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PurposeQuantitative computed tomography (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data.Patients and methodsWe selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers.ResultsCOPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%). Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity and airway narrowing over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering.ConclusionqCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.

The article addresses the global impact of chronic obstructive pulmonary disease (COPD), noting its high prevalence and the significant burden of undiagnosed cases. Topics include the limitations of current screening practices and the U.S. Preventive Services Task Force's stance against routine spirometry for asymptomatic individuals, despite evidence suggesting that at-risk populations could benefit from early detection.

The Society of Cardiovascular Computed Tomography (SCCT) and the Society of Thoracic Radiology (STR) have jointly produced this document. Experts in this subject have been selected from both organizations to examine subject-specific data and write this guideline in partnership. A formal literature review, weighing the strength of evidence has been performed. When available, information from studies on cost was considered. Computed tomography (CT) acquisition, CAC scoring methodologies and clinical outcomes are the primary basis for the recommendations in this guideline. This guideline is intended to assist healthcare providers in clinical decision making. The recommendations reflect a consensus after a thorough review of the best available current scientific evidence and practice patterns of experts in the field and are intended to improve patient care while acknowledging that situations arise where additional information may be needed to better inform patient care.

The Society of Cardiovascular Computed Tomography (SCCT) and the Society of Thoracic Radiology (STR) have jointly produced this document. Experts in this subject have been selected from both organizations to examine subject-specific data and write this guideline in partnership. A formal literature review, weighing the strength of evidence has been performed. When available, information from studies on cost was considered. Computed tomography (CT) acquisition, CAC scoring methodologies and clinical outcomes are the primary basis for the recommendations in this guideline. This guideline is intended to assist healthcare providers in clinical decision making. The recommendations reflect a consensus after a thorough review of the best available current scientific evidence and practice patterns of experts in the field and are intended to improve patient care while acknowledging that situations arise where additional information may be needed to better inform patient care.

Simple Summary: Detecting lung cancer early and initiating treatment promptly can greatly enhance patient outcomes. While low-dose computed tomography (LDCT) screening aids in identifying lung cancer at an early stage, there is a risk of diagnostic delays as patients await follow-up scans. To mitigate this challenge, we developed a deep predictive model leveraging generative AI methods to forecast nodule growth patterns in follow-up LDCT scans based on baseline LDCT scans. Our findings illustrated that utilizing the predicted follow-up nodule images generated by our model during baseline screening improved diagnostic accuracy compared to using baseline nodules alone and achieved comparable performance with using real follow-up nodules. This demonstrated the potential of employing deep generative models to forecast nodule appearance in follow-up imaging from baseline LDCT scans, thereby enhancing risk assessment during initial screening. Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam. [ABSTRACT FROM AUTHOR]

The article focuses on the challenges of achieving repeatable and accurate quantitative computed tomography (QCT) measurements in pulmonary medicine, particularly in chronic obstructive pulmonary disease (COPD) studies. It discusses the barriers to widespread adoption of QCT measures, the efforts to standardize QCT acquisition protocols, and the development of key repeatability data through the NIH-funded SPIROMICS cohort.

Lung cancer screening (LCS) with low-dose CT (LDCT) imaging was recommended in 2013, making approximately 8 million Americans eligible for LCS. The demographic characteristics and outcomes of individuals screened in the United States have not been reported at the population level. What are the outcomes among people screened and entered in the American College of Radiology's Lung Cancer Screening Registry compared with those of trial participants? This was a cohort study of individuals undergoing baseline LDCT imaging for LCS between 2015 and 2019. Predictors of adherence to annual screening were computed. LDCT scan interpretations by Lung Imaging Reporting and Data System (Lung-RADS) score, cancer detection rates (CDRs), and stage at diagnosis were compared with National Lung Cancer Screening Trial data. Adherence was 22.3%, and predictors of poor adherence included current smoking status and Hispanic or Black race. On baseline screening, 83% of patients showed negative results and 17% showed positive screening results. The overall CDR was 0.56%. The percentage of people with cancer detected at baseline was higher in the positive Lung-RADS categories at 0.4% for Lung-RADS category 3, 2.6% for Lung-RADS category 4A, 11.1% for Lung-RADS category 4B, and 19.9% for Lung-RADS category 4X. The cancer stage distribution was similar to that observed in the National Lung Cancer Screening Trial, with 53.5% of patients receiving a diagnosis of stage I cancer and 14.3% with stage IV cancer. Underreporting into the registry may have occurred. This study revealed both the positive aspects of CT scan screening for lung cancer and the challenges that remain. Findings on CT imaging were correlated accurately with lung cancer detection using the Lung-RADS system. A significant stage shift toward early-stage lung cancer was present. Adherence to LCS was poor and likely contributes to the lower than expected cancer detection rate, all of which will impact the outcomes of patients undergoing screening for lung cancer. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was recommended by the U.S. Preventive Services Task Force (USPSTF) in 2013, making approximately 8 million Americans eligible for screening. The demographic characteristics and adherence of persons screened in the United States have not been reported at the population level.Objective: To define sociodemographic characteristics and adherence among persons screened and entered into the American College of Radiology's Lung Cancer Screening Registry (LCSR).Design: Cohort study.Setting: United States, 2015 to 2019.Participants: Persons receiving a baseline LDCT for LCS from 3625 facilities reporting to the LCSR.Measurements: Age, sex, and smoking status distributions (percentages) were computed among persons who were screened and among respondents in the 2015 National Health Interview Survey (NHIS) who were eligible for screening. The prevalence between the LCSR and the NHIS was compared with prevalence ratios (PRs) and 95% CIs. Adherence to annual screening was defined as having a follow-up test within 11 to 15 months of an initial LDCT.Results: Among 1 159 092 persons who were screened, 90.8% (n = 1 052 591) met the USPSTF eligibility criteria. Compared with adults from the NHIS who met the criteria (n = 1257), screening recipients in the LCSR were older (34.7% vs. 44.8% were aged 65 to 74 years; PR, 1.29 [95% CI, 1.20 to 1.39]), more likely to be female (41.8% vs. 48.1%; PR, 1.15 [CI, 1.08 to 1.23]), and more likely to currently smoke (52.3% vs. 61.4%; PR, 1.17 [CI, 1.11 to 1.23]). Only 22.3% had a repeated annual LDCT. If follow-up was extended to 24 months and more than 24 months, 34.3% and 40.3% were adherent, respectively.Limitations: Underreporting of LCS and missing data may skew demographic characteristics of persons reported to be screened. Underreporting of adherence may result in underestimates of follow-up.Conclusion: Approximately 91% of persons who had LCS met USPSTF eligibility criteria. In addition to continuing to target all eligible adults, men, those who formerly smoked, and younger eligible patients may be less likely to be screened. Adherence to annual follow-up screening was poor, potentially limiting screening effectiveness.Primary Funding Source: None. [ABSTRACT FROM AUTHOR]

