCCN guidelines are most sensitive to nodule size definition. KEY POINTS: * PanCan model outperforms Lung-RADS and 1.2016 NCCN guidelines in identifying malignant pulmonary nodules. * Nodule size definition had no significant impact on Lung-RADS and PanCan model. * 1.2016 NCCN guidelines were significantly superior when using mean diameter to longest diameter. * Longest diameter achieved lowest performance for all models. * Mean diameter performed equivalently when derived from axial sections and from volumetry.

Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The purpose of the study was to quantify how well readers agree in assigning Lung-RADS categories to screening CTs; secondary goals were to assess causes of disagreement and evaluate its impact on patient management. For the observer study, 80 baseline and 80 follow-up scans were randomly selected from the NLST trial covering all Lung-RADS categories in an equal distribution. Agreement of seven observers was analysed using Cohen’s kappa statistics. Discrepancies were correlated with patient management, test performance and diagnosis of malignancy within the scan year. Pairwise interobserver agreement was substantial (mean kappa 0.67, 95% CI 0.58–0.77). Lung-RADS category disagreement was seen in approximately one-third (29%, 971) of 3360 reading pairs, resulting in different patient management in 8% (278/3360). Out of the 91 reading pairs that referred to scans with a tumour diagnosis within 1 year, discrepancies in only two would have resulted in a substantial management change. Assignment of lung cancer screening CT scans to Lung-RADS categories achieves substantial interobserver agreement. Impact of disagreement on categorisation of malignant nodules was low. • Lung-RADS categorisation of low-dose lung screening CTs achieved substantial interobserver agreement. • Major cause for disagreement was assigning a different nodule as risk-dominant. • Disagreement led to a different follow-up time in 8% of reading pairs.

Overall survival of patients with cancer continues to increase and so they receive more frequent CT imaging, making oncological patients a growing population that effectively receives lung cancer screening in the course of daily practice. However, it is currently uncertain how early lung cancer detection in this subgroup of patients should be optimally managed. We describe the relationship between primary lung cancer and prior malignancies in a nationwide cohort, in an attempt to identify possible areas of improvement in nodule management. We found that a substantial number of subjects with lung cancer suffered from a prior malignancy; however, with the exception of otorhinolaryngeal malignancies, they did not show a high absolute risk for lung cancer. Future research should provide more data on how to handle this subgroup of patients in clinical and screening setting.

PurposeNormalized emphysema score is a protocol-robust CT biomarker of mortality. We aimed to improve mortality prediction by including the emphysema score progression rate-its change over time-into the models.Method and materialsCT scans from 6000 National Lung Screening Trial CT arm participants were included. Of these, 1810 died (445 lung cancer-specific). The remaining 4190 survivors were sampled with replacement up to 24432 to approximate the full cohort. Three overlapping subcohorts were formed which required participants to have images from specific screening rounds. Emphysema scores were obtained after resampling, normalization, and bullae cluster analysis of the original images. Base models contained solely the latest emphysema score. Progression models included emphysema score progression rate. Models were adjusted by including baseline age, sex, BMI, smoking status, smoking intensity, smoking duration, and previous COPD diagnosis. Cox proportional hazard models predicting all-cause and lung cancer mortality were compared by calculating the area under the curve per year follow-up.ResultsIn the subcohort of participants with baseline and first annual follow-up scans, the analysis was performed on 4940 participants (23227 after resampling). Area under the curve for all-cause mortality predictions of the base and progression models 6 years after baseline were 0.564 (0.564 to 0.565) and 0.569 (0.568 to 0.569) when unadjusted, and 0.704 (0.703 to 0.704) to 0.705 (0.704 to 0.705) when adjusted. The respective performances predicting lung cancer mortality were 0.638 (0.637 to 0.639) and 0.643 (0.642 to 0.644) when unadjusted, and 0.724 (0.723 to 0.725) and 0.725 (0.725 to 0.726) when adjusted.ConclusionIncluding emphysema score progression rate into risk models shows no clinically relevant improvement in mortality risk prediction. This is because scan normalization does not adjust for an overall change in lung density. Adjusting for changes in smoking behavior is likely required to make this a clinically useful measure of emphysema progression.

Contains fulltext : 171478.pdf (Publisher’s version ) (Open Access) The textural patterns in the lung parenchyma, as visible on computed tomography (CT) scans, are essential to make a correct diagnosis in interstitial lung disease. We developed one automatic and two interactive protocols for classification of normal and seven types of abnormal lung textures. Lungs were segmented and subdivided into volumes of interest (VOIs) with homogeneous texture using a clustering approach. In the automatic protocol, VOIs were classified automatically by an extra-trees classifier that was trained using annotations of VOIs from other CT scans. In the interactive protocols, an observer iteratively trained an extra-trees classifier to distinguish the different textures, by correcting mistakes the classifier makes in a slice-by-slice manner. The difference between the two interactive methods was whether or not training data from previously annotated scans was used in classification of the first slice. The protocols were compared in terms of the percentages of VOIs that observers needed to relabel. Validation experiments were carried out using software that simulated observer behavior. In the automatic classification protocol, observers needed to relabel on average 58\% of the VOIs. During interactive annotation without the use of previous training data, the average percentage of relabeled VOIs decreased from 64\% for the first slice to 13\% for the second half of the scan. Overall, 21\% of the VOIs were relabeled. When previous training data was available, the average overall percentage of VOIs requiring relabeling was 20\%, decreasing from 56\% in the first slice to 13\% in the second half of the scan.

