AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis.

Background: Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths. Methods: We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score. Results: During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342). Conclusion: In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.

INTRODUCTION: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF). METHODS: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years. RESULTS: Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p â€‹< â€‹0.0001), and comparable to clinical risk factors (0.85, p â€‹= â€‹0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p â€‹< â€‹0.0001). CONCLUSION: AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CACTM) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis.

BACKGROUND: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. METHODS: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CACTM) to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 â€‹s per CAC scan. We used the 5-year outcomes data for incident atrial fibrillation (AF) and assessed discrimination using the time-dependent area under the curve (AUC) of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP. The mean follow-up time to an AF event was 2.9 â€‹± â€‹1.4 years. RESULTS: At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p â€‹< â€‹0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p â€‹< â€‹0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p â€‹< â€‹0.01). CONCLUSION: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.

Visual scoring of osteoporosis on low-dose CT in lung cancer screening population.

OBJECTIVES: The risk factors for lung cancer screening eligibility, age as well as smoking history, are also present for osteoporosis. This study aims to develop a visual scoring system to identify osteoporosis that can be applied to low-dose CT scans obtained for lung cancer screening. MATERIALS AND METHODS: We retrospectively reviewed 1000 prospectively enrolled participants in the lung cancer screening program at the Mount Sinai Hospital. Optimal window width and level settings for the visual assessment were chosen based on a previously described approach. Visual scoring of osteoporosis and automated measurement using dedicated software were compared. Inter-reader agreement was conducted using six readers with different levels of experience who independently visually assessed 30 CT scans. RESULTS: Based on previously validated formulas for choosing window and level settings, we chose osteoporosis settings of Width = 230 and Level = 80. Of the 1000 participants, automated measurement was successfully performed on 774 (77.4 %). Among these, 138 (17.8 %) had osteoporosis. There was a significant correlation between the automated measurement and the visual score categories for osteoporosis (Kendall's Tau = -0.64, p < 0.0001; Spearman's rho = -0.77, p < 0.0001). We also found substantial to excellent inter-reader agreement on the osteoporosis classification among the 6 radiologists (Fleiss κ = 0.91). CONCLUSIONS: Our study shows that a simple approach of applying specific window width and level settings to already reconstructed sagittal images obtained in the context of low-dose CT screening for lung cancer is highly feasible and useful in identifying osteoporosis.

ADME Gene-Related Pharmacogenomic Labeling of FDA-Approved Drugs: Comparison with Clinical Pharmacogenetics Implementation Consortium (CPIC) Evidence Levels.

Pharmacogenomics (PGx) can facilitate the transition to patient-specific drug regimens and thus improve their efficacy and reduce toxicity. The aim of this study was to evaluate the overlap of PGx classification for drug absorption, distribution, metabolism, and elimination (ADME)-related genes in the U.S. Food and Drug Administration (FDA) PGx labeling and in the Clinical Pharmacogenetics Implementation Consortium (CPIC) database. FDA-approved drugs and PGx labeling for ADME genes were identified in the CPIC database. Drugs were filtered by their association with ADME (pharmacokinetics)-related genes, PGx FDA labeling class, and CPIC evidence level. FDA PGx labeling was classified as either actionable, informative, testing recommended, or testing required, and varying CPIC evidence levels as either A, B, C, or D. From a total of 442 ADME and non-ADME gene-drug pairs in the CPIC database, 273, 55, and 48 pairs were excluded for lack of FDA labeling, mixed CPIC evidence level provisional classification, and non-ADME gene-drug pairs, respectively. The 66 ADME gene-drug pairs were classified into the following categories: 10 (15%) informative, 49 (74%) actionable, 6 (9%) testing recommended, and 1 (2%) testing required. CYP2D6 was the most prevalent gene among the FDA PGx labeling. From the ADME gene-drug pairs with both FDA and CPIC PGx classification, the majority of the drugs were for depression, cancer, and pain medications. The ADME gene-drug pairs with FDA PGx labeling considerably overlap with CPIC classification; however, a large number of ADME gene-drug pairs have only CPIC evidence levels but not FDA classification. PGx actionable labeling was the most common classification, with CYP2D6 as the most prevalent ADME gene in the FDA PGx labeling. Health professionals can impact therapeutic outcomes via pharmacogenetic interventions by analyzing and reconciling the FDA labels and CPIC database.

Cluster Analysis of World Trade Center Related Lower Airway Diseases.

