Screening for Early Lung Cancer, Chronic Obstructive Pulmonary Disease, and Cardiovascular Disease (the Big-3) Using Low-dose Chest Computed Tomography: Current Evidence and Technical Considerations

Willingness to participate in combination screening for lung cancer, chronic obstructive pulmonary disease and cardiovascular disease in four European countries

OBJECTIVES

Lung cancer screening (LCS), using low-dose computed tomography (LDCT), can be more efficient by simultaneously screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), the Big-3 diseases. This study aimed to determine the willingness to participate in (combinations of) Big-3 screening in four European countries and the relative importance of amendable participation barriers.

METHODS

An online cross-sectional survey aimed at (former) smokers aged 50-75 years elicited the willingness of individuals to participate in Big-3 screening and used analytical hierarchy processing (AHP) to determine the importance of participation barriers.

RESULTS

Respondents were from France (n = 391), Germany (n = 338), Italy (n = 399), and the Netherlands (n = 342), and consisted of 51.2% men. The willingness to participate in screening was marginally influenced by the diseases screened for (maximum difference of 3.1%, for Big-3 screening (73.4%) vs. lung cancer and COPD screening (70.3%)) and by country (maximum difference of 3.7%, between France (68.5%) and the Netherlands (72.3%)). The largest effect on willingness to participate was personal perceived risk of lung cancer. The most important barriers were the missed cases during screening (weight 0.19) and frequency of screening (weight 0.14), while diseases screened for (weight 0.11) ranked low.

CONCLUSIONS

The difference in willingness to participate in LCS showed marginal increase with inclusion of more diseases and limited variation between countries. A marginal increase in participation might result in a marginal additional benefit of Big-3 screening. The amendable participation barriers are similar to previous studies, and the new criterion, diseases screened for, is relatively unimportant.

CLINICAL RELEVANCE STATEMENT

Adding diseases to combination screening modestly improves participation, driven by personal perceived risk. These findings guide program design and campaigns for lung cancer and Big-3 screening. Benefits of Big-3 screening lie in long-term health and economic impact, not participation increase.

KEY POINTS

• It is unknown whether or how combination screening might affect participation. • The addition of chronic obstructive pulmonary disease and cardiovascular disease to lung cancer screening resulted in a marginal increase in willingness to participate. • The primary determinant influencing individuals' engagement in such programs is their personal perceived risk of the disease.

The Smokers Health Multiple ACtions (SMAC-1) Trial: Study Design and Results of the Baseline Round

BACKGROUND

Lung cancer screening with low-dose helical computed tomography (LDCT) reduces mortality in high-risk subjects. Cigarette smoking is linked to up to 90% of lung cancer deaths. Even more so, it is a key risk factor for many other cancers and cardiovascular and pulmonary diseases. The Smokers health Multiple ACtions (SMAC-1) trial aimed to demonstrate the feasibility and effectiveness of an integrated program based on the early detection of smoking-related thoraco-cardiovascular diseases in high-risk subjects, combined with primary prevention. A new multi-component screening design was utilized to strengthen the framework on conventional lung cancer screening programs. We report here the study design and the results from our baseline round, focusing on oncological findings.

METHODS

High-risk subjects were defined as being >55 years of age and active smokers or formers who had quit within 15 years (>30 pack/y). A PLCOm2012 threshold >2% was chosen. Subject outreach was streamlined through media campaign and general practitioners' engagement. Eligible subjects, upon written informed consent, underwent a psychology consultation, blood sample collection, self-evaluation questionnaire, spirometry, and LDCT scan. Blood samples were analyzed for pentraxin-3 protein levels, interleukins, microRNA, and circulating tumor cells. Cardiovascular risk assessment and coronary artery calcium (CAC) scoring were performed. Direct and indirect costs were analyzed focusing on the incremental cost-effectiveness ratio per quality-adjusted life years gained in different scenarios. Personalized screening time-intervals were determined using the "Maisonneuve risk re-calculation model", and a threshold <0.6% was chosen for the biennial round.