Objective: Accurate segmentation of the lung nodule in computed tomography images is a critical component of a computer‐assisted lung cancer detection/diagnosis system. However, lung nodule segmentation is a challenging task due to the heterogeneity of nodules. This study is to develop a hybrid deep learning (H‐DL) model for the segmentation of lung nodules with a wide variety of sizes, shapes, margins, and opacities. Materials and methods: A dataset collected from Lung Image Database Consortium image collection containing 847 cases with lung nodules manually annotated by at least two radiologists with nodule diameters greater than 7 mm and less than 45 mm was randomly split into 683 training/validation and 164 independent test cases. The 50% consensus consolidation of radiologists' annotation was used as the reference standard for each nodule. We designed a new H‐DL model combining two deep convolutional neural networks (DCNNs) with different structures as encoders to increase the learning capabilities for the segmentation of complex lung nodules. Leveraging the basic symmetric U‐shaped architecture of U‐Net, we redesigned two new U‐shaped deep learning (U‐DL) models that were expanded to six levels of convolutional layers. One U‐DL model used a shallow DCNN structure containing 16 convolutional layers adapted from the VGG‐19 as the encoder, and the other used a deep DCNN structure containing 200 layers adapted from DenseNet‐201 as the encoder, while the same decoder with only one convolutional layer at each level was used in both U‐DL models, and we referred to them as the shallow and deep U‐DL models. Finally, an ensemble layer was used to combine the two U‐DL models into the H‐DL model. We compared the effectiveness of the H‐DL, the shallow U‐DL and the deep U‐DL models by deploying them separately to the test set. The accuracy of volume segmentation for each nodule was evaluated by the 3D Dice coefficient and Jaccard index (JI) relative to the reference standard. For comparison, we calculated the median and minimum of the 3D Dice and JI over the individual radiologists who segmented each nodule, referred to as M‐Dice, min‐Dice, M‐JI, and min‐JI. Results: For the 164 test cases with 327 nodules, our H‐DL model achieved an average 3D Dice coefficient of 0.750 ± 0.135 and an average JI of 0.617 ± 0.159. The radiologists' average M‐Dice was 0.778 ± 0.102, and the average M‐JI was 0.651 ± 0.127; both were significantly higher than those achieved by the H‐DL model (p < 0.05). The radiologists' average min‐Dice (0.685 ± 0.139) and the average min‐JI (0.537 ± 0.153) were significantly lower than those achieved by the H‐DL model (p < 0.05). The results indicated that the H‐DL model approached the average performance of radiologists and was superior to the radiologist whose manual segmentation had the min‐Dice and min‐JI. Moreover, the average Dice and average JI achieved by the H‐DL model were significantly higher than those achieved by the individual shallow U‐DL model (Dice of 0.745 ± 0.139, JI of 0.611 ± 0.161; p < 0.05) or the individual deep U‐DL model alone (Dice of 0.739 ± 0.145, JI of 0.604 ± 0.163; p < 0.05). Conclusion: Our newly developed H‐DL model outperformed the individual shallow or deep U‐DL models. The H‐DL method combining multilevel features learned by both the shallow and deep DCNNs could achieve segmentation accuracy comparable to radiologists' segmentation for nodules with wide ranges of image characteristics. [ABSTRACT FROM AUTHOR]

Recent clinical practice guidelines have addressed the diagnosis of idiopathic pulmonary fibrosis (IPF) and fibrotic hypersensitivity pneumonitis (fHP). These disease-specific guidelines were developed independently, without clear direction on how to apply their respective recommendations concurrently within a single patient, where discrimination between these two fibrotic interstitial lung diseases represents a frequent diagnostic challenge. The objective of this review, created by an international group of experts, was to suggest a pragmatic approach on how to apply existing guidelines to distinguish IPF and fHP. Key clinical, radiologic, and pathologic features described in previous guidelines are integrated in a set of diagnostic algorithms, which then are placed in the broader context of multidisciplinary discussion to guide the generation of a consensus diagnosis. Although these algorithms necessarily reflect some uncertainty wherever strong evidence is lacking, they provide insight into the current approach favored by experts in the field based on currently available knowledge. The authors further identify priorities for future research to clarify ongoing uncertainties in the diagnosis of fibrotic interstitial lung diseases. [ABSTRACT FROM AUTHOR]

Lung cancer screening with a low radiation dose chest CT scan is the standard of care for screening-eligible individuals. The net benefit of screening may be optimized by delivering high-quality care, capable of maximizing the benefit and minimizing the harms of screening. Valid, feasible, and relevant indicators of the quality of lung cancer screening may help programs to evaluate their current practice and to develop quality improvement plans. The purpose of this project was to develop quality indicators related to the processes and outcomes of screening. Potential quality indicators were explored through surveys of multidisciplinary lung cancer screening experts. Those that achieved predefined measures of consensus for each of the validity, feasibility, and relevance domains are proposed as quality indicators. Each of the proposed indicators is described in detail, with guidance on how to define, measure, and improve program performance within the indicator. [ABSTRACT FROM AUTHOR]

Background: Lung cancer risk prediction models do not routinely incorporate imaging metrics available on low-dose CT (LDCT) imaging of the chest ordered for lung cancer screening.Research Question: What is the association between quantitative emphysema measured on LDCT imaging and lung cancer incidence and mortality, all-cause mortality, and airflow obstruction in individuals who currently or formerly smoked and are undergoing lung cancer screening?Study Design and Methods: In 7,262 participants in the CT arm of the National Lung Screening Trial, percent low attenuation area (%LAA) was defined as the percentage of lung volume with voxels less than -950 Hounsfield units on the baseline examination. Multivariable Cox proportional hazards models, adjusting for competing risks where appropriate, were built to test for association between %LAA and lung cancer incidence, lung cancer mortality, and all-cause mortality with censoring at 6 years. In addition, multivariable logistic regression models were built to test the cross-sectional association between %LAA and airflow obstruction on spirometry, which was available in 2,700 participants.Results: The median %LAA was 0.8% (interquartile range, 0.2%-2.7%). Every 1% increase in %LAA was independently associated with higher hazards of lung cancer incidence (hazard ratio [HR], 1.02; 95% CI, 1.01-1.03; P = .004), lung cancer mortality (HR, 1.02; 95% CI, 1.00-1.05; P = .045), and all-cause mortality (HR, 1.01; 95% CI, 1.00-1.03; P = .042). Among participants with spirometry, 892 had airflow obstruction. The likelihood of airflow obstruction increased with every 1% increase in %LAA (odds ratio, 1.07; 95% CI, 1.06-1.09; P < .001). A %LAA cutoff of 1% had the best discriminative accuracy for airflow obstruction in participants aged > 65 years.Interpretation: Quantitative emphysema measured on LDCT imaging of the chest can be leveraged to improve lung cancer risk prediction and help diagnose COPD in individuals who currently or formerly smoked and are undergoing lung cancer screening. [ABSTRACT FROM AUTHOR]

Background: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. Objective: To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. Materials and methods: Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. Results: QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. Conclusion: The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network to learn from CT data and derive tissue pattern-clusters jointly. We then applied exploratory factor analysis (EFA) to discover the unobserved latent traits (factors) among pattern-clusters. CT images at total lung capacity (TLC) and residual volume (RV) of 541 former smokers and 59 healthy non-smokers from the cohort of the SubPopulations and Intermediate Outcome Measures in the COPD Study (SPIROMICS) were analyzed. TLC and RV images were registered to calculate the Jacobian (determinant) values for all the voxels in TLC images. 3D Regions of interest (ROIs) with two data channels of CT intensity and Jacobian value were randomly extracted from training images and were fed to the 3D CAE-FC model. 80 pattern-clusters and 7 factors were identified. Factor scores computed for individual subjects were able to predict spirometry-measured pulmonary functions. Two factors which correlated with various emphysema subtypes, parametric response mapping (PRM) metrics, airway variants, and airway tree to lung volume ratio were discriminants of patients across all severity stages. Our findings suggest the potential of developing factor-based surrogate markers for new COPD phenotypes. [ABSTRACT FROM AUTHOR]