Item does not contain fulltext Purpose To quantify the reproducibility and accuracy of experienced thoracic radiologists in differentiating between subsolid and solid pulmonary nodules at CT. Materials and Methods The institutional review board of Beth Israel Deaconess Medical Center approved this multicenter study. Six thoracic radiologists, with a mean of 21 years of experience in thoracic radiology (range, 17-22 years), selected images of 10 solid and 10 subsolid nodules to create a database of 120 nodules; this selection served as the reference standard. Each radiologist then interpreted 120 randomly ordered nodules in two different sessions that were separated by a minimum of 3 weeks. The radiologists classified whether or not each nodule was subsolid. Inter- and intraobserver agreement was assessed with a κ statistic. The number of correct classifications was calculated and correlated with nodule size by using Bland-Altman plots. The relationship between disagreement and nodule morphologic characteristics was analyzed by calculating the intraclass correlation coefficient. Results Interobserver agreement (κ) was 0.619 (range, 0.469-0.745; 95\% confidence interval (CI): 0.576, 0.663) and 0.670 (range, 0.440-0.839; 95\% CI: 0.608, 0.733) for interpretation sessions 1 and 2, respectively. Intraobserver agreement (κ) was 0.792 (95\% CI: 0.750, 0.833). Averaged for interpretation sessions, correct classification was achieved by all radiologists for 58\% (70 of 120) of nodules. Radiologists agreed with their initial determination (the reference standard) in 77\% of cases (range, 45\%-100\%). Nodule size weakly correlated with correct classification (long axis: Spearman rank correlation coefficient, rs = 0.161 and P = .049; short axis: rs = 0.128 and P = .163). Conclusion The reproducibility and accuracy of thoracic radiologists in classifying whether or not a nodule is subsolid varied in the retrospective study. This inconsistency may affect surveillance recommendations and prognostic determinations. (©) RSNA, 2015 Online supplemental material is available for this article.

Contains fulltext : 167509.pdf (Publisher’s version ) (Open Access) PURPOSE: The aim of the study was to investigate the effect of bone-suppressed chest radiographs on the detection of common chest abnormalities. MATERIALS AND METHODS: A total of 261 posteroanterior and lateral chest radiographs were collected from 2 hospitals. Radiographs could contain single or multiple focal opacities 3 cm (n=33), diffuse lung disease (n=49), signs of cardiogenic congestion (n=26), or no abnormalities (n=110). Twenty-one cases contained >1 type of disease. All abnormalities were confirmed by a computed tomographic scan obtained within 4 weeks of the radiograph. Bone-suppressed images (BSIs) were generated from every posteroanterior radiograph (ClearRead BSI 3.2). All cases were read by 6 radiologists without BSI, followed by an evaluation of the same case with BSI. Presence or absence of each disease category and confidence (0-100) of the observers were documented for each interpretation. Differences in the number of correct detections without and with BSI were analyzed using the Wilcoxon signed-rank test. RESULTS: On average, 6 more cases with focal lesions were correctly identified with BSI (P=0.03), and 1 additional case with diffuse abnormalities was found with BSI (P=0.32). None of the observers demonstrated a decrease in the number of correctly detected cases with diffuse abnormalities or cardiogenic congestion with BSI. False positives in normal cases with availability of BSI mainly referred to the detection of small focal lesions (on average 7 per reader; P=0.04). CONCLUSIONS: BSI does not negatively affect the interpretation of diffuse lung disease, while improving visualization of focal lesions on chest radiographs. BSI leads to overcalling of focal abnormalities in normal radiographs.

Contains fulltext : 172119.pdf (Publisher’s version ) (Closed access) BACKGROUND: Currently 80% of donor lungs are not accepted for transplantation, often due to fluid overload. Our aim was to investigate if forced fluid infusion may be replaced by a new pharmacological therapy to stabilize circulation after brain death in an animal model, and to assess therapy effects on lung function and morphology trough blood gas parameters and state-of-the-art High-resolution CT (HRCT). METHODS: Brain death was caused by surgical decapitation. To maintain mean aortic pressure > 60 mmHg, pigs were treated with forced electrolyte solution infusion (GI; n = 6) or the pharmacological therapy (GII; n = 11). GIII (n = 11) were non-decapitated controls. Lung function was investigated with blood gases and lung morphology with HRCT. RESULTS: GI pigs became circulatory instable 4-6 h after brain death in spite of forced fluid infusion, five pigs showed moderate to severe pulmonary edema on HRCT and median final PaO2 /FiO2 was 29 kPa (Q1; Q3; range 26; 40; 17-76). GII and GIII were circulatory stable (mean aortic pressure > 80 mmHg) and median final PaO2 /FiO2 after 24 h was 72 kPa (Q1; Q3; range 64; 76; 53-91) (GII) and 66 kPa (55; 78; 43-90) (GIII). On HRCT, only two pigs in GII had mild pulmonary edema and none in GIII. More than 50% of HRCT exams revealed unexpected lung disease even in spite of PaO2 /FiO2 > 40 kPa. CONCLUSION: Pharmacological therapy but not forced fluid infusion prevented circulatory collapse and extensive HRCT verified pulmonary edema after acute brain death. HRCT was useful to evaluate lung morphology and revealed substantial occult parenchymal changes justifying efforts toward a more intense use of HRCT in the pre-transplant evaluation.

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

Contains fulltext : 193523.pdf (Publisher’s version ) (Open Access) OBJECTIVES: The aim of this study was to compare computer-aided diagnosis (CAD) and visual reading for the detection of subsolid nodules (SSNs) in volumetrl measuremic low-dose computed tomography (LDCT) for lung cancer screening. MATERIALS AND METHODS: Prospective visual detection (VD) and manuaent of SSN were performed in the 2303 baseline volumetric LDCTs of the Multicenter Italian Lung Detection trial. Baseline and 2- and 4-year LDCTs underwent retrospective CAD analysis, subsequently reviewed by 2 experienced thoracic radiologists. The reference standard was defined by the cumulative number of SSNs detected by any reading method between VD and CAD. The number of false-positive CAD marks per scan (FP/scan) was calculated. The positive predictive value of CAD was quantified per nodule (PPV) and per screenee (PPV). The sensitivity and negative predictive value were compared between CAD and VD. The longitudinal 3-time-point sensitivity of CAD was calculated in the subgroup of persistent SSNs seen by VD (ratio between the prevalent SSNs detected by CAD through 3 time points and the total number of persistent prevalent SSNs detected by VD) to test the sensitivity of iterated CAD analysis during a screening program. Semiautomatic characteristics (diameter, volume, and mass; both for whole nodule and solid component) were compared between SSN detected CAD-only or VD-only to investigate whether either reading method could suffer from specific sensitivity weakness related to SSN features. Semiautomatic and manual diameters were compared using Spearman rho correlation and Bland-Altman plot. RESULTS: Computer-aided diagnosis and VD detected a total of 194 SSNs in 6.7% (155/2,303) of screenees at baseline LDCT. The CAD showed mean FP/scan of 0.26 (604/2,303); PPV 22.5% (175/779) for any SSN, with 54.4% (37/68) for PSN and 19.4% for NSN (138/711; P < 0.001); PPV 25.6% (137/536). The sensitivity of CAD was superior to that of VD (88.4% and 34.2%, P < 0.001), as well as negative predictive value (99.2% and 95.5%, P < 0.001). The longitudinal 3-time-point sensitivity of CAD was 87.5% (42/48). There was no influence of semiautomatic characteristics on the performance of either reading method. The diameter of the solid component in PSN was larger by CAD compared with manual measurement. At baseline, CAD detected 3 of 4 SSNs, which were first overlooked by VD and subsequently evolved to lung cancer. CONCLUSIONS: Computer-aided diagnosis and VD as concurrent reading methods showed complementary performance, with CAD having a higher sensitivity, especially for PSN, but requiring visual confirmation to reduce false-positive calls. Computer-aided diagnosis and VD should be jointly used for LDCT reading to reduce false-negatives of either lone method. The semiautomatic measurement of solid core showed systematic shift toward a larger diameter, potentially resulting in an up-shift within Lung CT Screening Reporting and Data System classification.

