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After the results of two large, randomized trials, the global implementation of lung cancer screening is of outmost importance. However, COVID-19 infections occurring at heightened levels during the current global pandemic, and also other respiratory infections, can influence scan interpretation, and screening safety and uptake. Several respiratory infections can lead to lesions that mimic malignant nodules and other imaging changes suggesting malignancy, leading to an increased level of follow-up procedures or even invasive diagnostic procedures. In periods of increased rates of respiratory infections such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) there is also a risk of transmission of these infections to the health care providers, the screenees and patients. This became very clear and evident for the SARS-CoV-2 pandemic and led to a temporary global stop of lung cancer and other cancer screening programs. Data about the optimal management of these situations are not available. The pandemic is still ongoing and there will come further periods of increased respiratory infections, where practical guidance would be helpful. The aim of this report is a) to summarize the data available for possible false positive results due to respiratory infections, b) to evaluate the safety concerns for screening during times of increased respiratory infections, especially during a regional outbreak or an epidemic or pandemic event, c) to provide guidance for these situations and d) to stimulate research and discussions about these scenarios.

After the results of two large, randomized trials, the global implementation of lung cancer screening is of utmost importance. However, coronavirus disease 2019 infections occurring at heightened levels during the current global pandemic and also other respiratory infections can influence scan interpretation and screening safety and uptake. Several respiratory infections can lead to lesions that mimic malignant nodules and other imaging changes suggesting malignancy, leading to an increased level of follow-up procedures or even invasive diagnostic procedures. In periods of increased rates of respiratory infections from severe acute respiratory syndrome coronavirus 2 and others, there is also a risk of transmission of these infections to the health care providers, the screenees, and patients. This became evident with the severe acute respiratory syndrome coronavirus 2 pandemic that led to a temporary global stoppage of lung cancer and other cancer screening programs. Data on the optimal management of these situations are not available. The pandemic is still ongoing and further periods of increased respiratory infections will come, in which practical guidance would be helpful. The aims of this report were: (1) to summarize the data available for possible false-positive results owing to respiratory infections; (2) to evaluate the safety concerns for screening during times of increased respiratory infections, especially during a regional outbreak or an epidemic or pandemic event; (3) to provide guidance on these situations; and (4) to stimulate research and discussions about these scenarios.

Screening with low-dose computed tomography of high-risk individuals with a smoking history reduces lung cancer mortality. Current screening guidelines and eligibility criteria can miss more than 50% of lung cancers, and in some geographic areas, such as East Asia, a large proportion of the missed lung cancers are in never-smokers. Although randomized trials revealed the benefits of screening for people who smoke, these trials generally excluded never-smokers. Thus, the feasibility and effectiveness of lung cancer screening of individuals who never smoked are uncertain. Several known and suspected risk factors for lung cancers in never-smokers such as exposure to secondhand smoke, occupational carcinogens, radon, air pollution, and pulmonary diseases, such as chronic obstructive pulmonary disease and interstitial lung diseases, and intrinsic factors, such as age, are well noted. In this regard, knowledge of risk factors may make possible quantification and prediction of lung cancer risk in never smokers. It is worth considering if and how never smokers could be included in population-based screening programs. As the implementation of these programs is challenging in many countries owing to multiple factors and the epidemiologic differences by global regions, these issues will need to be evaluated in each country taking into account various factors, including accuracy of risk assessment and cost-effectiveness of screening in never smokers. This report aims to outline current knowledge on risk factors for lung cancer in never smokers to propose research strategies for this topic and initiate a broader discussion on lung cancer screening of never smokers. Similar considerations can be made in current and ex-smokers, which do not fulfill the current screening inclusion criteria, but otherwise are at increased risk. Although screening of never smokers may in the future be effectively conducted, current evidence to support widespread implementation of this practice is lacking.