RESULTS

In total, 3228 subjects were willing to be enrolled. Out of 1654 eligible subjects, 1112 participated. The mean age was 64 years (M/F 62/38%), with a mean PLCOm2012 of 5.6%. Former and active smokers represented 23% and 77% of the subjects, respectively. At least one nodule was identified in 348 subjects. LDCTs showed no clinically significant findings in 762 subjects (69%); thus, they were referred for annual/biennial LDCTs based on the Maisonneuve risk (mean value = 0.44%). Lung nodule active surveillance was indicated for 122 subjects (11%). Forty-four subjects with baseline suspicious nodules underwent a PET-FDG and twenty-seven a CT-guided lung biopsy. Finally, a total of 32 cancers were diagnosed, of which 30 were lung cancers (2.7%) and 2 were extrapulmonary cancers (malignant pleural mesothelioma and thymoma). Finally, 25 subjects underwent lung surgery (2.25%). Importantly, there were zero false positives and two false negatives with CT-guided biopsy, of which the patients were operated on with no stage shift. The final pathology included lung adenocarcinomas (69%), squamous cell carcinomas (10%), and others (21%). Pathological staging showed 14 stage I (47%) and 16 stage II-IV (53%) cancers.

CONCLUSIONS

LDCTs continue to confirm their efficacy in safely detecting early-stage lung cancer in high-risk subjects, with a negligible risk of false-positive results. Re-calculating the risk of developing lung cancer after baseline LDCTs with the Maisonneuve model allows us to optimize time intervals to subsequent screening. The Smokers health Multiple ACtions (SMAC-1) trial offers solid support for policy assessments by policymakers. We trust that this will help in developing guidelines for the large-scale implementation of lung cancer screening, paving the way for better outcomes for lung cancer patients.

Evaluation of Conventional Cardiovascular Risk Factors and Ordinal Coronary Artery Calcium Scoring in a Lung Cancer Screening Cohort

(1) Background: Lung cancer screening (LCS) consists of low-dose computed tomography (LDCT) results to reduce lung cancer-related mortality. The LCS program has a unique opportunity to impact CVD mortality by providing tools for CVD risk assessment and implementing preventative strategies. In this study, we estimated standardized CVD risk (SCORE) and assessed the prevalence of coronary artery calcium (CAC) in a Polish LCS cohort. (2) Methods: In this observational study, 494 LCS participants aged 50-79 years with a cigarette smoking history of at least 30 pack-years were included. Medical history, anthropometric measurements, blood pressure measurements, serum glucose, and cholesterol levels were assessed in one visit. CVD risk assessment using SCORE tables was performed. The results were compared to the general population (NATPOL 2011 study). On LDCT scans, CAC was classified using an Ordinal Score ranging from 0 to 12. (3) Results: The prevalence of classic cardiovascular risk factors was very high. Among study participants, 83.7% of men and 40.7% of women were classified with a very high CVD SCORE risk (>10%). CAC was reported in 190 (47%) participants. Calcification was categorized as severe (CAC ≥ 4) in 84 (21%) participants. (4) Conclusions: Due to the high cardiovascular risk, intensive preventive strategies are recommended for LCS participants.

Cost-effectiveness of volume computed tomography in lung cancer screening: a cohort simulation based on Nelson study outcomes

OBJECTIVES

This study aimed to evaluate the cost-effectiveness of lung cancer screening (LCS) with volume-based low-dose computed tomography (CT) versus no screening for an asymptomatic high-risk population in the United Kingdom (UK), utilising the long-term insights provided by the NELSON study, the largest European randomized control trial investigating LCS.

METHODS

A cost-effectiveness analysis was conducted using a decision tree and a state-transition Markov model to simulate the identification, diagnosis, and treatments for a lung cancer high-risk population, from a UK National Health Service (NHS) perspective. Eligible participants underwent annual volume CT screening and were compared to a cohort without the option of screening. Screen-detected lung cancers, costs, quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER) were predicted.