Purpose To evaluate whether, and to which extent, experienced radiologists are able to visually correctly differentiate transient from persistent subsolid nodules from a single CT examination alone and to determine CT morphological features to make this differentiation. Materials and methods We selected 86 transient and 135 persistent subsolid nodules from the National Lung Screening Trial (NLST) database. Four experienced radiologists visually assessed a predefined list of morphological features and gave a final judgment on a continuous scale (0-100). To assess observer performance, area under the receiver operating characteristic (ROC) curve was calculated. Statistical differences of morphological features between transient and persistent lesions were calculated using Chi-square. Inter-observer agreement of morphological features was evaluated by percentage agreement. Results Forty-nine lesions were excluded by at least 2 observers, leaving 172 lesions for analysis. On average observers were able to differentiate transient from persistent subsolid nodules ≥ 10 mm with an area under the curve of 0.75 (95% CI 0.67-0.82). Nodule type, lesion margin, presence of a well-defined border, and pleural retraction showed significant differences between transient and persistent lesions in two observers. Average pair-wise percentage agreement for these features was 81%, 64%, 47% and 89% respectively. Agreement for other morphological features varied from 53% to 95%. Conclusion The visual capacity of experienced radiologists to differentiate persistent and transient subsolid nodules is moderate in subsolid nodules larger than 10 mm. Performance of the visual assessment of CT morphology alone is not sufficient to generally abandon a short-term follow-up for subsolid nodules.

PURPOSE: To compare human observers to a mathematically derived computer model for differentiation between malignant and benign pulmonary nodules detected on baseline screening computed tomography (CT) scans.METHODS: A case-cohort study design was chosen. The study group consisted of 300 chest CT scans from the Danish Lung Cancer Screening Trial (DLCST). It included all scans with proven malignancies (n = 62) and two subsets of randomly selected baseline scans with benign nodules of all sizes (n = 120) and matched in size to the cancers, respectively (n = 118). Eleven observers and the computer model (PanCan) assigned a malignancy probability score to each nodule. Performances were expressed by area under the ROC curve (AUC). Performance differences were tested using the Dorfman, Berbaum and Metz method. Seven observers assessed morphological nodule characteristics using a predefined list. Differences in morphological features between malignant and size-matched benign nodules were analyzed using chi-square analysis with Bonferroni correction. A significant difference was defined at p < 0.004.RESULTS: Performances of the model and observers were equivalent (AUC 0.932 versus 0.910, p = 0.184) for risk-assessment of malignant and benign nodules of all sizes. However, human readers performed superior to the computer model for differentiating malignant nodules from size-matched benign nodules (AUC 0.819 versus 0.706, p < 0.001). Large variations between observers were seen for ROC areas and ranges of risk scores. Morphological findings indicative of malignancy referred to border characteristics (spiculation, p < 0.001) and perinodular architectural deformation (distortion of surrounding lung parenchyma architecture, p < 0.001; pleural retraction, p = 0.002).CONCLUSIONS: Computer model and human observers perform equivalent for differentiating malignant from randomly selected benign nodules, confirming the high potential of computer models for nodule risk estimation in population based screening studies. However, computer models highly rely on size as discriminator. Incorporation of other morphological criteria used by human observers to superiorly discriminate size-matched malignant from benign nodules, will further improve computer performance.

Item does not contain fulltext The Fleischner Society Guidelines for management of solid nodules were published in 2005, and separate guidelines for subsolid nodules were issued in 2013. Since then, new information has become available; therefore, the guidelines have been revised to reflect current thinking on nodule management. The revised guidelines incorporate several substantive changes that reflect current thinking on the management of small nodules. The minimum threshold size for routine follow-up has been increased, and recommended follow-up intervals are now given as a range rather than as a precise time period to give radiologists, clinicians, and patients greater discretion to accommodate individual risk factors and preferences. The guidelines for solid and subsolid nodules have been combined in one simplified table, and specific recommendations have been included for multiple nodules. These guidelines represent the consensus of the Fleischner Society, and as such, they incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. Changes from the previous guidelines issued by the Fleischner Society are based on new data and accumulated experience. (c) RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on March 13, 2017.

PURPOSE:: The aim of the study was to retrospectively evaluate the diagnostic imaging that potential lung donors undergo, the reader variability of image interpretation and its relevance for donation, and the potential information gained from imaging studies not primarily intended for lung evaluation but partially including them. MATERIALS AND METHODS:: Bedside chest radiography and computed tomography (CT), completely or incompletely including the lungs, of 110 brain-dead potential organ donors in a single institution during 2007 to 2014 were reviewed from a donation perspective. Two chest radiologists in consensus analyzed catheters and cardiovascular, parenchymal, and pleural findings. Clinical reports and study review were compared for substantial differences in findings that could have led to a treatment change, triggered additional examinations such as bronchoscopy, or were considered important for donation. RESULTS:: Among 136 bedside chest radiographs, no differences between clinical reports and study reviews were found in 37 (27%), minor differences were found in 28 (21%), and substantial differences were found in 71 (52%) examinations (P

Contains fulltext : 179531.pdf (Publisher’s version ) (Open Access) Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems. 13 p.