Chunrui Zou,1,2 Frank Li,2,3 Jiwoong Choi,1,4 Babak Haghighi,5 Sanghun Choi,6 Prathish K Rajaraman,1,2 Alejandro P Comellas,7 John D Newell Jnr,8 Chang Hyun Lee,8,9 R Graham Barr,10 Eugene Bleecker,11 Christopher B Cooper,12 David Couper,13 Meilan Han,14 Nadia N Hansel,15 Richard E Kanner,16 Ella A Kazerooni,17 Eric C Kleerup,18 Fernando J Martinez,19 Wanda O’Neal,20 Robert Paine III,16 Stephen I Rennard,21 Benjamin M Smith,22,23 Prescott G Woodruff,24 Eirc A Hoffman,3,7,8 Ching-Long Lin1– 3,8 1Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA; 2IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA; 3Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA; 4Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, KS, USA; 5Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 6School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea; 7Department of Internal Medicine, University of Iowa, Iowa City, IA, USA; 8Department of Radiology, University of Iowa, Iowa City, IA, USA; 9Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea; 10Mailman School of Public Health, Columbia University, New York, NY, USA; 11Department of Medicine, The University of Arizona, Tucson, AZ, USA; 12Department of Physiology, UCLA, Los Angeles, CA, USA; 13Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA; 14Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; 15School of Medicine, Johns Hopkins, Baltimore, MD, USA; 16School of Medicine, University of Utah, Salt Lake City, UT, USA; 17Department of Radiology, University of Michigan, Ann Arbor, MI, USA; 18Department of Medicine, UCLA, Los Angeles, CA, USA; 19Weill Cornell Medicine, Cornell University, New York, NY, USA; 20School of Medicine, University of North Carolina, Chapel Hill, NC, USA; 21Department of Internal Medicine, University of Nebraska College of Medicine, Omaha, NE, USA; 22Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA; 23Department of Medicine, McGill University Health Centre Research Institute, Montreal, Canada; 24Department of Medicine, University of California at San Francisco, San Francisco, CA, USACorrespondence: Ching-Long Lin 2406 Seamans Center for the Engineering Art and Science, Iowa City, IA, 52242, USATel +1 319 335 5673Email ching-long-lin@uiowa.eduPurpose: Quantitative 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 Methods: We 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.Results: COPD 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.Conclusion: qCT 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.Keywords: computed tomography, emphysema, functional small airway disease, longitudinal clustering

Patients with systemic sclerosis are at high risk of developing systemic sclerosis–associated interstitial lung disease. Symptoms and outcomes of systemic sclerosis–associated interstitial lung disease range from subclinical lung involvement to respiratory failure and death. Early and accurate diagnosis of systemic sclerosis–associated interstitial lung disease is therefore important to enable appropriate intervention. The most sensitive and specific way to diagnose systemic sclerosis–associated interstitial lung disease is by high-resolution computed tomography, and experts recommend that high-resolution computed tomography should be performed in all patients with systemic sclerosis at the time of initial diagnosis. In addition to being an important screening and diagnostic tool, high-resolution computed tomography can be used to evaluate disease extent in systemic sclerosis–associated interstitial lung disease and may be helpful in assessing prognosis in some patients. Currently, there is no consensus with regards to frequency and scanning intervals in patients at risk of interstitial lung disease development and/or progression. However, expert guidance does suggest that frequency of screening using high-resolution computed tomography should be guided by risk of developing interstitial lung disease. Most experienced clinicians would not repeat high-resolution computed tomography more than once a year or every other year for the first few years unless symptoms arose. Several computed tomography techniques have been developed in recent years that are suitable for regular monitoring, including low-radiation protocols, which, together with other technologies, such as lung ultrasound and magnetic resonance imaging, may further assist in the evaluation and monitoring of patients with systemic sclerosis–associated interstitial lung disease. A video abstract to accompany this article is available at: https://www.globalmedcomms.com/respiratory/Khanna/HRCTinSScILD