RESULTS

Annual volume CT screening of 1.3 million eligible participants resulted in 96,474 more lung cancer cases detected in early stage, and 73,825 fewer cases in late stage, leading to 53,732 premature lung cancer deaths averted and 421,647 QALYs gained, compared to no screening. The ICER was £5,455 per QALY. These estimates were robust in sensitivity analyses.

LIMITATIONS

Lack of long-term survival data for lung cancer patients; deficiency in rigorous micro-costing studies to establish detailed treatment costs inputs for lung cancer patients.

CONCLUSIONS

Annual LCS with volume-based low-dose CT for a high-risk asymptomatic population is cost-effective in the UK, at a threshold of £20,000 per QALY, representing an efficient use of NHS resources with substantially improved outcomes for lung cancer patients, as well as additional societal and economic benefits for society as a whole. These findings advocate evidence-based decisions for the potential implementation of a nationwide LCS in the UK.

Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction

OBJECTIVES

Computed tomography (CT)-based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters.

METHODS

A deep-learning airway segmentation model was newly trained on 24 Imaging in Lifelines (ImaLife) low-dose chest CT scans. This model was combined with an optimal-surface graph-cut for airway wall segmentation. These tools were used to calculate bronchial parameters in CT scans of 188 ImaLife participants with two scans an average of 3 months apart. Bronchial parameters were compared for reproducibility assessment, assuming no change between scans.

RESULTS

Of 376 CT scans, 374 (99%) were successfully measured. Segmented airway trees contained a mean of 10 generations and 250 branches. The coefficient of determination (R2) for the luminal area (LA) ranged from 0.93 at the trachea to 0.68 at the 6th generation, decreasing to 0.51 at the 8th generation. Corresponding values for Wall Area Percentage (WAP) were 0.86, 0.67, and 0.42, respectively. Bland-Altman analysis of LA and WAP per generation demonstrated mean differences close to 0; limits of agreement (LoA) were narrow for WAP and Pi10 (± 3.7% of mean) and wider for LA (± 16.4-22.8% for 2-6th generations). From the 7th generation onwards, there was a sharp decrease in reproducibility and a widening LoA.

CONCLUSION

The outlined approach for automatic bronchial parameter measurement on low-dose chest CT scans is a reliable way to assess the airway tree down to the 6th generation.

STATEMENT ON CLINICAL RELEVANCE

This reliable and fully automatic pipeline for bronchial parameter measurement on low-dose CT scans has potential applications in screening for early disease and clinical tasks such as virtual bronchoscopy or surgical planning, while also enabling the exploration of bronchial parameters in large datasets.

KEY POINTS

• Deep learning combined with optimal-surface graph-cut provides accurate airway lumen and wall segmentations on low-dose CT scans. • Analysis of repeat scans showed that the automated tools had moderate-to-good reproducibility of bronchial measurements down to the 6th generation airway. • Automated measurement of bronchial parameters enables the assessment of large datasets with less man-hours.

Favorable Mortality-to-Incidence Ratio Trends of Lung Cancer in Countries with High Computed Tomography Density

Background and Objectives: The prognoses of lung cancer deteriorate dramatically as the cancer progresses through its stages. Therefore, early screening using techniques such as low-dose computed tomography (LDCT) is critical. However, the epidemiology of the association between the popularization of CT and the prognosis for lung cancer is not known. Materials and Methods: Data were obtained from GLOBOCAN and the health data and statistics of the World Health Organization. Mortality-to-incidence ratios (MIRs) and the changes in MIR over time (δMIR; calculated as the difference between MIRs in 2018 and 2012) were used to evaluate the correlation with CT density disparities via Spearman's rank correlation coefficient. Results: Countries with zero CT density presented a relatively low incidence crude rate and a relatively high MIR in 2018 and a negative δMIR. Conversely, countries with a CT density over 30 had a positive δMIR. The CT density was significantly associated with the HDI score and MIR in 2018, whereas it demonstrated no association with MIR in 2012. The CT density and δMIR also showed a significant linear correlation. Conclusions: CT density was significantly associated with lung cancer MIR in 2018 and with δMIR, indicating favorable clinical outcomes in countries in which CT has become popularized.