Item does not contain fulltext PURPOSE OF REVIEW: Acute chest symptoms form an important incentive for imaging in the emergency setting. This review discusses the radiologic features of various vascular and pulmonary diseases leading to acute respiratory distress and recent developments on important emergency radiologic examinations. RECENT FINDINGS: Recently, triple-rule-out computed tomography protocol was introduced in diagnosis of chest pain, and advancing computed tomography technology and knowledge have led to discussion on treatment of pulmonary embolism. Diffuse pulmonary opacities remain a diagnostic dilemma in the emergency setting and although imaging findings can often be nonspecific, they help in guiding toward accurate diagnosis and timely management. SUMMARY: Though promising, triple-rule-out is not yet justified because of low incidence of additional findings compared with conventional computed tomography angiography in chest pain, but it might be suited for clinical practice in the near future. Relevance of isolated subsegmental pulmonary embolism is unknown and research on this topic is needed and on its way. We provided some key findings in differentiating diffuse pulmonary opacities and describe the additional value of chest ultrasound in this clinical dilemma. A brief sidestep to pneumothorax is made, as this is also a frequent finding in the acute dyspneic patient, as well as in patients with acute chest pain.

Contains fulltext : 173114.pdf (Publisher’s version ) (Open Access) We propose a novel method to improve airway segmentation in thoracic computed tomography (CT) by detecting and removing leaks. Leak detection is formulated as a classification problem, in which a convolutional network (ConvNet) is trained in a supervised fashion to perform the classification task. In order to increase the segmented airway tree length, we take advantage of the fact that multiple segmentations can be extracted from a given airway segmentation algorithm by varying the parameters that influence the tree length and the amount of leaks. We propose a strategy in which the combination of these segmentations after removing leaks can increase the airway tree length while limiting the amount of leaks. This strategy therefore largely circumvents the need for parameter fine-tuning of a given airway segmentation algorithm. The ConvNet was trained and evaluated using a subset of inspiratory thoracic CT scans taken from the COPDGene study. Our method was validated on a separate independent set of the EXACT'09 challenge. We show that our method significantly improves the quality of a given leaky airway segmentation, achieving a higher sensitivity at a low false-positive rate compared to all the state-of-the-art methods that entered in EXACT09, and approaching the performance of the combination of all of them.

Contains fulltext : 174818.pdf (Publisher’s version ) (Open Access) Purpose To evaluate the added value of Lung CT Screening Reporting and Data System (Lung-RADS) assessment category 4X over categories 3, 4A, and 4B for differentiating between benign and malignant subsolid nodules (SSNs). Materials and Methods SSNs on all baseline computed tomographic (CT) scans from the National Lung Cancer Trial that would have been classified as Lung-RADS category 3 or higher were identified, resulting in 374 SSNs for analysis. An experienced screening radiologist volumetrically segmented all solid cores and located all malignant SSNs visible on baseline scans. Six experienced chest radiologists independently determined which nodules to upgrade to category 4X, a recently introduced category for lesions that demonstrate additional features or imaging findings that increase the suspicion of malignancy. Malignancy rates of purely size-based categories and category 4X were compared. Furthermore, the false-positive rates of category 4X lesions were calculated and observer variability was assessed by using Fleiss kappa statistics. Results The observers upgraded 15%-24% of the SSNs to category 4X. The malignancy rate for 4X nodules varied from 46% to 57% per observer and was substantially higher than the malignancy rates of categories 3, 4A, and 4B SSNs without observer intervention (9%, 19%, and 23%, respectively). On average, the false-positive rate for category 4X nodules was 7% for category 3 SSNs, 7% for category 4A SSNs, and 19% for category 4B SSNs. Of the falsely upgraded benign lesions, on average 27% were transient. The agreement among the observers was moderate, with an average kappa value of 0.535 (95% confidence interval: 0.509, 0.561). Conclusion The inclusion of a 4X assessment category for lesions suspicious for malignancy in a nodule management tool is of added value and results in high malignancy rates in the hands of experienced radiologists. Proof of the transient character of category 4X lesions at short-term follow-up could avoid unnecessary invasive management. (c) RSNA, 2017.

Item does not contain fulltext PURPOSE:: To assess the effect of computer-assisted detection (CAD) on diagnostic accuracy, reader confidence, and reading time when used as a concurrent reader for the detection of acute pulmonary embolism in computed tomography pulmonary angiography. MATERIALS AND METHODS:: In this institutional review board-approved retrospective study, 6 observers with varying experience evaluated 158 negative and 38 positive consecutive computed tomography pulmonary angiographies (mean patient age 60 y; 115 women) without and with CAD as a concurrent reader. Readers were asked to determine the presence of pulmonary embolism, assess their diagnostic confidence using a 5-point scale, and document their reading time. Results were compared with an independent standard established by 2 readers, and a third chest radiologist was consulted in case of discordant findings. RESULTS:: Using logistic regression for repeated measurements, we found a significant increase in readers' sensitivity (P

Item does not contain fulltext PURPOSE: The aim of this study was to survey the current CT protocols used by members of the European Society of Thoracic Imaging (ESTI) to evaluate patients with interstitial lung diseases (ILD). METHODS: A questionnaire was e-mailed to 173 ESTI members. The survey focussed on CT acquisition and reconstruction techniques. In particular, questions referred to the use of discontinuous HRCT or volume CT protocols, the acquisition of additional acquisitions in expiration or in the prone position, and methods of radiation dose reduction and on reconstruction algorithms. RESULTS: The overall response rate was 37 \%. Eighty-five percent of the respondents used either volume CT alone or in combination with discontinuous HRCT. Forty-five percent of the respondents adapt their CT protocols to the patient's weight and/or age. Expiratory CT or CT in the prone position was performed by 58 \% and 59 \% of the respondents, respectively. The number of reconstructed series ranged from two to eight. CONCLUSION: Our survey showed that radiologists with a special interest and experience in chest radiology use a variety of CT protocols for the evaluation of ILD. There is a clear preference for volumetric scans and a strong tendency to use the 3D information. KEY POINTS: • Experienced thoracic radiologists use various CT protocols for evaluating interstitial lung diseases. • Most workers prefer volumetric CT acquisitions, making use of the 3D information • More attention to reducing the radiation dose appears to be needed.