After the results of two large, randomized trials, the globalimplementation of lung cancer screening is of utmostimportance. However, coronavirus disease 2019 infectionsoccurring at heightened levels during the current globalpandemic and also other respiratory infections can influence scan interpretation and screeningsafetyanduptake.Severalrespiratoryinfectionscanleadtolesionsthatmimicmalignantnodules and other imaging changes suggestingmalignancy, leading to an increased level of follow-up procedures or even invasive diagnostic procedures. In periodsof increased rates of respiratory infections from severeacute respiratory syndrome coronavirus 2 and others, thereis also a risk oftransmission of these infections to the healthcare providers, the screenees, and patients. This becameevident with the severe acute respiratory syndrome coronavirus 2 pandemic that led to atemporary global stoppageof lung cancer and other cancer screening programs. Dataon the optimal management of these situations arenotavailable. The pandemic is still ongoing and further periodsof increased respiratory infections will come, in whichpractical guidance would be helpful. The aims of this reportwere: (1) to summarize the data available for possible falsepositive results owing to respiratory infections; (2) toevaluate the safety concerns for screening during times ofincreased respiratoryinfections, especially during aregional outbreak or an epidemic or pandemic event; (3) toprovide guidance on these situations; and (4) to stimulateresearch and discussions about these scenarios.

While deep learning has shown promise in improving the automated diagnosis of disease based on chest X-rays, deep networks may exhibit undesirable behavior related to shortcuts. This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcome of interest. For instance, clinical protocols might lead to a dataset in which patients with pacemakers are disproportionately likely to have congestive heart failure. This skew can lead to models that take shortcuts by heavily relying on the biased attribute. We explore this problem across a number of attributes in the context of diagnosing the cause of acute hypoxemic respiratory failure. Applied to chest X-rays, we show that i) deep nets can accurately identify many patient attributes including sex (AUROC = 0.96) and age (AUROC >= 0.90), ii) they tend to exploit correlations between such attributes and the outcome label when learning to predict a diagnosis, leading to poor performance when such correlations do not hold in the test population (e.g., everyone in the test set is male), and iii) a simple transfer learning approach is surprisingly effective at preventing the shortcut and promoting good generalization performance. On the task of diagnosing congestive heart failure based on a set of chest X-rays skewed towards older patients (age >= 63), the proposed approach improves generalization over standard training from 0.66 (95% CI: 0.54-0.77) to 0.84 (95% CI: 0.73-0.92) AUROC. While simple, the proposed approach has the potential to improve the performance of models across populations by encouraging reliance on clinically relevant manifestations of disease, i.e., those that a clinician would use to make a diagnosis., 32 pages, 9 figures, 12 tables, MLHC 2020

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.

BackgroundChronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality. Present-day diagnostic criteria are largely based solely on spirometric criteria. Accumulating evidence has identified a substantial number of individuals without spirometric evidence of COPD who suffer from respiratory symptoms and/or increased morbidity and mortality. There is a clear need for an expanded definition of COPD that is linked to physiologic, structural (computed tomography [CT]) and clinical evidence of disease. Using data from the COPD Genetic Epidemiology study (COPDGene®), we hypothesized that an integrated approach that includes environmental exposure, clinical symptoms, chest CT imaging and spirometry better defines disease and captures the likelihood of progression of respiratory obstruction and mortality.MethodsFour key disease characteristics - environmental exposure (cigarette smoking), clinical symptoms (dyspnea and/or chronic bronchitis), chest CT imaging abnormalities (emphysema, gas trapping and/or airway wall thickening), and abnormal spirometry - were evaluated in a group of 8784 current and former smokers who were participants in COPDGene® Phase 1. Using these 4 disease characteristics, 8 categories of participants were identified and evaluated for odds of spirometric disease progression (FEV1 > 350 ml loss over 5 years), and the hazard ratio for all-cause mortality was examined.ResultsUsing smokers without symptoms, CT imaging abnormalities or airflow obstruction as the reference population, individuals were classified as Possible COPD, Probable COPD and Definite COPD. Current Global initiative for obstructive Lung Disease (GOLD) criteria would diagnose 4062 (46%) of the 8784 study participants with COPD. The proposed COPDGene® 2019 diagnostic criteria would add an additional 3144 participants. Under the new criteria, 82% of the 8784 study participants would be diagnosed with Possible, Probable or Definite COPD. These COPD groups showed increased risk of disease progression and mortality. Mortality increased in patients as the number of their COPD characteristics increased, with a maximum hazard ratio for all cause-mortality of 5.18 (95% confidence interval [CI]: 4.15-6.48) in those with all 4 disease characteristics.ConclusionsA substantial portion of smokers with respiratory symptoms and imaging abnormalities do not manifest spirometric obstruction as defined by population normals. These individuals are at significant risk of death and spirometric disease progression. We propose to redefine the diagnosis of COPD through an integrated approach using environmental exposure, clinical symptoms, CT imaging and spirometric criteria. These expanded criteria offer the potential to stimulate both current and future interventions that could slow or halt disease progression in patients before disability or irreversible lung structural changes develop.