Assessment of Calcium Score Cutoff Point for Clinically Significant Aortic Stenosis on Lung Cancer Screening Program Low-Dose Computed Tomography-A Cross-Sectional Analysis

Low-dose computed tomography (LDCT) is predominantly applied in lung cancer screening programs. Tobacco smoking is the main risk factor for developing lung cancer but is also common for cardiovascular diseases, including aortic stenosis (AS). Consequently, an increased prevalence of cardiovascular diseases is expected in lung cancer screenees. Therefore, initial aortic valve calcification evaluation should be additionally performed on LDCT. The aim of this study was to estimate a calcium score (CS) cutoff point for clinically significant AS diagnosis based on LDCT, confirmed by echocardiographic examination. The study included 6631 heavy smokers who participated in a lung cancer screening program (MOLTEST BIS). LDCTs were performed on all individuals and were additionally assessed for aortic valve calcification with the use of CS according to the Agatston method. Patients with CS ≥ 900 were referred for echocardiography to confirm the diagnosis of AS and to evaluate its severity. Of 6631 individuals, 54 met the inclusion criteria and underwent echocardiography for confirmation and assessment of AS. Based on that data, receiver operating characteristic (ROC) curves of CS were plotted, and cutoff points for clinically significant AS diagnosis were established: A CS of 1758 for at least moderate AS had 85.71% (CI 65.36-95.02%) sensitivity and 75.76% (CI 58.98-87.17%) specificity; a CS of 2665 for severe AS had 87.5% (CI 73.89-94.54%) sensitivity and 76.92% (CI 49.74-91.82%) specificity. This is the first study to assess possible CS cutoff points for diagnosing clinically significant AS detected by LDCT in lung cancer screening participants. LDCT with CS assessment could enable early detection of patients with clinically significant AS and therefore identify patients who require appropriate treatment.

Low-dose CT for lung cancer screening: position paper from the Italian college of thoracic radiology

Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program. Because LDCT is the backbone of LCS, Italian radiologists with LCS expertise are presenting this position paper that encompasses recommendations for LDCT scan protocol and its reading. Moreover, fundamentals for classification of lung nodules and other findings at LDCT test are detailed along with international guidelines, from the European Society of Thoracic Imaging, the British Thoracic Society, and the American College of Radiology, for their reporting and management in LCS. The Italian College of Thoracic Radiologists produced this document to provide the basics for radiologists who plan to set up or to be involved in LCS, thus fostering homogenous evidence-based approach to the LDCT test over the Italian territory and warrant comparison and analyses throughout National and International practices.

Can we increase efficiency of CT lung cancer screening by combining with CVD and COPD screening? Results of an early economic evaluation

Objectives

Estimating the maximum acceptable cost (MAC) per screened individual for low-dose computed tomography (LDCT) lung cancer (LC) screening, and determining the effect of additionally screening for chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), or both on the MAC.

Methods

A model-based early health technology assessment (HTA) was conducted to estimate whether a new intervention could be cost-effective by calculating the MAC at a willingness-to-pay (WTP) of €20k/quality-adjusted life-year (QALY) and €80k/QALY, for a population of current and former smokers, aged 50–75 years in The Netherlands. The MAC was estimated based on incremental QALYs gained from a stage shift assuming screened individuals are detected in earlier disease stages. Data were obtained from literature and publicly available statistics and validated with experts.

Results

The MAC per individual for implementing LC screening at a WTP of €20k/QALY was €113. If COPD, CVD, or both were included in screening, the MAC increased to €230, €895, or €971 respectively. Scenario analyses assessed whether screening-specific disease high-risk populations would improve cost-effectiveness, showing that high-risk CVD populations were more likely to improve economic viability compared to COPD.