Annotating lesion locations by radiologists' manual marking is a key step to provide reference standard for the training and testing of a computer-aided detection system by supervised machine learning. Inter-reader variability is not uncommon in readings even by expert radiologists. This study evaluated the variability of the radiologist-identified pulmonary emboli (PEs) to demonstrate the importance of improving the reliability of the reference standard by a multi-step process for performance evaluation. In an initial reading of 40 CTPA PE cases, two experienced thoracic radiologists independently marked the PE locations. For markings from the two radiologists that did not agree, each radiologist re-read the cases independently to assess the discordant markings. Finally, for markings that still disagreed after the second reading, the two radiologists read together to reach a consensus. The variability of radiologists was evaluated by analyzing the agreement between two radiologists. For the 40 cases, 475 and 514 PEs were identified by radiologists R1 and R2 in the initial independent readings, respectively. For a total of 545 marks by the two radiologists, 81.5% (444/545) of the marks agreed but 101 marks in 36 cases differed. After consensus, 65 (64.4%) and 36 (35.6%) of the 101 marks were determined to be true PEs and false positives (FPs), respectively. Of these, 48 and 17 were false negatives (FNs) and 14 and 22 were FPs by R1 and R2, respectively. Our study demonstrated that there is substantial variability in reference standards provided by radiologists, which impacts the performance assessment of a lesion detection system. Combination of multiple radiologists' readings and consensus is needed to improve the reliability of a reference standard. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Chronic obstructive pulmonary disease is a heterogeneous disease characterized by small airway abnormality and emphysema. We hypothesized that a voxel-wise computed tomography analytic approach would identify patterns of disease progression in smokers.Materials and Methods: We analyzed 725 smokers in spirometric GOLD stages 0-4 with two chest CTs 5 years apart. Baseline inspiration, follow-up inspiration and follow-up expiration images were spatially registered to baseline expiration so that each voxel had correspondences across all time points and respiratory phases. Voxel-wise Parametric Response Mapping (PRM) was then generated for the baseline and follow-up scans. PRM classifies lung as normal, functional small airway disease (PRMfSAD), and emphysema (PRMEMPH).Results: Subjects with low baseline PRMfSAD and PRMEMPH predominantly had an increase in PRMfSAD on follow-up; those with higher baseline PRMfSAD and PRMEMPH mostly had increases in PRMEMPH. For GOLD 0 participants (n = 419), mean 5-year increases in PRMfSAD and PRMEMPH were 0.3% for both; for GOLD 1-4 participants (n = 306), they were 0.6% and 1.6%, respectively. Eighty GOLD 0 subjects (19.1%) had overall radiologic progression (30.0% to PRMfSAD, 52.5% to PRMEMPH, and 17.5% to both); 153 GOLD 1-4 subjects (50.0%) experienced progression (17.6% to PRMfSAD, 48.4% to PRMEMPH, and 34.0% to both). In a multivariable model, both baseline PRMfSAD and PRMEMPH were associated with development of PRMEMPH on follow-up, although this relationship was diminished at higher levels of baseline PRMEMPH.Conclusion: A voxel-wise longitudinal PRM analytic approach can identify patterns of disease progression in smokers with and without chronic obstructive pulmonary disease. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Chronic obstructive pulmonary disease is a heterogeneous disease characterized by small airway abnormality and emphysema. We hypothesized that a voxel-wise computed tomography analytic approach would identify patterns of disease progression in smokers.Materials and Methods: We analyzed 725 smokers in spirometric GOLD stages 0-4 with two chest CTs 5 years apart. Baseline inspiration, follow-up inspiration and follow-up expiration images were spatially registered to baseline expiration so that each voxel had correspondences across all time points and respiratory phases. Voxel-wise Parametric Response Mapping (PRM) was then generated for the baseline and follow-up scans. PRM classifies lung as normal, functional small airway disease (PRMfSAD), and emphysema (PRMEMPH).Results: Subjects with low baseline PRMfSAD and PRMEMPH predominantly had an increase in PRMfSAD on follow-up; those with higher baseline PRMfSAD and PRMEMPH mostly had increases in PRMEMPH. For GOLD 0 participants (n = 419), mean 5-year increases in PRMfSAD and PRMEMPH were 0.3% for both; for GOLD 1-4 participants (n = 306), they were 0.6% and 1.6%, respectively. Eighty GOLD 0 subjects (19.1%) had overall radiologic progression (30.0% to PRMfSAD, 52.5% to PRMEMPH, and 17.5% to both); 153 GOLD 1-4 subjects (50.0%) experienced progression (17.6% to PRMfSAD, 48.4% to PRMEMPH, and 34.0% to both). In a multivariable model, both baseline PRMfSAD and PRMEMPH were associated with development of PRMEMPH on follow-up, although this relationship was diminished at higher levels of baseline PRMEMPH.Conclusion: A voxel-wise longitudinal PRM analytic approach can identify patterns of disease progression in smokers with and without chronic obstructive pulmonary disease. [ABSTRACT FROM AUTHOR]

Background: COPD is associated with cardiovascular disease (CVD), and coronary artery calcification (CAC) provides additional prognostic information. With increasing use of nongated CT scans in clinical practice, this study hypothesized that the visual Weston CAC score would perform as well as the Agatston score in predicting prevalent and incident coronary artery disease (CAD) and CVD in COPD.Methods: CAC was measured by using Agatston and Weston scores on baseline CT scans in 1,875 current and former smokers enrolled in the Genetic Epidemiology of COPD (COPDGene) study. Baseline cardiovascular disease and incident cardiac events on longitudinal follow-up were recorded. Accuracy of the CAC scores was measured by using receiver-operating characteristic analysis, and Cox proportional hazards analyses were used to estimate the risk of incident cardiac events.Results: CAD was reported by 133 (7.1%) subjects at baseline. A total of 413 (22.0%) and 241 (12.9%) patients had significant CAC according to the Weston (≥ 7) and Agatston (≥ 400) scores, respectively; the two methods were significantly correlated (r = 0.84; P < .001). Over 5 years of follow-up, 127 patients (6.8%) developed incident CVD. For predicting prevalent CAD, c-indices for the Weston and Agatston scores were 0.78 and 0.74 and for predicting incident CVD, they were 0.62 and 0.61. After adjustment for age, race, sex, smoking pack-years, FEV1, percent emphysema, and CT scanner type, a Weston score ≥ 7 was associated with time to first acute coronary event (hazard ratio, 2.16 [95% CI, 1.32 to 3.53]; P = .002), but a Agatston score ≥ 400 was not (hazard ratio, 1.75 [95% CI, 0.99-3.09]; P = .053).Conclusions: A simple visual score for CAC performed well in predicting incident CAD in smokers with and without COPD.Trial Registry: ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov. [ABSTRACT FROM AUTHOR]

Rationale: Adaptive multiple features method (AMFM) lung texture analysis software recognizes high-resolution computed tomography (HRCT) patterns.Objectives: To evaluate AMFM and visual quantification of HRCT patterns and their relationship with disease progression in idiopathic pulmonary fibrosis.Methods: Patients with idiopathic pulmonary fibrosis in a clinical trial of prednisone, azathioprine, and N-acetylcysteine underwent HRCT at study start and finish. Proportion of lung occupied by ground glass, ground glass-reticular (GGR), honeycombing, emphysema, and normal lung densities were measured by AMFM and three radiologists, documenting baseline disease extent and postbaseline change. Disease progression includes composite mortality, hospitalization, and 10% FVC decline.Measurements and Main Results: Agreement between visual and AMFM measurements was moderate for GGR (Pearson's correlation r = 0.60, P < 0.0001; mean difference = -0.03 with 95% limits of agreement of -0.19 to 0.14). Baseline extent of GGR was independently associated with disease progression when adjusting for baseline Gender-Age-Physiology stage and smoking status (hazard ratio per 10% visual GGR increase = 1.98, 95% confidence interval [CI] = 1.20-3.28, P = 0.008; and hazard ratio per 10% AMFM GGR increase = 1.36, 95% CI = 1.01-1.84, P = 0.04). Postbaseline visual and AMFM GGR trajectories were correlated with postbaseline FVC trajectory (r = -0.30, 95% CI = -0.46 to -0.11, P = 0.002; and r = -0.25, 95% CI = -0.42 to -0.06, P = 0.01, respectively).Conclusions: More extensive baseline visual and AMFM fibrosis (as measured by GGR densities) is independently associated with elevated hazard for disease progression. Postbaseline change in AMFM-measured and visually measured GGR densities are modestly correlated with change in FVC. AMFM-measured fibrosis is an automated adjunct to existing prognostic markers and may allow for study enrichment with subjects at increased disease progression risk. [ABSTRACT FROM AUTHOR]

