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In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. Here, we evaluated the CAD in a retrospective cohort of a routine clinical population. In total, a number of 337 scans of 314 different subjects with reported nodules of 3-30 mm in size were included into the evaluation. Two independent thoracic radiologists alternately reviewed scans with or without CAD assistance to detect, classify, segment, and register pulmonary nodules. A third, more experienced, radiologist served as an adjudicator. In addition, the cohort was analyzed by the CAD alone. The study cohort was divided into five different groups: 1) 178 CT studies without reported pulmonary nodules, 2) 95 studies with 1-10 pulmonary nodules, 23 studies from the same patients with 3) baseline and 4) follow-up studies, and 5) 18 CT studies with subsolid nodules. A reference standard for nodules was based on majority consensus with the third thoracic radiologist as required. Sensitivity, false positive (FP) rate and Dice inter-reader coefficient were calculated. After analysis of 470 pulmonary nodules, the sensitivity readings for radiologists without CAD and radiologist with CAD, were 71.9% (95% CI: 66.0%, 77.0%) and 80.3% (95% CI: 75.2%, 85.0%) (p < 0.01), with average FP rate of 0.11 and 0.16 per CT scan, respectively. Accuracy and kappa of CAD for classifying solid vs sub-solid nodules was 94.2% and 0.77, respectively. Average inter-reader Dice coefficient for nodule segmentation was 0.83 (95% CI: 0.39, 0.96) and 0.86 (95% CI: 0.51, 0.95) for CAD versus readers. Mean growth percentage discrepancy of readers and CAD alone was 1.30 (95% CI: 1.02, 2.21) and 1.35 (95% CI: 1.01, 4.99), respectively. The applied CAD significantly increased radiologist's detection of actionable nodules yet also minimally increasing the false positive rate. The CAD can automatically classify and quantify nodules and calculate nodule growth rate in a cohort of a routine clinical population. Results suggest this Deep Learning software has the potential to assist chest radiologists in the tasks of pulmonary nodule detection and management within their routine clinical practice.

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

Background Targeted lung cancer screening is effective in reducing mortality by upwards of twenty percent. However, screening is not universally available and uptake is variable and socially patterned. Understanding screening behaviour is integral to designing a service that serves its population and promotes equitable uptake. We sought to review the literature to identify barriers and facilitators to screening to inform the development of a pilot lung screening study in Scotland. Methods We used Arksey and O’Malley’s scoping review methodology and PRISMA-ScR framework to identify relevant literature to meet the study aims. Qualitative, quantitative and mixed methods primary studies published between January 2000 and May 2021 were identified and reviewed by two reviewers for inclusion, using a list of search terms developed by the study team and adapted for chosen databases. Results Twenty-one articles met the final inclusion criteria. Articles were published between 2003 and 2021 and came from high income countries. Following data extraction and synthesis, findings were organised into four categories: Awareness of lung screening , Enthusiasm for lung screening , Barriers to lung screening , and Facilitators or ways of promoting uptake of lung screening . Awareness of lung screening was low while enthusiasm was high. Barriers to screening included fear of a cancer diagnosis, low perceived risk of lung cancer as well as practical barriers of cost, travel and time off work. Being health conscious, provider endorsement and seeking reassurance were all identified as facilitators of screening participation. Conclusions Understanding patient reported barriers and facilitators to lung screening can help inform the implementation of future lung screening pilots and national lung screening programmes.

Chronic obstructive pulmonary disease (COPD) is defined by the presence of airflow limitation on spirometry, yet subjects with COPD can have marked differences in computed tomography imaging. These differences may be driven by genetic factors. We hypothesized that a genome-wide association study (GWAS) of quantitative imaging would identify loci not previously identified in analyses of COPD or spirometry. In addition, we sought to determine whether previously described genome-wide significant COPD and spirometric loci were associated with emphysema or airway phenotypes. To identify genetic determinants of quantitative imaging phenotypes. We performed a GWAS on two quantitative emphysema and two quantitative airway imaging phenotypes in the COPDGene (non-Hispanic white and African American), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints), NETT (National Emphysema Treatment Trial), and GenKOLS (Genetics of COPD, Norway) studies and on percentage gas trapping in COPDGene. We also examined specific loci reported as genome-wide significant for spirometric phenotypes related to airflow limitation or COPD. The total sample size across all cohorts was 12,031, of whom 9,338 were from COPDGene. We identified five loci associated with emphysema-related phenotypes, one with airway-related phenotypes, and two with gas trapping. These loci included previously reported associations, including the HHIP, 15q25, and AGER loci, as well as novel associations near SERPINA10 and DLC1. All previously reported COPD and a significant number of spirometric GWAS loci were at least nominally (P < 0.05) associated with either emphysema or airway phenotypes. Genome-wide analysis may identify novel risk factors for quantitative imaging characteristics in COPD and also identify imaging features associated with previously identified lung function loci.