ABSTRACT: Introduction: Cluster analysis can classify without a priori assumptions the heterogeneous chronic lower airway diseases found in former workers at the World Trade Center (WTC) disaster site. Methods: We selected the first available chest computed tomography scan with quantitative computed tomography measurements on 311 former WTC workers with complete clinical, and spirometric data from their closest surveillance visit. We performed a nonhierarchical iterative algorithm K-prototype cluster analysis, using gap measure. Results: A five-cluster solution was most satisfactory. Cluster 5 had the healthiest individuals. In cluster 4, smoking was most prevalent and intense but there was scant evidence of respiratory disease. Cluster 3 had symptomatic subjects with reduced forced vital capacity impairment (low FVC). Clusters 1 and 2 had less dyspneic subjects, but more functional and quantitative computed tomography evidence of chronic obstructive pulmonary disease (COPD) in cluster 1, or low FVC in cluster 2. Clusters 1 and 4 had the highest proportion of rapid first-second forced expiratory volume decliners. Conclusions: Cluster analysis confirms low FVC and COPD/pre-COPD as distinctive chronic lower airway disease phenotypes on long-term surveillance of the WTC workers.

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis.

Background: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. Methods: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC), taking on average 21 seconds per CAC scan, to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). We used the 5-year outcomes data for incident atrial fibrillation (AF) and compared the time-dependent AUC of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP (BNP). The mean follow-up time to an AF event was 2.9±1.4 years. Results: At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF or BNP at year 1 (0.836, 0.742, 0.742), year 2 (0.842, 0.807,0.772), and year 3 (0.811, 0.785, 0.745) (p<0.02), but similar for year 4 (0.785, 0.769, 0.725) and year 5 (0.781, 0.767, 0.734) respectively (p>0.05). AI-CAC LA volume significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CAC score (0.74, 0.49, 0.53, 0.39, 0.44), CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and BNP (0.68, 0.44, 0.42, 0.30, 0.37) respectively (p<0.01). Conclusion: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and BNP.

Opportunistic AI-enabled automated bone mineral density measurements in lung cancer screening and coronary calcium scoring CT scans are equivalent.

Rationale and objectives: We previously reported a novel manual method for measuring bone mineral density (BMD) in coronary artery calcium (CAC) scans and validated our method against Dual X-Ray Absorptiometry (DEXA). Furthermore, we have developed and validated an artificial intelligence (AI) based automated BMD (AutoBMD) measurement as an opportunistic add-on to CAC scans that recently received FDA approval. In this report, we present evidence of equivalency between AutoBMD measurements in cardiac vs lung CT scans. Materials and methods: AI models were trained using 132 cases with 7649 (3 mm) slices for CAC, and 37 cases with 21918 (0.5 mm) slices for lung scans. To validate AutoBMD against manual measurements, we used 6776 cases of BMD measured manually on CAC scans in the Multi-Ethnic Study of Atherosclerosis (MESA). We then used 165 additional cases from Harbor UCLA Lundquist Institute to compare AutoBMD in patients who underwent both cardiac and lung scans on the same day. Results: Mean±SD for age was 69 ± 9.4 years with 52.4% male. AutoBMD in lung and cardiac scans, and manual BMD in cardiac scans were 153.7 ± 43.9, 155.1 ± 44.4, and 163.6 ± 45.3 g/cm3, respectively (p = 0.09). Bland-Altman agreement analysis between AutoBMD lung and cardiac scans resulted in 1.37 g/cm3 mean differences. Pearson correlation coefficient between lung and cardiac AutoBMD was R2 = 0.95 (p < 0.0001). Conclusion: Opportunistic BMD measurement using AutoBMD in CAC and lung cancer screening scans is promising and yields similar results. No extra radiation plus the high prevalence of asymptomatic osteoporosis makes AutoBMD an ideal screening tool for osteopenia and osteoporosis in CT scans done for other reasons.

Dynamic Differential Image Circle Diameter Measurement Precision Assessment: Application to Burning Droplets.