Conclusions

The economic viability of combined screening is substantially larger than for LC screening alone, primarily due to benefits from CVD screening, and is dependent on the target screening population, which is key to optimise the screening program. The total cost of breast and cervical cancer screening is lower (€420) than the MAC of Big-3, indicating that Big-3 screening may be acceptable from a health economic perspective.

Key Points

• Once-off combined low-dose CT screening for lung cancer, COPD, and CVD in individuals aged 50–75 years is potentially cost-effective if screening would cost less than €971 per screened individual.

• Multi-disease screening requires detailed insight into the co-occurrence of these diseases to identify the optimal target screening population.

• With the same target screening population and WTP, lung cancer-only screening should cost less than €113 per screened individual to be cost-effective.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08422-7.

Prevalence, symptom burden and under-diagnosis of chronic obstructive pulmonary disease in Polish lung cancer screening population: a cohort observational study

OBJECTIVES

Lung cancer screening using low-dose CT may be not effective without considering the presence of comorbidities related to chronic smoking. The aim of the study was to establish the prevalence of chronic obstructive pulmonary disease (COPD) in group of phighlight the potential benefits atients participating in the largest Polish lung cancer screening programme MOLTEST-BIS and attempt to confirm the necessity of combined lung cancer and COPD screening.

DESIGN

Cohort, prospective study.

SETTING

Medical University of Gdańsk, Poland PARTICIPANTS: The study included 754 participants in lung cancer screening trial from the Pomeranian region, aged 50-70 years old, current and former smokers with a smoking history ≥30 pack-years.

PRIMARY AND SECONDARY OUTCOME MEASURES

Questionnaire, physical examination, anthropometric measurements, spirometry test before and after inhaled bronchodilator (400 µg of salbutamol) RESULTS: Obstructive disorders were diagnosed in 186 cases (103 male and 83 female). In the case of 144 participants (19.73%), COPD was diagnosed. Only 13.3% of participants with COPD were known about the disease earlier. According to classification of airflow limitation 55.6% of diagnosed COPD were in Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 (mild), 38.9% in GOLD 2 (moderate), 4.9% in GOLD 3 (severe) and 0.7% in GOLD 4 (very severe) stage. Women with recognition of COPD were younger than men (63.7 vs 66.3 age) and they smoked less cigarettes (41.1 vs 51.9 pack-years).

CONCLUSIONS

Prevalence of COPD in Polish lung cancer screening cohort is significant. The COPD in this group is remarkably under-diagnosed. Most diagnosed COPD cases were in the initial stage of advancement. This early detection of airflow limitation highlights the potential benefits arising from combined oncological-pulmonary screening.NKBBN.

Implementing lung cancer screening in Europe: taking a systems approach

Lung cancer is the leading cause of cancer death in Europe. Screening by means of low-dose computed tomography (LDCT) can shift detection to an earlier stage and reduce lung cancer mortality in high-risk individuals. However, to date, Poland, Croatia, Italy, and Romania are the only European countries to commit to large-scale implementation of targeted LDCT screening. Using a health systems approach, this article evaluates key factors needed to enable the successful implementation of screening programs across Europe. Recent literature on LDCT screening was reviewed for 10 countries (Belgium, Croatia, France, Germany, Italy, the Netherlands, Poland, Spain, Sweden, and United Kingdom) and complemented by 17 semistructured interviews with local experts. Research findings were mapped against a health systems framework adapted for lung cancer screening. The European policy landscape is highly variable, but potential barriers to implementation are similar across countries and consistent with those reported for other cancer screening programs. While consistent quality and safety of screening must be ensured across all screening centers, system factors are also important. These include appropriate data infrastructure, targeted recruitment methods that ensure equity in participation, sufficient capacity and workforce training, full integration of screening with multidisciplinary care pathways, and smoking cessation programs. Stigma and underlying perceptions of lung cancer as a self-inflicted condition are also important considerations. Building on decades of implementation research, governments now have a unique opportunity to establish effective, efficient, and equitable lung cancer screening programs adapted to their health systems, curbing the impact of lung cancer on their populations.