Contains fulltext : 181940.pdf (Publisher’s version ) (Open Access) PURPOSE: Lung-RADS proposes malignancy probabilities for categories 2 (15%). The purpose of this study was to quantify and compare malignancy rates for Lung-RADS 2 and 4B subsolid nodules (SSNs) on a nodule base. METHODS: We identified all baseline SSNs eligible for Lung-RADS 2 and 4B in the National Lung Screening Trial (NLST) database. Solid cores and nodule locations were annotated using in-house software. Malignant SSNs were identified by an experienced radiologist using NLST information. Malignancy rates and percentages of persistence were calculated. RESULTS: Of the Lung-RADS 2SSNs, 94.3% (1790/1897) could be located on chest CTs. Likewise, 95.1% (331/348) of part-solid nodules >/=6 mm in diameter could be located. Of these, 120 had a solid core >/=8 mm, corresponding to category 4B. Category 2 SSNs showed a malignancy rate of 2.5%, exceeding slightly the proposed rate of

Contains fulltext : 167183.pdf (Publisher’s version ) (Open Access) We present a method for automatic separation and classification of pulmonary arteries and veins in computed tomography. Our method takes advantage of local information to separate segmented vessels, and global information to perform the artery-vein classification. Given a vessel segmentation, a geometric graph is constructed that represents both the topology and the spatial distribution of the vessels. All nodes in the geometric graph where arteries and veins are potentially merged are identified based on graph pruning and individual branching patterns. At the identified nodes, the graph is split into subgraphs that each contain only arteries or veins. Based on the anatomical information that arteries and veins approach a common alveolar sag, an arterial subgraph is expected to be intertwined with a venous subgraph in the periphery of the lung. This relationship is quantified using periphery matching and is used to group subgraphs of the same artery-vein class. Artery-vein classification is performed on these grouped subgraphs based on the volumetric difference between arteries and veins. A quantitative evaluation was performed on 55 publicly available non-contrast CT scans. In all scans, two observers manually annotated randomly selected vessels as artery or vein. Our method was able to separate and classify arteries and veins with a median accuracy of 89%, closely approximating the inter-observer agreement. All CT scans used in this study, including all results of our system and all manual annotations, are publicly available at "http://www.w3.org/1999/xlink">http://arteryvein.grand-challenge.org".

Item does not contain fulltext OBJECTIVE: The purpose of this article is to assess the relationship between CT image quality and the number and type of false-positive (FP) findings found by a prototype computer-aided detection (CAD) algorithm for automatic detection of pulmonary embolism (PE). MATERIALS AND METHODS: This retrospective study included 278 subjects (138 men and 140 women; mean age, 57 years; range, 18-88 years) who underwent consecutive CT pulmonary angiographies performed during off hours. Twenty-four percent (68/278) of studies were reported as positive for PE. CAD findings were classified as true-positive or FP by two independent readers and, in cases of discordance, by a third radiologist. Each FP result was classified according to underlying cause. The degree of vascular enhancement, image noise, motion artifacts, overall quality, and presence of underlying lung disease were rated on a 4- or 5-point scale. Chi-square tests and t tests were used to test significance of differences. RESULTS: The mean number of FP CAD findings was 4.7 (median, 2) per examination. Most were caused by veins (30% [389/1,298]) or airspace consolidations (22% [286/1,298]). There was a significant positive association between the number of FP findings and image noise, motion artifacts, low vascular enhancement, low overall quality, and the extent of underlying disease. On a per-embolism basis, sensitivity decreased from 70.6% (214/303) for scans with zero to five FP findings, to 62.3% (33/53) for scans with six to 10 FP findings, to 60% (12/20) for scans with more than 10 FP findings. CONCLUSION: There is a strong association between CT image quality and the number of FP findings indicated by a CAD algorithm for the detection of PE.

Item does not contain fulltext AIM: To determine the gender-specific prevalence of pulmonary cysts typical for lymphangioleiomyomatosis (LAM) in adult patients with known tuberous sclerosis complex (TSC). MATERIALS AND METHODS: A retrospective, cross-sectional study in a cohort of 206 adult TSC patients was performed. Institutional review board approval was obtained, and patient informed consent was waived. Patients had routinely undergone abdominal CT scanning between 1996 and 2006. All 186 patients (mean age 38 years; range 19-72 years; 91 (49%) male patients) in whom at least the lung bases were depicted on computed tomography (CT) were included. Images were reviewed for the presence of pulmonary thin-walled cysts. Descriptive statistics, two sample t-test to compare means, and chi(2)-test to compare proportions were applied. RESULTS: CT demonstrated pulmonary thin-walled cysts in the lung bases in 52 (28%) of 186 patients. Size varied from 2mm in diameter to more than 2 cm. Pulmonary cysts were detected in 40 (42%) of 95 female patients and in 12 (13%) of 91 male patients (p

Computed tomography angiography (CTA) of the pulmonary arteries has become the main diagnostic test for the evaluation of pulmonary embolism (PE). Not only due to the good availability, low cost and minimal invasiveness of this technique, but mainly because of the introduction of multi-detector CT techniques resulting in significant improvement in resolution, speed and image quality. This continuous gain in image acquisition speed went along with the introduction of new techniques of image acquisition, such as the dual-source CT scanning and novel concepts of image interpretation beyond morphological findings including the definition of the resulting perfusion defects and assessment of the cardiopulmonary circulation as a functional unit. This article will focus on technical and practical aspects to optimize CTPA examinations with modern multi-detector CT scanners, discusses aspects to be considered in specific patient groups (e. g., during pregnancy, young patients) and outlines new advents such as dual-source lung perfusion and automatic detection of pulmonary emboli. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Item does not contain fulltext PURPOSE: The purpose of the study was to assess the stand-alone performance of computer-assisted detection (CAD) for evaluation of pulmonary CT angiograms (CTPA) performed in an on-call setting. METHODS: In this institutional review board-approved study, we retrospectively included 292 consecutive CTPA performed during night shifts and weekends over a period of 16 months. Original reports were compared with a dedicated CAD system for pulmonary emboli (PE). A reference standard for the presence of PE was established using independent evaluation by two readers and consultation of a third experienced radiologist in discordant cases. RESULTS: Original reports had described 225 negative studies and 67 positive studies for PE. CAD found PE in seven patients originally reported as negative but identified by independent evaluation: emboli were located in segmental (n = 2) and subsegmental arteries (n = 5). The negative predictive value (NPV) of the CAD algorithm was 92% (44/48). On average there were 4.7 false positives (FP) per examination (median 2, range 0-42). In 72% of studies or=10 FP. CONCLUSION: CAD identified small emboli originally missed under clinical conditions and found 93% of the isolated subsegmental emboli. On average there were 4.7 FP per examination. 01 april 2010