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

ObjectivesNoncardiac findings are common on coronary computed tomography angiography (CCTA). We assessed the clinical impact of noncardiac findings, and potential changes to surveillance scans with the application of new lung nodule guidelines.MethodsThis substudy of the SCOT-HEART randomized controlled trial assessed noncardiac findings identified on CCTA. Clinically significant noncardiac findings were those causing symptoms or requiring further investigation, follow-up or treatment. Lung nodule follow-up was undertaken following the 2005 Fleischner guidelines. The potential impact of the 2015 British Thoracic Society (BTS) and the 2017 Fleischner guidelines was assessed.ResultsCCTA was performed in 1,778 patients and noncardiac findings were identified in 677 (38%). In 173 patients (10%) the abnormal findings were clinically significant and in 55 patients (3%) the findings were the cause of symptoms. Follow-up imaging was recommended in 136 patients (7.6%) and additional clinic consultations were organized in 46 patients (2.6%). Malignancy was diagnosed in 7 patients (0.4%). Application of the new lung nodule guidelines would have reduced the number of patients undergoing a follow-up CT scan: 68 fewer with the 2015 BTS guidelines and 78 fewer with the 2017 Fleischner guidelines; none of these patients subsequently developed malignancy.ConclusionsClinically significant noncardiac findings are identified in 10% of patients undergoing CCTA. Application of new lung nodule guidelines will reduce the cost of surveillance, without the risk of missing malignancy.Key Points• Clinically significant noncardiac findings occur in 10% of patients undergoing CCTA.• Noncardiac findings may be an important treatable cause of chest pain• Further imaging investigations for noncardiac findings were recommended in 8% of patients after CCTA.• New lung nodule follow-up guidelines will result in cost savings.

Background Hypoxemia is a major complication of COPD and is a strong predictor of mortality. We previously identified independent risk factors for the presence of resting hypoxemia in the COPDGene cohort. However, little is known about characteristics that predict onset of resting hypoxemia in patients who are normoxic at baseline. We hypothesized that a combination of clinical, physiologic, and radiographic characteristics would predict development of resting hypoxemia after 5-years of follow-up in participants with moderate to severe COPD Methods We analyzed 678 participants with moderate-to-severe COPD recruited into the COPDGene cohort who completed baseline and 5-year follow-up visits and who were normoxic by pulse oximetry at baseline. Development of resting hypoxemia was defined as an oxygen saturation ≤88% on ambient air at rest during follow-up. Demographic and clinical characteristics, lung function, and radiographic indices were analyzed with logistic regression models to identify predictors of the development of hypoxemia. Results Forty-six participants (7%) developed resting hypoxemia at follow-up. Enrollment at Denver (OR 8.30, 95%CI 3.05–22.6), lower baseline oxygen saturation (OR 0.70, 95%CI 0.58–0.85), self-reported heart failure (OR 6.92, 95%CI 1.56–30.6), pulmonary artery (PA) enlargement on computed tomography (OR 2.81, 95%CI 1.17–6.74), and prior severe COPD exacerbation (OR 3.31, 95%CI 1.38–7.90) were independently associated with development of resting hypoxemia. Participants who developed hypoxemia had greater decline in 6-min walk distance and greater 5-year decline in quality of life compared to those who remained normoxic at follow-up. Conclusions Development of clinically significant hypoxemia over a 5-year span is associated with comorbid heart failure, PA enlargement and severe COPD exacerbation. Further studies are needed to determine if treatments targeting these factors can prevent new onset hypoxemia. Trial registration COPDGene is registered at ClinicalTrials.gov: NCT00608764 (Registration Date: January 28, 2008)