Figure S2. (a) Internal properties in different clustering methods to find the best clustering approaches as well as the optimal number of clusters; (b) Bootstrapping stability analysis between K-means and hierarchical clustering with 4 or 5 numbers of clusters. (DOCX 58 kb)

Figure S3. Predicting imaged-based cluster using only 5 important variables. Variables are βtissueRV (Total), Jacobian (Total), βtissueTLC (Total), Dh* (RMB) and ADI (Total) with 81% accuracy. (DOCX 59 kb)

Table S1. The confusion matrices to assess the possible overlap between current and former smoker clusters. Values are presented as the number of subjects (%). (DOCX 15 kb)

Figure S1. A scree plot: eigenvalues (magnitude of variances) according to the number of principal components for determining the optimal number of components. (DOCX 65 kb)

Table S1. Standardized loadings of seven principal components based upon correlation matrix. Table S2. Major structural and functional imaging-based variables in four imaging-based clusters for 45 current smokers from longitudinal study. Figure S1. Clustering analysis, a: Internal property in different clustering methods; b: Clustering stability analysis between K-means and Hierarchical clustering with different number of clusters. Figure S2. Cluster analysis in training set (a) and validation set (b) with four clusters. Figure S3. A scree plot for determining the optimal number of principal components for longitudinal study. (DOCX 276 kb)

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10−10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.

Multidetector row computed tomography (MDCT) is increasingly taking a central role in identifying subphenotypes within chronic obstructive pulmonary disease (COPD), asthma, and other lung-related disease populations, allowing for the quantification of the amount and distribution of altered parenchyma along with the characterization of airway and vascular anatomy. The embedding of quantitative CT (QCT) into a multicenter trial with a variety of scanner makes and models along with the variety of pressures within a clinical radiology setting has proven challenging, especially in the context of a longitudinal study. SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), sponsored by the National Institutes of Health, has established a QCT lung assessment system (QCT-LAS), which includes scanner-specific imaging protocols for lung assessment at total lung capacity and residual volume. Also included are monthly scanning of a standardized test object and web-based tools for subject registration, protocol assignment, and data transmission coupled with automated image interrogation to assure protocol adherence. The SPIROMICS QCT-LAS has been adopted and contributed to by a growing number of other multicenter studies in which imaging is embedded. The key components of the SPIROMICS QCT-LAS along with evidence of implementation success are described herein. While imaging technologies continue to evolve, the required components of a QCT-LAS provide the framework for future studies, and the QCT results emanating from SPIROMICS and the growing number of other studies using the SPIROMICS QCT-LAS will provide a shared resource of image-derived pulmonary metrics.

RationaleThe small conducting airways are the major site of airflow obstruction in chronic obstructive pulmonary disease and may precede emphysema development.ObjectivesWe hypothesized a novel computed tomography (CT) biomarker of small airway disease predicts FEV1 decline.MethodsWe 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 resultsMean (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

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.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.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.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.