Item does not contain fulltext PURPOSE: To examine the morphologic characteristics of focal fatty foci in the myocardium of patients with tuberous sclerosis complex (TSC) at computed tomography (CT). MATERIALS AND METHODS: Institutional review board approval was obtained, and patient informed consent was waived. Fifty-five patients with TSC (mean age, 37 years; range, 16-67 years; 22 male patients) who had CT results available that included at least the basal portions of the heart were included. Fifty-five age- and sex-matched control subjects without TSC were selected from a CT database. Images were reviewed for the presence of areas of fat attenuation in the depicted portions of the myocardium. Descriptive statistics and the McNemar test for case-control comparisons were used. RESULTS: CT results demonstrated foci of fat attenuation within the myocardium in 35 (64%) of 55 patients with TSC. Foci were well circumscribed and focal and located in the interventricular septum, left ventricle wall, right ventricle wall, and papillary muscles. Size varied between 3 x 1 mm and 62 x 31 mm. Multiple lesions were seen in 19 patients. In the control group, only one (2%) lesion with fat attenuation was found (P < .001). Its linear shape and subendocardial location in the left ventricular wall differed from the morphology of fatty foci seen in patients with TSC. CONCLUSION: Despite incomplete depiction of the heart with CT, the majority of patients with TSC demonstrated well-circumscribed foci of fat attenuation in the myocardium that were not present in age- and sex-matched control subjects. This suggests that such fatty foci may be another characteristic of TSC.

Contains fulltext : 80400.pdf (Publisher’s version ) (Open Access) OBJECTIVES: To study the impact of image subtraction of registered images on the detection of change in pulmonary ground-glass nodules identified on chest CT. MATERIALS AND METHODS: A cohort of 33 individuals (25 men, 8 women; age range 51-75 years) with 37 focal ground-glass opacities (GGO) were recruited from a lung cancer screening trial. For every participant, 1 to 3 follow-up scans were available (total number of pairs, 84). Pairs of scans of the same nodule were registered nonrigidly and then subtracted to enhance differences in size and density. Four observers rated size and density change of the GGO between pairs of scans by visual comparison alone and with additional availability of a subtraction image and indicated their confidence. An independent experienced chest radiologist served as an arbiter having all reader data, clinical data, and follow-up examinations available. Nodule pairs for which the arbiter could not establish definite progression, regression, or stability were excluded from further evaluation. This left 59 and 58 pairs for evaluation of size and density change, respectively. Weighted kappa statistics (kappa w) were used to assess interobserver agreement and agreement with the arbiter. Statistical significance was tested with a kappa z-test. RESULTS: When the subtraction image was available, the average interobserver kappa w improved from 0.52 to 0.66 for size change and from 0.47 to 0.57 for density change. Average agreement with the arbiter improved from 0.61 to 0.76 for size change and from 0.53 to 0.64 for density change. The effect was more pronounced when observer confidence without the subtraction image was low: agreement improved from 0.26 to 0.57 and from 0.19 to 0.47 in those cases. CONCLUSIONS: Image subtraction improves the evaluation of subtle changes in pulmonary ground-glass opacities and decreases interobserver variability.

Pulmonary hypertension (PHT) occurs in approximately 30% of adults with sickle cell disease (SCD) and is an independent risk factor for early death. In this study, we aimed to determine the value of general laboratory testing, plain chest radiography, electrocardiography (ECG), high-resolution computer tomography (HRCT) of the thorax, pulmonary function testing, and plasma N-terminal brain natriuretic peptide (NT-proBNP) and brain natriuretic peptide (BNP) in patients with SCD-related PHT. A cohort of 85 ambulatory sickle cell patients were prospectively screened for PHT with echocardiography (defined as a tricuspid regurgitation flow velocity of >= 2.5 ml All patients were systematically evaluated by the aforementioned diagnostic tests comparing patients with and without PHT. The prevalence of PHT was 41% in HbSS/HbS beta(0)-thalassemia patients and 13% in HbSC/HbS beta(+)-thalassemia patients. No statistically significant differences were detected in ECG, chest radiography, HRCT, and pulmonary function testing between patients with and without PHT. The degree of anemia and renal dysfunction, but not the presence of PHT, were the most important determinants of plasma (NT pro)BNP levels. The performed imaging and functional studies do not seem to be of value in identifying etiological conditions (such as airflow obstruction or parenchymal lung disease) nor do they offer clues to the presence of mild PHT in SCD. Am. J. Hematol. 83:850-854, 2008. (C) 2008 Wiley-Liss, Inc.

PURPOSE: To retrospectively determine the maximum compression ratio at which compressed images are indistinguishable from the original by using a three-dimensional (3D) wavelet algorithm. MATERIALS AND METHODS: The protocol of this study was approved by the local Institutional Review Board and informed consent was waived. Sixty emergency abdominal computed tomographic (CT) scans of patients (31 men, 29 women; mean age +/- standard deviation, 50.8 years +/- 20.1; range, 17-80 years) with acute abdominal pain were subjected to lossy irreversible three-dimensional Joint Photographic Experts Group 2000 (3D-JPEG2000) compression by using four compression ratios (4:1, 8:1, 12:1, and 16:1). Groups contained five patients for each of 12 common diagnoses for acute abdominal pain. Images were obtained by using a multidetector CT scanner (Sensation Cardiac 64; Siemens, Forcheim, Germany) with 3- and 6-mm-thick sections. Three radiologists independently compared one case-relevant image per patient with the original image at different compression ratios. They had to determine which image was the original by using a forced-choice, two-alternative model and to subjectively rank image quality. For analysis, a binomial test was used, a Bonferroni correction was applied, and a P value of .01 indicated a significant difference. RESULTS: Images compressed at ratios of 4:1 and 8:1 were visually indistinguishable and essentially indistinguishable, respectively, from the original images (P > .01 for all readers). For the 12:1 and 16:1 ratios, all readers definitively (P < .001) identified the original images. CONCLUSION: The highest 3D-JPEG2000 compression ratio for abdominal CT scans, at which compressed images are essentially indistinguishable from the original, is 8:1