Quantification of pulmonary vessel volumes on low-dose computed tomography in a healthy male Chinese population: the effects of aging and smoking

BACKGROUND

This study sought to determine pulmonary vascular volumes (PVVs) on low-dose computed tomography (LDCT) in a healthy male Chinese population and analyze the effects of aging and smoking on PVVs.

METHODS

A total of 1,320 healthy male participants (comprising 720 non-smokers, 445 smokers, and 155 ex-smokers) who underwent LDCT were retrospectively included in this study. Their demographic data and smoking status data were collected. An automatic integration segmentation approach for LDCT was used to segment pulmonary vessels semi-automatically. The PVVs of the whole lung, left lung, and right lung on LDCT were calculated, and correlations between PVVs and age and smoking status were then compared.

RESULTS

The inter-rater correlation coefficient of the whole lung, left lung, and right lung PVVs was 0.98 [95% confidence interval (CI): 0.95-0.99], 0.97 (95% CI: 0.93-0.98), and 0.97 (95% CI: 0.94-0.99), respectively. The intra-class correlation coefficient of the whole lung left lung, and right lung PVVs was 0.98 (95% CI: 0.95-0.99), 0.96 (95% CI: 0.95-0.99), and 0.96 (95% CI: 0.92-0.98), respectively. In non-smokers, PVVs decreased with age. The PVVs of heavy smokers were higher than those of light smokers, ex-smokers, and non-smokers. The PVVs of ex-smokers were comparable to those of light smokers.

CONCLUSIONS

The PVVs measured on LDCT tended to decrease with age in healthy male non-smokers gradually. Compared to non-smokers, the PVVs of smokers increased, even with the normal lung function.

Personalising lung cancer screening: An overview of risk-stratification opportunities and challenges

Randomised clinical trials have shown the efficacy of computed tomography lung cancer screening, initiating discussions on whether and how to implement population‐based screening programs. Due to smoking behaviour being the primary risk‐factor for lung cancer and part of the criteria for determining screening eligibility, lung cancer screening is inherently risk‐based. In fact, the selection of high‐risk individuals has been shown to be essential in implementing lung cancer screening in a cost‐effective manner. Furthermore, studies have shown that further risk‐stratification may improve screening efficiency, allow personalisation of the screening interval and reduce health disparities. However, implementing risk‐based lung cancer screening programs also requires overcoming a number of challenges. There are indications that risk‐based approaches can negatively influence the trade‐off between individual benefits and harms if not applied thoughtfully. Large‐scale implementation of targeted, risk‐based screening programs has been limited thus far. Consequently, questions remain on how to efficiently identify and invite high‐risk individuals from the general population. Finally, while risk‐based approaches may increase screening program efficiency, efficiency should be balanced with the overall impact of the screening program. In this review, we will address the opportunities and challenges in applying risk‐stratification in different aspects of lung cancer screening programs, as well as the balance between screening program efficiency and impact.

The radiologist's role in lung cancer screening

Lung cancer is still the deadliest cancer in men and women worldwide. This high mortality is related to diagnosis in advanced stages, when curative treatment is no longer an option. Large randomized controlled trials have shown that lung cancer screening (LCS) with low-dose computed tomography (CT) can detect lung cancers at earlier stages and reduce lung cancer-specific mortality. The recent publication of the significant reduction of cancer-related mortality by 26% in the Dutch-Belgian NELSON LCS trial has increased the likelihood that implementation of LCS in Europe will move forward. Radiologists are important stakeholders in numerous aspects of the LCS pathway. Their role goes beyond nodule detection and nodule management. Being part of a multidisciplinary team, radiologists are key players in numerous aspects of implementation of a high quality LCS program. In this non-systematic review we discuss the multifaceted role of radiologists in LCS.