Rationale: The predominant cause of chronic lung allograft failure is small airway obstruction arising from bronchiolitis obliterans. However, clinical methodologies for evaluating presence and degree of small airway disease are lacking.Objectives: To determine if parametric response mapping (PRM), a novel computed tomography voxel-wise methodology, can offer insight into chronic allograft failure phenotypes and provide prognostic information following spirometric decline.Methods: PRM-based computed tomography metrics quantifying functional small airways disease (PRMfSAD) and parenchymal disease (PRMPD) were compared between bilateral lung transplant recipients with irreversible spirometric decline and control subjects matched by time post-transplant (n = 22). PRMfSAD at spirometric decline was evaluated as a prognostic marker for mortality in a cohort study via multivariable restricted mean models (n = 52).Measurements and Main Results: Patients presenting with an isolated decline in FEV1 (FEV1 First) had significantly higher PRMfSAD than control subjects (28% vs. 15%; P = 0.005), whereas patients with concurrent decline in FEV1 and FVC had significantly higher PRMPD than control subjects (39% vs. 20%; P = 0.02). Over 8.3 years of follow-up, FEV1 First patients with PRMfSAD greater than or equal to 30% at spirometric decline lived on average 2.6 years less than those with PRMfSAD less than 30% (P = 0.004). In this group, PRMfSAD greater than or equal to 30% was the strongest predictor of survival in a multivariable model including bronchiolitis obliterans syndrome grade and baseline FEV1% predicted (P = 0.04).Conclusions: PRM is a novel imaging tool for lung transplant recipients presenting with spirometric decline. Quantifying underlying small airway obstruction via PRMfSAD helps further stratify the risk of death in patients with diverse spirometric decline patterns. [ABSTRACT FROM AUTHOR]

Rationale: Aging is associated with reduced FEV1 to FVC ratio (FEV1/FVC), hyperinflation, and alveolar enlargement, but little is known about how age affects small airways.Objectives: To determine if chest computed tomography (CT)-assessed functional small airway would increase with age, even among asymptomatic individuals.Methods: We used parametric response mapping analysis of paired inspiratory/expiratory CTs to identify functional small airway abnormality (PRMFSA) and emphysema (PRMEMPH) in the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) cohort. Using adjusted linear regression models, we analyzed associations between PRMFSA and age in subjects with or without airflow obstruction. We subdivided participants with normal spirometry based on respiratory-related impairment (6-minute-walk distance <350 m, modified Medical Research Council ≥2, chronic bronchitis, St. George's Respiratory Questionnaire >25, respiratory events requiring treatment [antibiotics and/or steroids or hospitalization] in the year before enrollment).Measurements and Main Results: Among 580 never- and ever-smokers without obstruction or respiratory impairment, PRMFSA increased 2.7% per decade, ranging from 3.6% (ages 40-50 yr) to 12.7% (ages 70-80 yr). PRMEMPH increased nonsignificantly (0.1% [ages 40-50 yr] to 0.4% [ages 70-80 yr]; P = 0.34). Associations were similar among nonobstructed individuals with respiratory-related impairment. Increasing PRMFSA in subjects without airflow obstruction was associated with increased FVC (P = 0.004) but unchanged FEV1 (P = 0.94), yielding lower FEV1/FVC ratios (P < 0.001). Although emphysema was also significantly associated with lower FEV1/FVC (P = 0.04), its contribution relative to PRMFSA in those without airflow obstruction was limited by its low burden.Conclusions: In never- and ever-smokers without airflow obstruction, aging is associated with increased FVC and CT-defined functional small airway abnormality regardless of respiratory symptoms. [ABSTRACT FROM AUTHOR]

Background: Surgical lung biopsy (SLB) is invasive and not possible in all patients with undiagnosed interstitial lung disease (ILD). We hypothesized that transbronchial biopsy (TBB) findings combined with clinical and high-resolution CT (HRCT) data leads to a confident diagnosis congruent to SLB and therefore avoids the need for SLB in some patients.Methods: We evaluated 33 patients being investigated for suspected ILD who underwent HRCT, TBB, and SLB. First, clinicians, radiologists, and a pathologist reviewed the clinical information and HRCT and TBB findings. Clinicians were asked to provide a diagnosis and were also asked if SLB was needed for a more confident diagnosis. Subsequently, the clinical, HRCT, and SLB data were reviewed, and the same participants were asked to provide a final diagnosis. Clinician consensus and overall agreement between TBB- and SLB-based diagnoses were calculated.Results: Four patients had definite usual interstitial pneumonia (UIP) on HRCT and would not be considered for biopsy using current guidelines. Of the 29 patients without a definitive HRCT diagnosis, the clinicians felt confident of the diagnosis (ie, would not recommend SLB) in six cases. In these cases, there was 100% agreement between TBB and SLB diagnoses. UIP was the most common diagnosis (n = 3) and was associated with an HRCT diagnosis of possible UIP/nonspecific interstitial pneumonia-like. Agreement was poor (33%) between TBB and SLB diagnoses when confidence in the TBB diagnosis was low.Conclusions: Information from TBB, when combined with clinical and HRCT data, may provide enough information to make a confident and accurate diagnosis in approximately 20% to 30% of patients with ILD. [ABSTRACT FROM AUTHOR]

Purpose This study aimed to investigate the breast dose reduction potential of a breast-positioning ( BP) technique for thoracic CT examinations with organ-based tube current modulation ( OTCM). Methods This study included 13 female anthropomorphic computational phantoms ( XCAT, age range: 27-65 y.o., weight range: 52-105.8 kg). Each phantom was modified to simulate three breast sizes in standard supine geometry. The modeled breasts were then morphed to emulate BP that constrained the majority of the breast tissue inside the 120° anterior tube current ( mA) reduction zone. The OTCM mA value was modeled using a ray-tracing program, which reduced the mA to 20% in the anterior region with a corresponding increase to the posterior region. The organ doses were estimated by a validated Monte Carlo program for a typical clinical CT system ( SOMATOM Definition Flash, Siemens Healthcare). The simulated organ doses and organ doses normalized by CTDIvol were used to compare three CT protocols: attenuation-based tube current modulation ( ATCM), OTCM, and OTCM with BP (OTCMBP). Results On average, compared to ATCM, OTCM reduced breast dose by 19.3 ± 4.5%, whereas OTCMBP reduced breast dose by 38.6 ± 8.1% (an additional 23.8 ± 9.4%). The dose saving of OTCMBP was more significant for larger breasts (on average 33, 38, and 44% reduction for 0.5, 1, and 2 kg breasts, respectively). Compared to ATCM, OTCMBP also reduced thymus and heart dose by 15.1 ± 7.4% and 15.9 ± 6.2% respectively. Conclusions In thoracic CT examinations, OTCM with a breast-positioning technique can markedly reduce unnecessary exposure to radiosensitive organs in anterior chest wall, specifically breast tissue. The breast dose reduction is more notable for women with larger breasts. [ABSTRACT FROM AUTHOR]

Purpose: The authors are developing an automated method to identify the best-quality coronary arterial segment from multiple-phase coronary CT angiography (cCTA) acquisitions, which may be used by either interpreting physicians or computer-aided detection systems to optimally and efficiently utilize the diagnostic information available in multiple-phase cCTA for the detection of coronary artery disease. Methods: After initialization with a manually identified seed point, each coronary artery tree is automatically extracted from multiple cCTA phases using our multiscale coronary artery response enhancement and 3D rolling balloon region growing vessel segmentation and tracking method. The coronary artery trees from multiple phases are then aligned by a global registration using an affine transformation with quadratic terms and nonlinear simplex optimization, followed by a local registration using a cubic B-spline method with fast localized optimization. The corresponding coronary arteries among the available phases are identified using a recursive coronary segment matching method. Each of the identified vessel segments is transformed by the curved planar reformation (CPR) method. Four features are extracted from each corresponding segment as quality indicators in the original computed tomography volume and the straightened CPR volume, and each quality indicator is used as a voting classifier for the arterial segment. A weighted voting ensemble (WVE) classifier is designed to combine the votes of the four voting classifiers for each corresponding segment. The segment with the highest WVE vote is then selected as the best-quality segment. In this study, the training and test sets consisted of 6 and 20 cCTA cases, respectively, each with 6 phases, containing a total of 156 cCTA volumes and 312 coronary artery trees. An observer preference study was also conducted with one expert cardiothoracic radiologist and four nonradiologist readers to visually rank vessel segment quality. The performance of our automated method was evaluated by comparing the automatically identified best-quality segments identified by the computer to those selected by the observers. Results: For the 20 test cases, 254 groups of corresponding vessel segments were identified after multiple phase registration and recursive matching. The AI-BQ segments agreed with the radiologist's top 2 ranked segments in 78.3% of the 254 groups (Cohen's kappa 0.60), and with the 4 nonradiologist observers in 76.8%, 84.3%, 83.9%, and 85.8% of the 254 groups. In addition, 89.4% of the AI-BQ segments agreed with at least two observers' top 2 rankings, and 96.5% agreed with at least one observer's top 2 rankings. In comparison, agreement between the four observers' top ranked segment and the radiologist's top 2 ranked segments were 79.9%, 80.7%, 82.3%, and 76.8%, respectively, with kappa values ranging from 0.56 to 0.68. Conclusions: The performance of our automated method for selecting the best-quality coronary segments from a multiple-phase cCTA acquisition was comparable to the selection made by human observers. This study demonstrates the potential usefulness of the automated method in clinical practice, enabling interpreting physicians to fully utilize the best available information in cCTA for diagnosis of coronary disease, without requiring manual search through the multiple phases and minimizing the variability in image phase selection for evaluation of coronary artery segments across the diversity of human readers with variations in expertise. C 2016 American Association of Physicists in Medicine. [ABSTRACT FROM AUTHOR]