Dynamic measurement precision assessment has been achieved for a differential circle measurement application. Differential circle diameter measurement, in image analysis, typically requires fitting a circle model that optimizes for image distortions, defects or occlusions. The differential task occurs when precise measurements of diameter change are required given object size variation with time. An automated system was designed to provide diameter measurements and associated measurement precision of images of a fuel droplet undergoing combustion in zero gravity for the FLEX-2 dataset. An image gradient-based, least-squares boundary point fitting method to a circle or ellipse model is used for diameter measurement. The presence of soot aggregates poses significant challenges for diameter measurements when it occludes part of the droplet boundary. The precision of the diameter measurements depends upon the image quality. Using synthetic image simulations that model the soot behavior, we developed a model based on image quality measures that assesses the measurement precision for each individual diameter measurement. Thus, diameter measurements with precision assessments were made available for follow-up scientific analysis. The algorithm's success rate for measurable runs was 98%. In cases of limited occlusion, a measurement precision of ±0.2 pixels for the FLEX-2 dataset was achieved.

Evaluation of pharmacogenomic evidence for drugs related to ADME genes in CPIC database.

OBJECTIVES: Clinical Pharmacogenetics Implementation Consortium (CPIC) is a platform that advances the pharmacogenomics (PGx) practice by developing evidence-based guidelines. The purpose of this study was to analyze the CPIC database for ADME related genes and their corresponding drugs, and evidence level for drug-gene pairs; and to determine the presence of these drug-gene pairs in the highest mortality diseases in the United States. METHODS: CPIC database was evaluated for drug-gene pairs related to absorption, distribution, metabolism, and excretion (ADME) properties. National Vital Statistics from Centers for Disease Control and Prevention was used to identify the diseases with the highest mortality. CPIC levels are assigned to different drug-gene pairs based on varying levels of evidence as either A, B, C, or D. All drug-gene pairs assigned with A/B, B/C, or C/D mixed levels were excluded from this study. A stepwise exclusion process was followed to determine the prevalence of various ADME drug-gene pairs among phase I/II enzymes or transporters and stratify the drug-gene pairs relevant to different disease conditions most commonly responsible for death in the United States. RESULTS: From a total of 442 drug-gene pairs in the CPIC database, after exclusion of 86 drug-gene pairs with levels A/B, B/C, or C/D, and 211 non-ADME related genes, 145 ADME related drug-gene pairs resulted. From the 145 ADME related drug-genes pairs, the following were the distribution of levels: Level A: 43 (30%), Level B: 22 (15%), Level C: 59 (41%), Level D: 21 (14%). The most prevalent ADME gene with CPIC level A classification was cytochrome P450 2C9 (CYP2C9) (26%) and overall, the most prevalent ADME gene in the CPIC database was CYP2D6 (30%). The most prevalent diseases related to the CPIC evidence related drugs were cancer and depression. CONCLUSIONS: We found that there is an abundance of ADME related genes in the CPIC database, including in the high mortality disease states of cancer and depression. There is a differential level of pharmacogenomic evidence in drug-gene pairs enlisted in CPIC where levels A and D having the greatest number of drug-gene pairs. CYP2D6 was the most common ADME gene with CPIC evidence for drug-gene pairs. Pharmacogenomic applications of CPIC evidence can be leveraged to individualize patient therapy and lower adverse effect events.

ADME and Pharmacokinetic Properties of Remdesivir: Its Drug Interaction Potential.

On 11 March 2020, the World Health Organization (WHO) classified the Coronavirus Disease 2019 (COVID-19) as a global pandemic, which tested healthcare systems, administrations, and treatment ingenuity across the world. COVID-19 is caused by the novel beta coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Since the inception of the pandemic, treatment options have been either limited or ineffective. Remdesivir, a drug originally designed to be used for Ebola virus, has antiviral activity against SARS-CoV-2 and has been included in the COVID-19 treatment regimens. Remdesivir is an adenosine nucleotide analog prodrug that is metabolically activated to a nucleoside triphosphate metabolite (GS-443902). The active nucleoside triphosphate metabolite is incorporated into the SARS-CoV-2 RNA viral chains, preventing its replication. The lack of reported drug development and characterization studies with remdesivir in public domain has created a void where information on the absorption, distribution, metabolism, elimination (ADME) properties, pharmacokinetics (PK), or drug-drug interaction (DDI) is limited. By understanding these properties, clinicians can prevent subtherapeutic and supratherapeutic levels of remdesivir and thus avoid further complications in COVID-19 patients. Remdesivir is metabolized by both cytochrome P450 (CYP) and non-CYP enzymes such as carboxylesterases. In this narrative review, we have evaluated the currently available ADME, PK, and DDI information about remdesivir and have discussed the potential of DDIs between remdesivir and different COVID-19 drug regimens and agents used for comorbidities. Considering the nascent status of remdesivir in the therapeutic domain, extensive future work is needed to formulate safer COVID-19 treatment guidelines involving this medication.