The American College of Radiology, with more than 30,000 members, is the principal organization of radiologists, radiation oncologists,and clinical medical physicists in the United States. The College is a nonprofit professional society whose primary purposes are to advance thescience of radiology, improve radiologic services to the patient, study the socioeconomic aspects of the practice of radiology, and encouragecontinuing education for radiologists, radiation oncologists, medical physicists, and persons practicing in allied professional fields.The American College of Radiology will periodically define new practice parameters and technical standards for radiologic practice to helpadvance the science of radiology and to improve the quality of service to patients throughout the United States. Existing practice parameters andtechnical standards will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated.Each practice parameter and technical standard, representing a policy statement by the College, has undergone a thorough consensusprocess in which it has been subjected to extensive review and approval. The practice parameters and technical standards recognize that the safeand effective use of diagnostic and therapeutic radiology requires specific training, skills, and techniques, as described in each document.Reproduction or modification of the published practice parameter and technical standard by those entities not providing these services is notauthorized.?

Cigarette smoking is a key factor in the development of numerous pulmonary diseases. An international group of clinicians, radiologists and pathologists evaluated patients with previously identified idiopathic interstitial pneumonia (IIP) to determine unique features of cigarette smoking. Phase 1 (derivation group) identified smoking-related features in patients with a history of smoking (n=41). Phase 2 (validation group) determined if these features correctly predicted the smoking status of IIP patients (n=100) to participants blinded to smoking history. Finally, the investigators sought to determine if a new smoking-related interstitial lung disease phenotype could be defined. Phase 1 suggested that preserved forced vital capacity with disproportionately reduced diffusing capacity of the lung for carbon monoxide, and various radiographic and histopathological findings were smoking-related features. In phase 2, the kappa coefficient among clinicians was 0.16 (95% CI 0.11-0.21), among the pathologists 0.36 (95% CI 0.32-0.40) and among the radiologists 0.43 (95% CI 0.35-0.52) for smoking-related features. Eight of the 100 cases were felt to represent a potential smoking-related interstitial lung disease. Smoking-related features of interstitial lung disease were identified in a minority of smokers and were not specific for smoking. This study is limited by its retrospective design, the potential for recall bias in smoking history and lack of information on second-hand smoke exposure. Further research is needed to understand the relationship between smoking and interstitial lung disease.

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.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.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.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.

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.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.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.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.

The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("noduleor =3 mm," "nodule3 mm," and "non-noduleor =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "noduleor =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings.The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

The composite physiologic index (CPI) was derived to represent the extent of fibrosis on high-resolution computed tomography (HRCT), adjusting for emphysema in patients with idiopathic pulmonary fibrosis (IPF). We hypothesised that longitudinal change in CPI would better predict mortality than forced expiratory volume in 1 s (FEV(1)), forced vital capacity (FVC) or diffusing capacity of the lung for carbon monoxide (D(L,CO)) in all patients with IPF, and especially in those with combined pulmonary fibrosis and emphysema (CPFE). Cox proportional hazard models were performed on pulmonary function data from IPF patients at baseline (n = 321), 6 months (n = 211) and 12 months (n = 144). Presence of CPFE was determined by HRCT. A five-point increase in CPI over 12 months predicted subsequent mortality (HR 2.1, p = 0.004). At 12 months, a 10% relative decline in FVC, a 15% relative decline in D(L,CO) or an absolute increase in CPI of five points all discriminated median survival by 2.1 to 2.2 yrs versus patients with lesser change. Half our cohort had CPFE. In patients with moderate/severe emphysema, only a 10% decline in FEV(1) predicted mortality (HR 3.7, p = 0.046). In IPF, a five-point increase in CPI over 12 months predicts mortality similarly to relative declines of 10% in FVC or 15% in D(L,CO). For CPFE patients, change in FEV(1) was the best predictor of mortality.

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.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.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).CAD has the potential to increase radiologists' accuracy in assessing the likelihood of malignancy of lung nodules on CT imaging.