Rationale: The small conducting airways are the major site of airflow obstruction in chronic obstructive pulmonary disease and may precede emphysema development.Objectives: We hypothesized a novel computed tomography (CT) biomarker of small airway disease predicts FEV1 decline.Methods: We analyzed 1,508 current and former smokers from COPDGene with linear regression to assess predictors of change in FEV1 (ml/yr) over 5 years. Separate models for subjects without and with airflow obstruction were generated using baseline clinical and physiologic predictors in addition to two novel CT metrics created by parametric response mapping (PRM), a technique pairing inspiratory and expiratory CT images to define emphysema (PRM(emph)) and functional small airways disease (PRM(fSAD)), a measure of nonemphysematous air trapping.Measurements and Main Results: Mean (SD) rate of FEV1 decline in ml/yr for GOLD (Global Initiative for Chronic Obstructive Lung Disease) 0-4 was as follows: 41.8 (47.7), 53.8 (57.1), 45.6 (61.1), 31.6 (43.6), and 5.1 (35.8), respectively (trend test for grades 1-4; P < 0.001). In multivariable linear regression, for participants without airflow obstruction, PRM(fSAD) but not PRM(emph) was associated with FEV1 decline (P < 0.001). In GOLD 1-4 participants, both PRM(fSAD) and PRM(emph) were associated with FEV1 decline (P < 0.001 and P = 0.001, respectively). Based on the model, the proportional contribution of the two CT metrics to FEV1 decline, relative to each other, was 87% versus 13% and 68% versus 32% for PRM(fSAD) and PRM(emph) in GOLD 1/2 and 3/4, respectively.Conclusions: CT-assessed functional small airway disease and emphysema are associated with FEV1 decline, but the association with functional small airway disease has greatest importance in mild-to-moderate stage chronic obstructive pulmonary disease where the rate of FEV1 decline is the greatest. Clinical trial registered with www.clinicaltrials.gov (NCT 00608764). [ABSTRACT FROM AUTHOR]

Purpose: A test object (phantom) is an important tool to evaluate comparability and stability of CT scanners used in multicenter and longitudinal studies. However, there are many sources of error that can interfere with the test object-derived quantitative measurements. Here the authors investigated three major possible sources of operator error in the use of a test object employed to assess pulmonary density-related as well as airway-related metrics. Methods: Two kinds of experiments were carried out to assess measurement variability caused by imperfect scanning status. The first one consisted of three experiments. A COPDGene test object was scanned using a dual source multidetector computed tomographic scanner (Siemens Somatom Flash) with the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) inspiration protocol (120 kV, 110 mAs, pitch = 1, slice thickness = 0.75 mm, slice spacing = 0.5 mm) to evaluate the effects of tilt angle, water bottle offset, and air bubble size. After analysis of these results, a guideline was reached in order to achieve more reliable results for this test object. Next the authors applied the above findings to 2272 test object scans collected over 4 years as part of the SPIROMICS study. The authors compared changes of the data consistency before and after excluding the scans that failed to pass the guideline. Results: This study established the following limits for the test object: tilt index ≤0.3, water bottle offset limits of [-6.6 mm, 7.4 mm], and no air bubble within the water bottle, where tilt index is a measure incorporating two tilt angles around x- and y-axis. With 95% confidence, the density measurement variation for all five interested materials in the test object (acrylic, water, lung, inside air, and outside air) resulting from all three error sources can be limited to ±0.9 HU (summed in quadrature), when all the requirements are satisfied. The authors applied these criteria to 2272 SPIROMICS scans and demonstrated a significant reduction in measurement variation associated with the test object. Conclusions: Three operator errors were identified which significantly affected the usability of the acquired scan images of the test object used for monitoring scanner stability in a multicenter study. The authors' results demonstrated that at the time of test object scan receipt at a radiology core laboratory, quality control procedures should include an assessment of tilt index, water bottle offset, and air bubble size within the water bottle. Application of this methodology to 2272 SPIROMICS scans indicated that their findings were not limited to the scanner make and model used for the initial test but was generalizable to both Siemens and GE scanners which comprise the scanner types used within the SPIROMICS study. [ABSTRACT FROM AUTHOR]

Objective: Systemic lupus erythematosus patients are frequently evaluated for chest pain and may have multiple pulmonary embolism (PE) computed tomography (CT) scans. This study was undertaken to determine the incidence of pulmonary embolism in the University of Michigan Lupus Cohort patients who have undergone PE CT scans and to estimate the associated increased risk of breast and lung cancer from radiation exposure.Methods: We reviewed records of patients in the University of Michigan Lupus Cohort (n = 854) and determined the number and outcome of PE CT scans. Radimetrics software was used to perform individualized calculations of radiation dose to the lung and breast of each patient. We used this dose information, the patient's age at the time of scan, and risks according to the Biological Effects of Ionizing Radiation, report VII, to estimate the increased incidence risks of breast and lung cancer.Results: A total of 182 of 856 patients (21%) underwent 357 PE CT scans. The overall rate of positivity was 7.5%. For patients undergoing their first through third scans, the rate of positivity for PE was 8.8%, whereas patients undergoing their fourth through tenth scans had 1.6% positivity. The highest increase in incidence risk was 0.87% for breast and 0.62% for lung.Conclusion: Patients with multiple previous PE CT scans had lower likelihood of a positive result on subsequent scans and higher risks of malignancy. The magnitude of risk should not discourage performance of PE CT when clinically indicated. [ABSTRACT FROM AUTHOR]