Simulation of Remdesivir Pharmacokinetics and Its Drug Interactions.

PURPOSE: Remdesivir, a drug originally developed against Ebola virus, is currently recommended for patients hospitalized with coronavirus disease of 2019 (COVID-19). In spite of United States Food and Drug Administration's recent assent of remdesivir as the only approved agent for COVID-19, there is limited information available about the physicochemical, metabolism, transport, pharmacokinetic (PK), and drug-drug interaction (DDI) properties of this drug. The objective of this in silico simulation work was to simulate the biopharmaceutical and DDI behavior of remdesivir and characterize remdesivir PK properties in special populations which are highly affected by COVID-19. METHODS: The Spatial Data File format structures of remdesivir prodrug (GS-5734) and nucleoside core (GS-441524) were obtained from the PubChem database to upload into the GastroPlus software 9.8 version (Simulations Plus Inc., USA). The Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) Predictor and PKPlus modules of GastroPlus were used to simulate physicochemical and PK properties, respectively, in healthy and predisposed patients. Physiologically based pharmacokinetic (PBPK) modeling of GastroPlus was used to simulate different patient populations based on age, weight, liver function, and renal function status. Subsequently, these data were used in the Drug-Drug Interaction module to simulate drug interaction potential of remdesivir with other COVID-19 drug regimens and with agents used for comorbidities. RESULTS: Remdesivir nucleoside core (GS-441524) is more hydrophilic than the inactive prodrug (GS-5734) with nucleoside core demonstrating better water solubility. GS-5734, but not GS-441524, is predicted to be metabolized by CYP3A4. Remdesivir is bioavailable and its clearance is achieved through hepatic and renal routes. Differential effects of renal function, liver function, weight, or age were observed on the PK profile of remdesivir. DDI simulation study of remdesivir with perpetrator drugs for comorbidities indicate that carbamazepine, phenytoin, amiodarone, voriconazole, diltiazem, and verapamil have the potential for strong interactions with victim remdesivir, whereas agents used for COVID-19 treatment such as chloroquine and ritonavir can cause weak and strong interactions, respectively, with remdesivir. CONCLUSIONS: GS-5734 (inactive prodrug) appears to be a superior remdesivir derivative due to its hepatic stability, optimum hydrophilic/lipophilic balance, and disposition properties. Remdesivir disposition can potentially be affected by different physiological and pathological conditions, and by drug interactions from COVID-19 drug regimens and agents used for comorbidities.

Neurotoxicities in the treatment of HIV between dolutegravir, rilpivirine and dolutegravir/rilpivirine: a meta-analysis.

OBJECTIVE: To assess the risk of neuropsychiatric adverse effects (ie, depression, anxiety, insomnia, dizziness, suicidal behaviour) among patients treated with rilpivirine, dolutegravir and dolutegravir/rilpivirine. DESIGN: This is a systematic review and meta-analysis of randomised controlled trials. Quality of evidence was assessed using Jadad scoring system. DATA SOURCES: Three electronic databases were searched for available publications up to 1 May 2020. Searches included relevant studies, trial registers, conference proceeding abstracts and grey literature. INCLUSION CRITERIA: Randomised controlled trials with data focused on adult participants (ie, 18 years of age or older) receiving dolutegravir 50 mg, rilpivirine 25 mg or combination of dolutegravir 50 mg/rilpivirine 25 mg once daily. RESULTS: Twenty studies with a minimum duration of 48 weeks and average Jadad score of 4 were included (n=10 998). Primary objective demonstrated a relative risk (RR) synergistic effect on depressive symptoms for dolutegravir/rilpivirine (RR=2.82; 95% CI (1.12 to 7.10)) when compared with dolutegravir (RR=1.10; 95% CI (0.88 to 1.38)) and rilpivirine (RR=1.08; 95% CI (0.80 to 1.48)). Secondary objectives showed no difference between dolutegravir, rilpivirine and dolutegravir/rilpivirine to efavirenz. Additionally, excluding efavirenz studies, dolutegravir and dolutegravir/rilpivirine yielded increased depression (RR=1.34; 95% CI (1.04 to 1.74)). CONCLUSION: The combination of dolutegravir/rilpivirine appears to increase the risk of depressive symptoms. Despite the increase, the clinical significance is unknown and needs further study. Additionally, neurotoxicity risk appears similar between dolutegravir, rilpivirine and dolutegravir/rilpivirine antiretroviral therapy when compared with efavirenz-based antiretroviral therapy.