OBJECTIVE: The objectives of our study were to assess whether coronal reformations improve the diagnostic performance of MDCT in patients with acute flank pain and suspected urinary stone disease; and to determine if performing such reformations from 3-mm-thick sections is sufficient or if it is necessary to perform reformations from thinner sections. MATERIALS AND METHODS: We included 147 consecutive patients (72 women and 75 men; mean age +/- SD, 58 +/- 18.1 years) with suspected urinary stone disease who underwent unenhanced MDCT. Scans were obtained with a 4 x 1 mm collimation and were reconstructed with a section thickness of 1.25 and 3 mm. We compared the diagnostic yield of 3-mm axial sections with that of coronal reformations reconstructed from 1.25- and 3-mm axial sections. Imaging data were evaluated in random order by two radiologists. The significance of the difference between the axial sections and coronal multiplanar reformations (MPRs) was tested for the number, size, and location of uroliths and for the presence of alternative diagnoses. The time required for review by both observers was recorded. RESULTS: We found uroliths in 72 patients. There was no difference between 3-mm axial sections and coronal reformations from 1.25-mm sections with regard to the number of detected stones (n = 264 for both protocols), whereas coronal reformations from 3-mm sections revealed significantly fewer calcifications (n = 255, p = 0.016). Coronal reformations did not improve the localization of calcifications. Review time, however, was significantly shorter for coronal reformations than for axial sections (p = 0.001); however, coronal reformations were less sensitive than axial sections for the detection of additional findings suggestive of alternative diagnoses in 16 (30%) of 53 patients. CONCLUSION: Coronal reformations from MDCT do not improve urinary stone detection but may reduce evaluation time; however, there is the danger of missing additional findings. Coronal reformations reconstructed from thick (i.e., 3-5 mm) axial sections may result in reduced detection of small stones and should therefore be avoided

Purpose: To prospectively assess the precision of semiautomated volume measurements of pulmonary nodules at low-dose multi-detector row computed tomography (CT) and to investigate the influence of nodule size, segmentation algorithm, and inspiration level. Materials and Methods: This study had institutional review board approval; written informed consent was obtained from all patients. Between June 2004 and March 2005, 20 patients (15 men, five women; age range, 40-84 years; mean age, 57 years) referred for chest CT for known lung metastases under-went two additional low-dose chest CT examinations without contrast material (collimation, 16 x 0.75 mm). Between these examinations, patients got off and on the table to simulate the conditions for a follow-up examination. Noncalcified solid pulmonary nodules between 15 and 500 mm(3) that did not abut vessel or pleura were measured in both studies by using widely applied commercial semiautomated software. Interscan variability was established with the Bland and Altman approach. The impact of nodule shape (spherical or nonspherical) on measurement variability was assessed by using one-way analysis of variance, while the contributions of mean nodule volume and change in lung volume were investigated with univariate linear regression for completely (group A) and incompletely (group B) segmented nodules. Results: Two hundred eighteen eligible nodules (volume range, 16.4-472.7 mm(3); 106 spherical, 112 nonspherical) were evaluated. The 95% confidence interval for difference in measured volumes was -21.2%, 23.8% ( mean difference, 1.3%). The precision of nodule segmentation was highly dependent on nodule shape (P

Contains fulltext : 153984.pdf (Publisher’s version ) (Open Access) The purpose of this study was to develop and validate a computer-aided diagnosis (CAD) tool for automatic classification of pulmonary nodules seen on low-dose computed tomography into solid, part-solid, and non-solid. Study lesions were randomly selected from 2 sites participating in the Dutch-Belgian NELSON lung cancer screening trial. On the basis of the annotations made by the screening radiologists, 50 part-solid and 50 non-solid pulmonary nodules with a diameter between 5 and 30 mm were randomly selected from the 2 sites. For each unique nodule, 1 low-dose chest computed tomographic scan was randomly selected, in which the nodule was visible. In addition, 50 solid nodules in the same size range were randomly selected. A completely automatic 3-dimensional segmentation-based classification system was developed, which analyzes the pulmonary nodule, extracting intensity-, texture-, and segmentation-based features to perform a statistical classification. In addition to the nodule classification by the screening radiologists, an independent rating of all nodules by 3 experienced thoracic radiologists was performed. Performance of CAD was evaluated by comparing the agreement between CAD and human experts and among human experts using the Cohen κ statistics.Pairwise agreement for the differentiation between solid, part-solid, and non-solid nodules between CAD and each of the human experts had a κ range between 0.54 and 0.72. The interobserver agreement among the human experts was in the same range (κ range, 0.56-0.81). A novel automated classification tool for pulmonary nodules achieved good agreement with the human experts, yielding κ values in the same range as the interobserver agreement. Computer-aided diagnosis may aid radiologists in selecting the appropriate workup for pulmonary nodules.

To compare the diagnostic performance of multidetector computed tomography (CT) and magnetic resonance (MR) imaging in patients clinically suspected of having a scaphoid fracture and who had normal initial radiographs, with radiographs obtained 6 weeks after trauma as the reference standard.The ethics committee approved the study, and all patients gave written informed consent. Twenty-nine patients (17 male, 12 female; age range, 17-62 years; mean age, 34 years +/- 13) underwent multidetector CT and MR imaging within 6 days after trauma. CT data were obtained with 0.5-mm collimation. For image review, 0.7-mm-thick multiplanar reformations were performed in transverse, coronal, and sagittal planes relative to the wrist. The 1.0-T MR examination consisted of coronal and transverse short inversion time inversion-recovery, coronal and transverse T1-weighted spin-echo, and coronal volume-rendered T2-weighted gradient-echo sequences. Two radiologists analyzed the CT and MR images. A binomial test was used to evaluate the significance of the differences between MR imaging and CT in detection of scaphoid fractures and cortical involvement (P.05).The 6-week follow-up radiographs depicted a scaphoid fracture in 11 (38%) patients. Eight patients had a cortical fracture, while three patients had only a bandlike lucency within the trabecular portion of the scaphoid. MR imaging depicted all 11 fractures but only three [corrected] cortical fractures. Multidetector CT depicted all eight cortical fractures but failed to depict trabecular fractures. No false-positive fractures were seen on MR or CT images. Differences between MR imaging and CT were not significant for the detection of scaphoid fractures (P = .25) but were significant for cortical involvement (P = .03).Multidetector CT is highly accurate in depicting occult cortical scaphoid fractures but appears inferior to MR imaging in depicting solely trabecular injury. MR imaging is inferior to multidetector CT in depicting cortical involvement.