Background: Lung cancer screening with low-dose computed tomography (LDCT) has been recommended, based primarily on the results of the NLST (National Lung Screening Trial). The American College of Radiology recently released Lung-RADS, a classification system for LDCT lung cancer screening. Objective: To retrospectively apply the Lung-RADS criteria to the NLST. Design: Secondary analysis of a group from a randomized trial. Setting: 33 U.S. screening centers. Patients: Participants were randomly assigned to the LDCT group of the NLST, were aged 55 to 74 years, had at least a 30-pack-year history of smoking, and were current smokers or had quit within the past 15 years. Intervention: 3 annual LDCT lung cancer screenings. Measurements: Lung-RADS classifications for LDCT screenings. Lung-RADS categories 1 to 2 constitute negative screening results, and categories 3 to 4 constitute positive results. Results: Of 26 722 LDCT group participants, 26 455 received a baseline screening; 48 671 screenings were done after baseline. At baseline, the false-positive result rate (1 minus the specificity rate) for Lung-RADS was 12.8% (95% CI, 12.4% to 13.2%) versus 26.6% (CI, 26.1% to 27.1%) for the NLST; after baseline, the false-positive result rate was 5.3% (CI, 5.1% to 5.5%) for Lung-RADS versus 21.8% (CI, 21.4% to 22.2%) for the NLST. Baseline sensitivity was 84.9% (CI, 80.8% to 89.0%) for Lung-RADS versus 93.5% (CI, 90.7% to 96.3%) for the NLST, and sensitivity after baseline was 78.6% (CI, 74.6% to 82.6%) for Lung-RADS versus 93.8% (CI, 91.4% to 96.1%) for the NLST. Limitation: Lung-RADS criteria were applied retrospectively. Conclusion: Lung-RADS may substantially reduce the false-positive result rate; however, sensitivity is also decreased. The effect of using Lung-RADS criteria in clinical practice must be carefully studied. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The longitudinal relationship between regional air trapping and emphysema remains unexplored. We have sought to demonstrate the utility of parametric response mapping (PRM), a computed tomography (CT)-based biomarker, for monitoring regional disease progression in chronic obstructive pulmonary disease (COPD) patients, linking expiratory- and inspiratory-based CT metrics over time.Materials and Methods: Inspiratory and expiratory lung CT scans were acquired from 89 COPD subjects with varying Global Initiative for Chronic Obstructive Lung Disease (GOLD) status at 30 days (n = 13) or 1 year (n = 76) from baseline as part of the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) clinical trial. PRMs of CT data were used to quantify the relative volumes of normal parenchyma (PRM(Normal)), emphysema (PRM(Emph)), and functional small airways disease (PRM(fSAD)). PRM measurement variability was assessed using the 30-day interval data. Changes in PRM metrics over a 1-year period were correlated to pulmonary function (forced expiratory volume at 1 second [FEV1]). A theoretical model that simulates PRM changes from COPD was compared to experimental findings.Results: PRM metrics varied by ∼6.5% of total lung volume for PRM(Normal) and PRM(fSAD) and 1% for PRM(Emph) when testing 30-day repeatability. Over a 1-year interval, only PRM(Emph) in severe COPD subjects produced significant change (19%-21%). However, 11 of 76 subjects showed changes in PRM(fSAD) greater than variations observed from analysis of 30-day data. Mathematical model simulations agreed with experimental PRM results, suggesting fSAD is a transitional phase from normal parenchyma to emphysema.Conclusions: PRM of lung CT scans in COPD patients provides an opportunity to more precisely characterize underlying disease phenotypes, with the potential to monitor disease status and therapy response. [ABSTRACT FROM AUTHOR]

Introduction Left ventricular ( LV) and right ventricular ( RV) volumetric and functional parameters are important biomarkers for morbidity and mortality in patients with heart failure. Purpose To retrospectively determine reference mean values of LV and RV volume, function and mass normalised by age, gender and body surface area ( BSA) from retrospectively electrocardiographically gated 64-slice cardiac computed tomography ( CCT) by using automated analysis software in healthy adults. Materials and Methods The study was approved by the institutional review board with a waiver of informed consent. Seventy-four healthy subjects (49% female, mean age 49.6 ± 11) free of hypertension and hypercholesterolaemia with a normal CCT formed the study population. Analyses of LV and RV volume (end-diastolic, end-systolic and stroke volumes), function (ejection fraction), LV mass and inter-rater reproducibility were performed with commercially available analysis software capable of automated contour detection. General linear model analysis was performed to assess statistical significance by age group after adjustment for gender and BSA. Bland-Altman analysis assessed the inter-rater agreement. Results The reference range for LV and RV volume, function, and LV mass was normalised to age, gender and BSA. Statistically significant differences were noted between genders in both LV mass and RV volume ( P-value < 0.0001). Age, in concert with gender, was associated with significant differences in RV end-diastolic volume and LV ejection fraction ( P-values 0.027 and 0.03). Bland-Altman analysis showed acceptable limits of agreement (±1.5% for ejection fraction) without systematic error. Conclusion LV and RV volume, function and mass normalised to age, gender and BSA can be reported from CCT datasets, providing additional information important for patient management. [ABSTRACT FROM AUTHOR]

Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (p = 0.01). At the core-level, PRM-defined parenchymal disease, a potential readout of RAS, was found to correlate to neutrophil and collagen I levels (p < 0.05). We demonstrate the relationship of structural and biological markers to the CT-based distribution and arrangement of PRM-derived readouts of BOS and RAS. [ABSTRACT FROM AUTHOR]

Purpose: The buildup of noncalcified plaques (NCPs) that are vulnerable to rupture in coronary arteries is a risk for myocardial infarction. Interpretation of coronary CT angiography (cCTA) to search for NCP is a challenging task for radiologists due to the low CT number of NCP, the large number of coronary arteries, and multiple phase CT acquisition. The authors conducted a preliminary study to develop machine learning method for automated detection of NCPs in cCTA. Methods: With IRB approval, a data set of 83 ECG-gated contrast enhanced cCTA scans with 120 NCPs was collected retrospectively from patient files. A multiscale coronary artery response and rolling balloon region growing (MSCAR-RBG) method was applied to each cCTA volume to extract the coronary arterial trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for NCP candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. The NCP candidates were then characterized by a luminal analysis that used 3D geometric features to quantify the shape information and gray-level features to evaluate the density of the NCP candidates. With machine learning techniques, useful features were identified and combined into an NCP score to differentiate true NCPs from false positives (FPs). To evaluate the effectiveness of the image analysis methods, the authors performed tenfold cross-validation with the available data set. Receiver operating characteristic (ROC) analysis was used to assess the classification performance of individual features and the NCP score. The overall detection performance was estimated by free response ROC (FROC) analysis. Results: With our TSG prescreening method, a prescreening sensitivity of 92.5% (111/120) was achieved with a total of 1181 FPs (14.2 FPs/scan). On average, six features were selected during the tenfold cross-validation training. The average area under the ROC curve (AUC) value for training was 0.87 ± 0.01 and the AUC value for validation was 0.85 ± 0.01. Using the NCP score, FROC analysis of the validation set showed that the FP rates were reduced to 3.16, 1.90, and 1.39 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. Conclusions: The topological soft-gradient prescreening method in combination with the luminal analysis for FP reduction was effective for detection of NCPs in cCTA, including NCPs causing positive or negative vessel remodeling. The accuracy of vessel segmentation, tracking, and centerline identification has a strong impact on NCP detection. Studies are underway to further improve these techniques and reduce the FPs of the CADe system. [ABSTRACT FROM AUTHOR]

Purpose: The authors are developing a computer-aided detection system to assist radiologists in analysis of coronary artery disease in coronary CT angiograms (cCTA). This study evaluated the accuracy of the authors' coronary artery segmentation and tracking method which are the essential steps to define the search space for the detection of atherosclerotic plaques. Methods: The heart region in cCTA is segmented and the vascular structures are enhanced using the authors' multiscale coronary artery response (MSCAR) method that performed 3D multiscale filtering and analysis of the eigenvalues of Hessian matrices. Starting from seed points at the origins of the left and right coronary arteries, a 3D rolling balloon region growing (RBG) method that adapts to the local vessel size segmented and tracked each of the coronary arteries and identifies the branches along the tracked vessels. The branches are queued and subsequently tracked until the queue is exhausted. With Institutional Review Board approval, 62 cCTA were collected retrospectively from the authors' patient files. Three experienced cardiothoracic radiologists manually tracked and marked center points of the coronary arteries as reference standard following the 17-segment model that includes clinically significant coronary arteries. Two radiologists visually examined the computer-segmented vessels and marked the mistakenly tracked veins and noisy structures as false positives (FPs). For the 62 cases, the radiologists marked a total of 10191 center points on 865 visible coronary artery segments. Results: The computer-segmented vessels overlapped with 83.6% (8520/10191) of the center points. Relative to the 865 radiologist-marked segments, the sensitivity reached 91.9% (795/865) if a true positive is defined as a computer-segmented vessel that overlapped with at least 10% of the reference center points marked on the segment. When the overlap threshold is increased to 50% and 100%, the sensitivities were 86.2% and 53.4%, respectively. For the 62 test cases, a total of 55 FPs were identified by radiologist in 23 of the cases. Conclusions: The authors' MSCAR-RBG method achieved high sensitivity for coronary artery segmentation and tracking. Studies are underway to further improve the accuracy for the arterial segments affected by motion artifacts, severe calcified and noncalcified soft plaques, and to reduce the false tracking of the veins and other noisy structures. Methods are also being developed to detect coronary artery disease along the tracked vessels. [ABSTRACT FROM AUTHOR]