Diagnostic strategies for pulmonary embolism are complex and consist of non-invasive diagnostic tests done to avoid conventional pulmonary angiography as much as possible. We aimed to assess the diagnostic accuracy of magnetic resonance angiography (MRA) for the diagnosis of pulmonary embolism, using conventional pulmonary angiography as a reference method. In a prospective study, we enrolled 141 patients with suspected pulmonary embolism and an abnormal perfusion scan. Patients underwent MRA before conventional pulmonary angiography. Two reviewers, masked with respect to the results of conventional pulmonary angiography, assessed MRA images independently. Statistical analyses used X 2 and 95% CI. MRA was contraindicated in 13 patients (9%), and images were not interpretable in eight (6%). MRA was done in two patients in whom conventional pulmonary angiography was contraindicated. Thus, MRA and conventional pulmonary angiography results were available in 118 patients (84%). Prevalence of pulmonary embolism was 30%. Images were read independently in 115 patients, and agreement obtained in 105 (91%), K=0–75. MRA identified 27 of 35 patients with proven pulmonary embolism (sensitivity 77%, 95% CI 61–90). Sensitivity of MRA for isolated subsegmental, segmental, and central or lobar pulmonary embolism was 40%, 84%, and 100%, respectively (p<0·01 for isolated subsegmental vs segmental or larger pulmonary embolism). However, subgroups contained small numbers. MRA identified pulmonary embolism in two patients with normal angiogram (98%, 92–100). MRA is sensitive and specific for segmental or larger pulmonary embolism. Results are similar to those obtained with helical computed tomography, but MRA has safer contrast agents and does not involve ionising radiation. MRA could become part of the diagnostic strategy for pulmonary embolism.

BackgroundMachine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.MethodsFrom the SCOT-HEART (Scottish Computed Tomography of the HEART) trial, data from 1769 patients was used to train and to test machine learning models (XGBoost, 10-fold cross validation, grid search hyperparameter selection). Two models were separately generated to predict the presence of coronary artery disease (CAD) and an increased burden of low-attenuation coronary artery plaque (LAP) using symptoms, demographic and clinical characteristics, electrocardiography and exercise tolerance testing (ETT).ResultsMachine learning predicted the presence of CAD on CCTA (area under the curve (AUC) 0.80, 95% CI 0.74 to 0.85) better than the 10-year cardiovascular risk score alone (AUC 0.75, 95% CI 0.70, 0.81, p=0.004). The most important features in this model were the 10-year cardiovascular risk score, age, sex, total cholesterol and an abnormal ETT. In contrast, the second model used to predict an increased LAP burden performed similarly to the 10-year cardiovascular risk score (AUC 0.75, 95% CI 0.70 to 0.80 vs AUC 0.72, 95% CI 0.66 to 0.77, p=0.08) with the most important features being the 10-year cardiovascular risk score, age, body mass index and total and high-density lipoprotein cholesterol concentrations.ConclusionMachine learning models can improve prediction of the presence of CAD on CCTA, over the standard cardiovascular risk score. However, it was not possible to improve the prediction of an increased LAP burden based on clinical factors alone.

The use of non-invasive imaging to identify ruptured or high-risk coronary atherosclerotic plaques would represent a major clinical advance for prevention and treatment of coronary artery disease. We used combined PET and CT to identify ruptured and high-risk atherosclerotic plaques using the radioactive tracers 18F-sodium fluoride (18F-NaF) and 18F-fluorodeoxyglucose (18F-FDG). In this prospective clinical trial, patients with myocardial infarction (n=40) and stable angina (n=40) underwent 18F-NaF and 18F-FDG PET-CT, and invasive coronary angiography. 18F-NaF uptake was compared with histology in carotid endarterectomy specimens from patients with symptomatic carotid disease, and with intravascular ultrasound in patients with stable angina. The primary endpoint was the comparison of 18F-fluoride tissue-to-background ratios of culprit and non-culprit coronary plaques of patients with acute myocardial infarction. In 37 (93%) patients with myocardial infarction, the highest coronary 18F-NaF uptake was seen in the culprit plaque (median maximum tissue-to-background ratio: culprit 1·66 [IQR 1·40–2·25] vs highest non-culprit 1·24 [1·06–1·38], p<0·0001). By contrast, coronary 18F-FDG uptake was commonly obscured by myocardial uptake and where discernible, there were no differences between culprit and non-culprit plaques (1·71 [1·40–2·13] vs 1·58 [1·28–2·01], p=0·34). Marked 18F-NaF uptake occurred at the site of all carotid plaque ruptures and was associated with histological evidence of active calcification, macrophage infiltration, apoptosis, and necrosis. 18 (45%) patients with stable angina had plaques with focal 18F-NaF uptake (maximum tissue-to-background ratio 1·90 [IQR 1·61–2·17]) that were associated with more high-risk features on intravascular ultrasound than those without uptake: positive remodelling (remodelling index 1·12 [1·09–1·19] vs 1·01 [0·94–1·06]; p=0·0004), microcalcification (73% vs 21%, p=0·002), and necrotic core (25% [21–29] vs 18% [14–22], p=0·001). 18F-NaF PET-CT is the first non-invasive imaging method to identify and localise ruptured and high-risk coronary plaque. Future studies are needed to establish whether this method can improve the management and treatment of patients with coronary artery disease. Chief Scientist Office Scotland and British Heart Foundation.