The authors are developing a computer-aided detection system for pulmonary emboli (PE) in computed tomographic pulmonary angiography (CTPA) scans. The pulmonary vessel tree is extracted using a 3D expectation-maximization segmentation method based on the analysis of eigen-values of Hessian matrices at multiple scales. A parallel multiprescreening method is applied to the segmented vessels to identify volume of interests (VOIs) that contained suspicious PE. A linear discriminant analysis (LDA) classifier with feature selection is designed to reduce false positives (FPs). Features that characterize the contrast, gray level, and size of PE are extracted as input predictor variables to the LDA classifier. With the IRB approval, 59 CTPA PE cases were collected retrospectively from the patient files (UM cases). With access permission, 69 CTPA PE cases were randomly selected from the data set of the prospective investigation of pulmonary embolism diagnosis (PIOPED) II clinical trial. Extensive lung parenchymal or pleural diseases were present in 22/59 UM and 26/69 PIOPED cases. Experienced thoracic radiologists manually marked 595 and 800 PE as the reference standards in the UM and PIOPED data sets, respectively. PE occlusion of arteries ranged from 5% to 100%, with PE located from the main pulmonary artery to the subsegmental artery levels. Of the 595 PE identified in the UM cases, 245 and 350 PE were located in the subsegmental arteries and the more proximal arteries, respectively. The detection performance was assessed by free response ROC (FROC) analysis. The FROC analysis indicated that the PE detection system could achieve an overall sensitivity of 80% at 18.9 FPs/case for the PIOPED cases when the LDA classifier was trained with the UM cases. The test sensitivity with the UM cases was 80% at 22.6 FPs/cases when the LDA classifier was trained with the PIOPED cases. The detection performance depended on the arterial level where the PE was located and on the percentage of occlusion. The sensitivity was lower for PE in the subsegmental arteries than in more proximal arteries and was lower for PE with less than 20% occlusion. The results indicate that the PE detection system achieves high sensitivity for PE detection on independent CTPA scans for both the PIOPED and UM data sets and demonstrate the potential that the automated PE detection approach can be generalized to unknown cases.

The purpose of this work is to develop a computer-aided diagnosis (CAD) system to differentiate malignant and benign lung nodules on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a 3D active contour method. The initial contour was obtained as the boundary of a binary object generated by k-means clustering within the VOI and smoothed by morphological opening. A data set of 256 lung nodules (124 malignant and 132 benign) from 152 patients was used in this study. In addition to morphological and texture features, the authors designed new nodule surface features to characterize the lung nodule surface smoothness and shape irregularity. The effects of two demographic features, age and gender, as adjunct to the image features were also investigated. A linear discriminant analysis (LDA) classifier built with features from stepwise feature selection was trained using simplex optimization to select the most effective features. A two-loop leave-one-out resampling scheme was developed to reduce the optimistic bias in estimating the test performance of the CAD system. The area under the receiver operating characteristic curve, A(z), for the test cases improved significantly (p0.05) from 0.821 +/- 0.026 to 0.857 +/- 0.023 when the newly developed image features were included with the original morphological and texture features. A similar experiment performed on the data set restricted to primary cancers and benign nodules, excluding the metastatic cancers, also resulted in an improved test A(z), though the improvement did not reach statistical significance (p = 0.07). The two demographic features did not significantly affect the performance of the CAD system (p0.05) when they were added to the feature space containing the morphological, texture, and new gradient field and radius features. To investigate if a support vector machine (SVM) classifier can achieve improved performance over the LDA classifier, we compared the performance of the LDA and SVMs with various kernels and parameters. Principal component analysis was used to reduce the dimensionality of the feature space for both the LDA and the SVM classifiers. When the number of selected principal components was varied, the highest test A(z) among the SVMs of various kernels and parameters was slightly higher than that of the LDA in one-loop leave-one-case-out resampling. However, no SVM with fixed architecture consistently performed better than the LDA in the range of principal components selected. This study demonstrated that the authors' proposed segmentation and feature extraction techniques are promising for classifying lung nodules on CT images.