Lung cancer screening with low dose computed tomography (CT) is the only method ever proven to reduce lung cancer--specific mortality in high-risk current and former cigarette smokers. Radiation exposure from annual screening CT examinations and subsequent CT and nuclear medicine testing to further evaluate positive screening CTs is sometimes raised as a reason to avoid screening and is often misunderstood. With all testing, there are potential benefits and risks. As we sit on the brink of widespread adoption of lung cancer screening CT, we aim to explain why the risks associated with radiation exposure from lung cancer screening are very low and should not be used to avoid screening or dissuade individuals who qualify for screening CT to participate in a lung cancer screening program. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To determine the impact of coronary computed tomography angiography (CCTA) on treatment decisions made by cardiac surgeons and to explore barriers to implementation of CCTA as first-line imaging workup before coronary artery bypass. Materials and Methods: Tertiary academic medical center retrospective cohort study. Questionnaires administered to three cardiac surgeons regarding diagnosis and treatment decisions with level of confidence in decision making for 30 chest pain patients, after reviewing deidentified catheter coronary angiogram (CCA) and CCTA images on the same patients, several weeks apart. Results: All surgeons tended to report more severe disease with CCTA than CCA (P < .05). Treatment decisions differed in 12 (40%), 14 (47%), and 18 (60%) patients within each individual surgeon based on CCTA images compared to CCA images (P = .11, .01, and .02, respectively). Confidence levels were significantly higher with CCA for the diagnosis and treatment of coronary artery disease for all three surgeons (P < .05). There was a tendency toward more invasive treatment choices (angioplasty, stent insertion, or coronary artery bypass graft) when making treatment decisions based upon CCTA images. Conclusions: Severity of disease may be overestimated by cardiac surgeons when using CCTA as a decision-making test, tending toward more invasive treatment choices compared to CCA. Additionally, cardiac surgeons may be more confident with their diagnosis and treatment based on CCA. These may be important limitations in the application of CCTA in clinical practice and may reflect the maturation of CCTA use in practice, diffusion of the technology and/or a reflection of the technology itself. [Copyright &y& Elsevier]

Purpose: Lung segmentation is a fundamental step in many image analysis applications for lung diseases and abnormalities in thoracic computed tomography (CT). The authors have previously developed a lung segmentation method based on expectation-maximization (EM) analysis and morphological operations (EMM) for our computer-aided detection (CAD) system for pulmonary embolism (PE) in CT pulmonary angiography (CTPA). However, due to the large variations in pathology that may be present in thoracic CT images, it is difficult to extract the lung regions accurately, especially when the lung parenchyma contains extensive lung diseases. The purpose of this study is to develop a new method that can provide accurate lung segmentation, including those affected by lung diseases. Methods: An iterative neutrosophic lung segmentation (INLS) method was developed to improve the EMM segmentation utilizing the anatomic features of the ribs and lungs. The initial lung regions (ILRs) were extracted using our previously developed EMM method, in which the ribs were extracted using 3D hierarchical EM segmentation and the ribcage was constructed using morphological operations. Based on the anatomic features of ribs and lungs, the initial EMM segmentation was refined using INLS to obtain the final lung regions. In the INLS method, the anatomic features were mapped into a neutrosophic domain, and the neutrosophic operation was performed iteratively to refine the ILRs. With IRB approval, 5 and 58 CTPA scans were collected retrospectively and used as training and test sets, of which 2 and 34 cases had lung diseases, respectively. The lung regions manually outlined by an experienced thoracic radiologist were used as reference standard for performance evaluation of the automated lung segmentation. The percentage overlap area (POA), the Hausdorff distance (Hdist), and the average distance (AvgDist) of the lung boundaries relative to the reference standard were used as performance metrics. Results: The proposed method achieved larger POAs and smaller distance errors than the EMM method. For the 58 test cases, the average POA, Hdist, and AvgDist were improved from 85.4 ± 18.4%, 22.6 ± 29.4 mm, and 3.5 ± 5.4 mm using EMM to 91.2 ± 6.7%, 16.0 ± 11.3 mm, and 2.5 ± 1.0 mm using INLS, respectively. The improvements were statistically significant (p < 0.05). To evaluate the accuracy of the INLS method in the identification of the lung boundaries affected by lung diseases, the authors separately analyzed the performance of the proposed method on the cases with versus without the lung diseases. The results showed that the cases without lung diseases were segmented more accurately than the cases with lung diseases by both the EMM and the INLS methods, but the INLS method achieved better performance than the EMM method in both cases. Conclusions: The new INLS method utilizing the anatomic features of the rib and lung significantly improved the accuracy of lung segmentation, especially for the cases affected by lung diseases. Improvement in lung segmentation will facilitate many image analysis tasks and CAD applications for lung diseases and abnormalities in thoracic CT, including automated PE detection. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The aim of this study was to evaluate the effect of computer-aided diagnosis (CAD) on radiologists'' estimates of the likelihood of malignancy of lung nodules on computed tomographic (CT) imaging. Methods and Materials: A total of 256 lung nodules (124 malignant, 132 benign) were retrospectively collected from the thoracic CT scans of 152 patients. An automated CAD system was developed to characterize and provide malignancy ratings for lung nodules on CT volumetric images. An observer study was conducted using receiver-operating characteristic analysis to evaluate the effect of CAD on radiologists'' characterization of lung nodules. Six fellowship-trained thoracic radiologists served as readers. The readers rated the likelihood of malignancy on a scale of 0% to 100% and recommended appropriate action first without CAD and then with CAD. The observer ratings were analyzed using the Dorfman-Berbaum-Metz multireader, multicase method. Results: The CAD system achieved a test area under the receiver-operating characteristic curve (A z) of 0.857 ± 0.023 using the perimeter, two nodule radii measures, two texture features, and two gradient field features. All six radiologists obtained improved performance with CAD. The average A z of the radiologists improved significantly (P < .01) from 0.833 (range, 0.817–0.847) to 0.853 (range, 0.834–0.887). Conclusion: CAD has the potential to increase radiologists'' accuracy in assessing the likelihood of malignancy of lung nodules on CT imaging. [Copyright &y& Elsevier]

Rationale and Objectives: The goal was to investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, because the latter are always qualified with respect to a given size range of nodules.Materials and Methods: This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium (LIDC) using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a unidimensional and two bidimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the interradiologist variation, while those with at least one marking were used to examine the difference between the metrics.Results: The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high interobserver variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges for the three-dimensional, unidimensional, and two bidimensional size metrics, respectively (in mm): 0.49-1.25, 0.67-2.55, 0.78-2.11, and 0.96-2.69. Also, a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on unidimensional, and the two bidimensional size measurements of 10 mm were 7.32, 7.72, and 6.29 mm, respectively.Conclusions: The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish “truth” for algorithm development, training, and testing. The integrity of this “truth,” however, must be established before investigators commit to this “gold standard” as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the “truth” collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database. Materials and Methods: One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the “blinded read phase”), radiologists independently identified and annotated lesions, assigning each to one of three categories: “nodule ≥3 mm,” “nodule <3 mm,” or “non-nodule ≥3 mm.” For the second read (the “unblinded read phase”), the same radiologists independently evaluated the same CT scans, but with all of the annotations from the previously performed blinded reads presented; each radiologist could add to, edit, or delete their own marks; change the lesion category of their own marks; or leave their marks unchanged. The post-unblinded read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of identification of potential errors introduced during the complete image annotation process and correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional. Results: A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process. Conclusions: The establishment of “truth” must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems. [Copyright &y& Elsevier]