The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program.

We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed.

Association of quantitative CT lung density measurements and lung function decline in World Trade Center workers.

BACKGROUND: Occupational exposures at the WTC site after 11 September 2001 have been associated with presumably inflammatory chronic lower airway diseases. AIMS: In this study, we describe the trajectories of expiratory air flow decline, identify subgroups with adverse progression, and investigate the association of those trajectories with quantitative computed tomography (QCT) imaging measurement of increased and decreased lung density. METHODS: We examined the trajectories of expiratory air flow decline in a group of 1,321 former WTC workers and volunteers with at least three periodic spirometries, and using QCT-measured low (LAV%, -950 HU) and high (HAV%, from -600 to -250 HU) attenuation volume percent. We calculated the individual regression line slopes for first-second forced expiratory volume (FEV1 slope), identified subjects with rapidly declining ("accelerated decliners") and increasing ("improved"), and compared them to subjects with "intermediate" (0 to -66.5 mL/year) FEV1 slope. We then used multinomial logistic regression to model those three trajectories, and the two lung attenuation metrics. RESULTS: The mean longitudinal FEV1 slopes for the entire study population, and its intermediate, decliner, and improved subgroups were, respectively, -40.4, -34.3, -106.5, and 37.6 mL/year. In unadjusted and adjusted analyses, LAV% and HAV% were both associated with "accelerated decliner" status (ORadj , 95% CI 2.37, 1.41-3.97, and 1.77, 1.08-2.89, respectively), compared to the intermediate decline. CONCLUSIONS: Longitudinal FEV1 decline in this cohort, known to be associated with QCT proximal airway inflammation metric, is also associated with QCT indicators of increased and decreased lung density. The improved FEV1 trajectory did not seem to be associated with lung density metrics.

Simulation of Physicochemical and Pharmacokinetic Properties of Vitamin D3 and Its Natural Derivatives.

Vitamin D3 is an endogenous fat-soluble secosteroid, either biosynthesized in human skin or absorbed from diet and health supplements. Multiple hydroxylation reactions in several tissues including liver and small intestine produce different forms of vitamin D3. Low serum vitamin D levels is a global problem which may origin from differential absorption following supplementation. The objective of the present study was to estimate the physicochemical properties, metabolism, transport and pharmacokinetic behavior of vitamin D3 derivatives following oral ingestion. GastroPlus software, which is an in silico mechanistically-constructed simulation tool, was used to simulate the physicochemical and pharmacokinetic behavior for twelve vitamin D3 derivatives. The Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) Predictor and PKPlus modules were employed to derive the relevant parameters from the structural features of the compounds. The majority of the vitamin D3 derivatives are lipophilic (log P values >5) with poor water solubility which are reflected in the poor predicted bioavailability. The fraction absorbed values for the vitamin D3 derivatives were low except for calcitroic acid, 1,23S,25-trihydroxy-24-oxo-vitamin D3, and (23S,25R)-1,25-dihydroxyvitamin D3-26,23-lactone each being greater than 90% fraction absorbed. Cytochrome P450 3A4 (CYP3A4) is the primary hepatic enzyme along with P-glycoprotein involved in the disposition of the vitamin D derivatives. Lipophilicity and solubility appear to be strongly associated with the oral absorption of the vitamin D3 derivatives. Understanding the ADME properties of vitamin D3 derivatives with the knowledge of pharmacological potency could influence the identification of pharmacokinetically most acceptable vitamin D3 derivative for routine supplementation.

Quantitative CT Evidence of Airway Inflammation in WTC Workers and Volunteers with Low FVC Spirometric Pattern.