Rationale and ObjectivesTo 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, since the latters are always qualified with respect to a given size range of nodules.Materials and MethodsThis study used 265 whole-lung CT scans documented by the Lung Image Database Consortium 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 uni-dimensional and two bi-dimensional 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 inter-radiologist variation, while those with at least one marking were used to examine the difference between the metrics.ResultsThe 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 inter-observer variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges [0.49, 1.25], [0.67, 2.55], [0.78, 2.11], and [0.96, 2.69] for the three-dimensional, the uni-dimensional, and the two bi-dimensional size metrics respectively (in mm). Also a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on uni-dimensional, and the two bi-dimensional size measurements of 10mm were 7.32, 7.72, and 6.29 mm respectively.ConclusionsThe 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.

Rationale and ObjectivesComputer-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 MethodsOne 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 ≥ 3mm,” “nodule < 3mm,” or “non-nodule ≥ 3mm.” 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 marks, 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 (1) identification of potential errors introduced during the complete image annotation process (such as two marks on what appears to be a single lesion or an incomplete nodule contour) and (2) 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.ResultsA 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.ConclusionThe 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.

Integral to the mission of the National Institutes of Health-sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary.The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologists' spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects.Across the combination of all nodules, volume and p-map model parameters were found to be significant at P.05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively.Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume.

ObjectivesThe purpose of the study was to determine the effectiveness of a collaborative educational, continuous quality improvement (CQI) initiative to increase appropriate use of coronary computed tomography angiography (CCTA).BackgroundPotential overuse of CCTA has prompted multisociety appropriate use criteria (AUC) publications.MethodsThis prospective, observational study was conducted with pre-intervention (July 2007 to June 2008), intervention (July 2008 to June 2010), and follow-up (July 2010 to December 2010) periods during which patients were enrolled in the Advanced Cardiovascular Imaging Consortium (ACIC) at 47 Michigan hospitals. Continuous education was provided to referring physicians. The possibility of losing third-party payer coverage in the absence of a measurable change in AUC was emphasized. AUC was compared between the 3 periods.ResultsThe study group included 25,387 patients. Compared with the pre-intervention period, there was a 23.4% increase in appropriate (61.3% to 80%, p < 0.0001), 60.3% decrease in inappropriate (14.6% to 5.8%, p < 0.0001), 40.8% decrease in uncertain (10.3% to 6.1%, p < 0.0001), and 41.7% decrease in unclassifiable (13.9% to 8.1%, p < 0.0001) scans during follow-up. Between pre-intervention and follow-up, change in CCTA referrals by provider specialty were cardiology (appropriate: 60.4% to 79.5%; inappropriate: 13% to 5.2%; p < 0.0001), internal medicine/family practice (appropriate: 51.1% to 70.4%; inappropriate: 20.2% to 12.5%; p < 0.0001), emergency medicine (appropriate: 83.6% to 91.6%; inappropriate: 9.1% to 0.6%; p < 0.0001), and other (appropriate: 61.1% to 83.2%; inappropriate: 18.6% to 5.9%; p < 0.0001).ConclusionsApplication of a systematic CQI and emphasis on possible loss of coverage were associated with a significant improvement in the proportion of CCTA examinations meeting AUC across referring physician specialties.

Table S1. Lack of association of GDF-15 levels or CAC score with PA:A ratio*. Table S2. Bivariate and Multivariate Associations with GDF-15 among COPDGene participants with COPD (n = 694). Table S3. Multivariate models of association of GDF-15 with CAC score among different subgroups of COPDGene participants with COPD. (DOCX 33 kb)

Table S1 . Lack of association of GDF-15 levels or CAC score with PA:A ratio*. Table S2. Bivariate and Multivariate Associations with GDF-15 among COPDGene participants with COPD (n = 694). Table S3. Multivariate models of association of GDF-15 with CAC score among different subgroups of COPDGene participants with COPD. (DOCX 33 kb)