Rationale and Objectives: To evaluate the interobserver agreement of readers in evaluating pulmonary venous anatomy and in measuring pulmonary vein ostial diameters and distance to first bifurcation. Materials and Methods: This study was approved by our institutional review board. Thin-section contrast material-enhanced multidetector computed tomography examinations of the thorax were retrospectively reviewed in 200 consecutive patients (38 females and 162 males), age 24-79 years (mean, 52.8) referred for imaging before radiofrequency ablation therapy for atrial fibrillation. For each patient, pulmonary venous anatomy and drainage patterns including the number of venous ostia was assessed independently by experienced cardiothoracic radiologists. Pulmonary vein ostial diameter and distance to the first bifurcation of the four major pulmonary veins (right inferior and superior, left inferior and superior), the middle lobe pulmonary vein, and any anomalous pulmonary veins (common trunks and accessory veins) were measured independently at a workstation. Interreader assessment of pulmonary venous anatomy was evaluated using the Kappa statistic. Interreader variation in measurements of venous diameter and distant to first bifurcation were estimated by Bland-Altman plots and Pitman's test of difference in variance. Results: Very good to excellent interreader agreement in detection of anomalous pulmonary venous anatomy, middle lobe pulmonary venous drainage, and other thoracic venous anomalies. No significant variation between readers in pulmonary vein ostial diameter measurements for the four major and middle lobe pulmonary veins, or the anomalous pulmonary veins. Significant interreader variability was noted in measurements of the pulmonary vein distance to first bifurcation for the right inferior (P = .017), middle lobe (P = .005), and left inferior (P = .015) pulmonary veins. Conclusions: There is excellent interobserver agreement when evaluating normal and anomalous pulmonary venous drainage patterns, and when measuring normal or anomalous pulmonary vein diameters. However, measurements of distances to first bifurcation were less reliable across readers. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To determine the diagnostic quality, performance characteristics and interreader reliability of computed tomography pulmonary angiography (CTPA) and venography (CTV) in intensive care unit (ICU) patients with suspected venous thromboembolism (VTE). Materials and Methods: A total of 100 consecutive ICU patient CT examinations performed for clinically suspected VTE on a four-row CT scanner were reviewed. Three readers rated the diagnostic quality of each CTPA and CTV examination as excellent, acceptable, or nondiagnostic. Readers scored the overall determination for pulmonary embolism (PE) and deep venous thrombosis (DVT) using a 5-point scale, and scored the determination for PE by anatomic level. Receiver operator characteristic (ROC) analysis was performed for each reader and the original clinical report, using consensus interpretation as the reference standard. Interobserver variability for PE and DVT was determined using kappa analysis, and was stratified by examination quality. Results: A total of 25% of CTPA examinations were nondiagnostic, most commonly because of motion artifact and poor contrast opacification. A total of 24% of CTV examinations were nondiagnostic, most commonly because of poor contrast opacification and metallic hardware. Using receiver operating characteristic analysis, the areas under the curve (Az) for PE diagnosis were 0.875, 0.923, 0.888, and 0.674 for the three readers and clinical reading, respectively, and for DVT diagnosis were 0.842, 0.859, 0.952 and 0.669. Interobserver agreement for detection of PE was moderate at the supralobar level (kappa = 0.55), very good at the lobar level (kappa = 0.69), and moderate for segmental (kappa = 0.54) and subsegmental arteries (kappa = 0.44). Overall reader agreement was good for excellent/good quality CTPA examinations (kappa = 0.52-0.56), and poor when examination quality was poor (kappa = 0.06). Conclusions: CTPA and CTV are sufficiently accurate and reliable techniques for evaluating VTE in ICU patients, particularly in light of patient complexity. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: A critical element in determining biologic behavior of pulmonary nodules is volume and temporal volume change. We evaluate variability in nodule volume among readers and measuring methods. Materials and Methods: 55 small (<2 cm) lung nodules were measured in long- and short-axis dimensions independently by 4 radiologists, using 3 methods: 1) hard copy, 2) GE Advantage Windows workstation (GE Healthcare, Milwaukee, WI), 3) Siemens IMACS workstation (Siemens Medical Systems, Iselan, NJ). Nodule margin was recorded as smooth, lobulated, or spiculated. Volume was calculated from diameter measurements. Variability in nodule volume was evaluated within each reader, between readers, and across measurement tools. Results: Mean nodule short-axis diameter was 5.3 mm; mean long-axis diameter 7.2 mm. There was statistically significant variation among readers and measurement method for nodule volume. Volume was significantly larger using hard-copy measurements (51.9%-54.1% variation; P < .0001) than either workstation, and not different between workstations. There was greater intraobserver variability in volume using the hard-copy method, and no difference between workstation methods. Volumes based on measurements from one reader were consistently lower than those from other readers (P = < .001, .003, and .02); volume was consistently larger for another reader (P < .0001, .03, and .12). Reader agreement for nodule margin was good to excellent. Conclusion: Considerable interobserver and intraobserver variability in measuring nodules exists using hard-copy and computer tools. Since a small change in diameter indicates a much larger change in volume, this may be significant when using early repeat CT to follow small pulmonary nodules. Computer-aided diagnostic tools that reproducibly measure nodule volume are strongly needed. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to determine the effect of left atrial circumferential ablation on the size of the left atrium and pulmonary veins (PVs). Background: The long-term effects of left atrial circumferential ablation on left atrial and PV size and anatomy have not been analyzed in quantitative fashion. Methods: PV and left atrial sizes were analyzed in 41 consecutive patients (mean age 54 +/- 12 years) with paroxysmal (n = 25) or chronic (n = 16) atrial fibrillation. Computed tomography of the chest with three-dimensional reconstruction was performed before and 4 +/- 2 months after left atrial circumferential ablation. Left atrial circumferential ablation was performed to encircle the PVs 1 to 2 cm from the ostia, using a power output of 70 W. Additional ablation lines were created in the posterior left atrium and mitral isthmus. Radiofrequency energy also was delivered within the circles and at the PV ostia in 51% of patients at a reduced power output of 35 W. Results: At 6 months, 36 patients (88%) were in sinus rhythm without antiarrhythmic drug therapy, including 3 patients (7%) who developed persistent left atrial flutter and underwent subsequent successful ablation of atrial flutter. There was a 15 +/- 16% decrease in left atrial volume (P < .01) and 10 +/- 35% decrease in PV ostial area (P < .01), without focal narrowing, in patients with a successful outcome. Focal PV stenosis did not occur in any of the 41 patients. Conclusions: Maintenance of sinus rhythm after left atrial circumferential ablation is associated with reduced left atrial and PV ostial size. Left atrial circumferential ablation for atrial fibrillation does not cause PV stenosis. [ABSTRACT FROM AUTHOR]

Pulmonary Vein Anatomy.Introduction: The anatomic arrangement of pulmonary veins (PVs) is variable. No prior studies have quantitatively analyzed the effects of segmental ostial ablation on the PVs. The aim of this study was to determine the effect of segmental ostial radiofrequency ablation on PV anatomy in patients with atrial fibrillation (AF). Methods and Results: Three-dimensional models of the PVs were constructed from computed tomographic (CT) scans in 58 patients with AF undergoing segmental ostial ablation to isolate the PVs and in 10 control subjects without a history of AF. CT scans were repeated approximately 4 months later. PV and left atrial dimensions were measured with digital calipers. Four separate PV ostia were present in 47 subjects; 3 ostia were present in 2 subjects; and 5 ostia were present in 9 subjects. The superior PVs had a larger ostium than the inferior PVs. Patients with AF had a larger left atrial area between the PV ostia and larger ostial diameters than the controls. Segmental ostial ablation resulted in a 1.5 ± 3.2 mm narrowing of the ostial diameter. A 28% to 61% focal stenosis was present 7.6 ± 2.2 mm from the ostium in 3% of 128 isolated PVs. There were no instances of symptomatic PV stenosis during a mean follow-up of 245 ± 105 days . Conclusion: CT of the PVs allows identification of anatomic variants prior to catheter ablation procedures. Segmental ostial ablation results in a significant but small reduction in ostial diameter. Focal stenosis occurs infrequently and is attributable to delivery of radiofrequency energy within the PV. (J Cardiovasc Electrophysiol, Vol. 14, pp. 150-155, February 2003). [ABSTRACT FROM AUTHOR]