We sought to evaluate the performance of dual-readout and single-readout computed radiography compared with direct radiography for detecting subtle lung abnormalities with a standard and a low-dose technique.Posteroanterior radiographs of an anthropomorphic chest phantom were obtained with a single-readout storage phosphor radiography system (CRS, pixel size 200 microm), a dual-readout storage phosphor radiography system (CRD, pixel size 100 microm), and a direct detector (DR, pixel size 143 microm) at dose levels of 400 and 800 speed. Ten templates were superimposed to project 4 types of lesions over low- and high-attenuation areas, simulating nodules, micronodules, lines, and patchy opacities. Six radiologists evaluated 60 hard-copy images for the presence or absence of lesions. Statistical significance of differences was evaluated using receiver operating characteristic analysis and analysis of variance.For both low- and high-attenuation areas, CRD (Az = 0.85 and 0.66) was superior to CRS (Az = 0.75 and 0.58) for overall performance and all lesion subtypes (P0.05). DR (Az = 0.87 and 0.67) performed slightly better than CRD, being significant only for the detection of micronodules. Acquisition dose significantly affected only the detection of lines and micronodules, whereas the detection of nodules and patchy opacities was not significantly different with reduced exposure, regardless of the system used.The dual-readout CR system significantly outperformed the single-readout CR and almost equaled the performance of DR. Dose reduction was more critical for small-sized lesions (micronodules, lines) than for nodular or patchy opacifications and affected mainly the lesions in high attenuation areas.

To analyze the capabilities of chest roentgenogram (CXR) and computed tomography (CT) in the evaluation of pulmonary infectious disease in the late period (100 days) after allogeneic bone marrow transplantation (BMT).Ninety-four matched CXR and CT examinations were performed for clinical suspicion of infectious lung disease. The time gap between CXR and CT was 48 h at maximum. The image pairs were correlated with the patients' clinical course and with the results of diagnostic bronchoalveolar lavage (BAL). An unremarkable clinical course over the subsequent seven days after imaging and/or negative microbiological culture served as the basis for excluding infectious lung disease. Positive microbiological culture and/or improvement of symptoms after antibiotic therapy were considered as evidence of infectious disease.The correlation with the clinical course and/or BAL revealed a significantly higher sensitivity, negative predictive value, and accuracy for CT than for CXR (89% versus 58%, P0.0001; 78% versus 47%, P0.0001; 90% versus 68%, P0.0001, respectively). CT was significantly more diagnostic in BAL verified fungal and bacterial infections (P0.05).CT is significantly superior to CXR in the evaluation of infectious pulmonary disease in the late phase after BMT. Therefore, an unremarkable CXR should be followed by a CT scan to reliably detect or to accurately exclude early pulmonary infection in these patients.

Peripheral pulmonary vascular disorders that can be evaluated with computed tomography (CT) include various disease entities with overlapping imaging features and a wide range of clinical manifestations. The overall accuracy of CT in the diagnosis of pulmonary vascular disorders increases with improved spatial resolution, administration of a high-flow contrast material bolus, and the use of cardiac gating. The integration of high-resolution CT and CT angiographic techniques into one scanning protocol has important clinical implications for multisection CT and makes it the imaging modality of choice in the evaluation of this complex group of disorders.

Many medical image analysis systems require segmentation of the structures of interest as a first step. For scans with gross pathology, automatic segmentation methods may fail. The authors' aim is to develop a versatile, fast, and reliable interactive system to segment anatomical structures. In this study, this system was used for segmenting lungs in challenging thoracic computed tomography (CT) scans.In volumetric thoracic CT scans, the chest is segmented and divided into 3D volumes of interest (VOIs), containing voxels with similar densities. These VOIs are automatically labeled as either lung tissue or nonlung tissue. The automatic labeling results can be corrected using an interactive or a supervised interactive approach. When using the supervised interactive system, the user is shown the classification results per slice, whereupon he/she can adjust incorrect labels. The system is retrained continuously, taking the corrections and approvals of the user into account. In this way, the system learns to make a better distinction between lung tissue and nonlung tissue. When using the interactive framework without supervised learning, the user corrects all incorrectly labeled VOIs manually. Both interactive segmentation tools were tested on 32 volumetric CT scans of pigs, mice and humans, containing pulmonary abnormalities.On average, supervised interactive lung segmentation took under 9 min of user interaction. Algorithm computing time was 2 min on average, but can easily be reduced. On average, 2.0% of all VOIs in a scan had to be relabeled. Lung segmentation using the interactive segmentation method took on average 13 min and involved relabeling 3.0% of all VOIs on average. The resulting segmentations correspond well to manual delineations of eight axial slices per scan, with an average Dice similarity coefficient of 0.933.The authors have developed two fast and reliable methods for interactive lung segmentation in challenging chest CT images. Both systems do not require prior knowledge of the scans under consideration and work on a variety of scans.

Contains fulltext : 137680.pdf (Publisher’s version ) (Open Access) To evaluate the added value of computer-aided detection (CAD) for lung nodules on chest radiographs when radiologists have bone-suppressed images (BSIs) available.Written informed consent was waived by the institutional review board. Selection of study images and study setup was reviewed and approved by the institutional review boards. Three hundred posteroanterior (PA) and lateral chest radiographs (189 radiographs with negative findings and 111 radiographs with a solitary nodule) in 300 subjects were selected from image archives at four institutions. PA images were processed by using a commercially available CAD, and PA BSIs were generated. Five radiologists and three residents evaluated the radiographs with BSIs available, first, without CAD and, second, after inspection of the CAD marks. Readers marked locations suspicious for a nodule and provided a confidence score for that location to be a nodule. Location-based receiver operating characteristic analysis was performed by using jackknife alternative free-response receiver operating characteristic analysis. Area under the curve (AUC) functioned as figure of merit, and P values were computed with the Dorfman-Berbaum-Metz method.Average nodule size was 16.2 mm. Stand-alone CAD reached a sensitivity of 74\% at 1.0 false-positive mark per image. Without CAD, average AUC for observers was 0.812. With CAD, performance significantly improved to an AUC of 0.841 (P = .0001). CAD detected 127 of 239 nodules that were missed after evaluation of the radiographs together with BSIs pooled over all observers. Only 57 of these detections were eventually marked by the observers after review of CAD candidates.CAD improved radiologists' performance for the detection of lung nodules on chest radiographs, even when baseline performance was optimized by providing lateral radiographs and BSIs. Still, most of the true-positive CAD candidates are dismissed by observers.