BACKGROUND: The most common abnormal spirometric pattern reported in WTC worker and volunteer cohorts has consistently been that of a nonobstructive reduced forced vital capacity (low FVC). Low FVC is associated with obesity, which is highly prevalent in these cohorts. We used quantitative CT (QCT) to investigate proximal and distal airway inflammation and emphysema in participants with stable low FVC pattern. METHODS: We selected study participants with at least two available longitudinal surveillance spirometries, and a chest CT with QCT measurements of proximal airway inflammation (wall area percent, WAP), end-expiratory air trapping, suggestive of distal airway obstruction (expiratory to inspiratory mean lung attenuation ratio, MLAEI), and emphysema (percentage of lung volume with attenuation below - 950 HU, LAV%). The comparison groups in multinomial logistic regression models were participants with consistently normal spirometries, and participants with stable fixed obstruction (COPD). RESULTS: Compared to normal spirometry participants, and after adjusting for age, sex, race/ethnicity, BMI, smoking, and early arrival at the WTC disaster site, low FVC participants had higher WAP (ORadj 1.24, 95% CI 1.06, 1.45, per 5% unit), suggestive of proximal airway inflammation, but did not differ in MLAEI, or LAV%. COPD participants did not differ in WAP with the low FVC ones and were more likely to have higher MLAEI or LAV% than the other two subgroups. DISCUSSION: WTC workers with spirometric low FVC have higher QCT-measured WAP compared to those with normal spirometries, but did not differ in distal airway and emphysema measurements, independently of obesity, smoking, and other covariates.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation.

PURPOSE: To improve outcomes for lung cancer through low-dose computed tomography (LDCT) early lung cancer detection. The International Association for the Study of Lung Cancer is developing the Early Lung Imaging Confederation (ELIC) to serve as an open-source, international, universally accessible environment to analyze large collections of quality-controlled LDCT images and associated biomedical data for research and routine screening care. METHODS: ELIC is an international confederation that allows access to efficiently analyze large numbers of high-quality computed tomography (CT) images with associated de-identified clinical information without moving primary imaging/clinical or imaging data from its local or regional site of origin. Rather, ELIC uses a cloud-based infrastructure to distribute analysis tools to the local site of the stored imaging and clinical data, thereby allowing for research and quality studies to proceed in a vendor-neutral, collaborative environment. ELIC's hub-and-spoke architecture will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal the data itself (ie, privacy protecting). Identifiable data remain under local control, so the resulting environment complies with national regulations and mitigates against privacy or data disclosure risk. RESULTS: The goal of pilot experiments is to connect image collections of LDCT scans that can be accurately analyzed in a fashion to support a global network using methodologies that can be readily scaled to accrued databases of sufficient size to develop and validate robust quantitative imaging tools. CONCLUSION: This initiative can rapidly accelerate improvements to the multidisciplinary management of early, curable lung cancer and other major thoracic diseases (eg, coronary artery disease and chronic obstructive pulmonary disease) visualized on a screening LDCT scan. The addition of a facile, quantitative CT scanner image quality conformance process is a unique step toward improving the reliability of clinical decision support with CT screening worldwide.

Automated measurement of liver attenuation to identify moderate-to-severe hepatic steatosis from chest CT scans.

PURPOSE: Develop and validate an automated method for measuring liver attenuation in non-contrast low-dose chest CT (LDCT) scans and compare it to the standard manual method for identifying moderate-to-severe hepatic steatosis (HS). METHOD: The automated method identifies a region below the right lung within the liver and uses statistical sampling techniques to exclude non-liver parenchyma. The method was used to assess moderate-to-severe HS on two IRB-approved cohorts: 1) 24 patients with liver disease examined between 1/2013-1/2017 with non-contrast chest CT and abdominal MRI scans obtained within three months of liver biopsy, and 2) 319 lung screening participants with baseline LDCT performed between 8/2011-1/2017. Agreement between the manual and automated CT methods, the manual MRI method, and pathology for determining moderate-to-severe HS was assessed using Cohen's Kappa by applying a 40 HU threshold to the CT method and 17.4% fat fraction to MRI. Agreement between the manual and automated CT methods was assessed using the intraclass correlation coefficient (ICC). Variability was assessed using Bland-Altman limits of agreement (LoA). RESULTS: In the first cohort, the manual and automated CT methods had almost perfect agreement (ICC = 0.97, κ = 1.00) with LoA of -7.6 to 4.7 HU. Both manual and automated CT methods had almost perfect agreement with MRI (κ = 0.90) and substantial agreement with pathology (κ = 0.77). In the second cohort, the manual and automated CT methods had almost perfect agreement (ICC = 0.94, κ = 0.87). LoA were -10.6 to 5.2 HU. CONCLUSION: Automated measurements of liver attenuation from LDCT scans can be used to identify moderate-to-severe HS on LDCT.