Screen-detected solid nodules: from detection of nodule to structured reporting

Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.

Improved trends of lung cancer mortality-to-incidence ratios in countries with high healthcare expenditure

Background

Lung cancer stage has a significant impact on prognosis, and early detection of lung cancer relies on screenings. Despite the strong relationship between screening and lung cancer staging, the role of healthcare expenditure in lung cancer outcomes remains unknown. The aim of this study was to evaluate the relationship between economic status and clinical outcomes in lung cancer.

Methods

Data were obtained from GLOBOCAN and the World Health Organization. Mortality‐to‐incidence ratios (MIRs) and their change over time, calculated as the difference between the MIRs of 2012 and 2018 (δMIR), were used to evaluate their correlation to expenditures on healthcare and human development index (HDI) disparities via Spearman's rank correlation coefficient.

Results

Regions such as North America have relatively high crude incidence rates but low MIR values. Furthermore, countries with lower crude incidence rates spent less on healthcare. The results show significant negative associations between HDI, current health expenditure (CHE) per capita, CHE as a percentage of gross domestic product (CHE/GDP), and MIR. As for MIR and δMIR, countries with favorable MIRs also showed improving MIRs based on δMIR.

Conclusions

HDI, CHE per capita, CHE/GDP, and development status play noticeable roles in the prognosis of lung cancer, leading to large disparities in clinical outcomes.

Impact on All-Cause and Cardiovascular Mortality Rates of Coronary Artery Calcifications Detected during Organized, Low-Dose, Computed-Tomography Screening for Lung Cancer: Systematic Literature Review and Meta-Analysis

Simple Summary

The results of several randomized studies showed the efficacy of organized, low-dose, computed-tomography (CT) scan lung-cancer screening in lowering all-cause and lung-cancer-specific mortality rates. Low-dose CT scans can also detect and quantify coronary artery calcifications (CACs). By means of meta-analysis, we were able to show that the presence of CACs in CT performed in this setting was associated with an enhanced risk of cardiovascular and all-cause mortality for men and women. These finding plead for the implementation of preventive interactions against cardiovascular risk in lung-cancer screening-program participants found to have CACs.

Abstract

Although organized, low-dose, computed-tomography (CT) scan lung-cancer screening has been shown to lower all-cause and lung-cancer-specific mortality, the primary cause of death for subjects eligible for such screening remains cardiovascular (CV) mortality. This meta-analysis study was undertaken to evaluate the impact of screening-scan-detected coronary artery calcifications (CACs) on CV and all-cause mortality. We conducted a systematic review and meta-analysis of studies reporting CV mortality according to the Agatson CAC score for participants in a lung-cancer screening program of randomized clinical or cohort studies. PubMed, Embase, and Cochrane databases were screened in June 2020. Two authors independently selected articles and extracted data. Six studies, including 20,175 subjects, were retained. CV and all-cause mortality rates were higher for subjects with CAC scores >0, with respective relative risks of 2.02 [95% CI 1.23–3.32] and 2.29 [95% CI 1.00–5.21]. Both mortality rates were even higher for those with high CAC scores (>400 or >1000). CACs are more common in men than in women, with an odds ratio of 1.49 [95% CI 1.40–1.59]. The presence of CAC is associated with CV mortality with an RR of 2.05 [95% CI 1.20–3.57] in men and 2.37 [CI 95% 1.29–5.09] in women, respectively. Analysis of lung-cancer-screening scans for CACs is a tool able to predict CV mortality. Prospective studies within those programs are needed to assess the benefit of primary CV prevention based on CAC detection.

Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography

PURPOSE

The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data.

MATERIALS AND METHODS

Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated.

RESULTS

CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined.

CONCLUSION

The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.

Computed Tomography Screening for Early Lung Cancer, COPD and Cardiovascular Disease in Shanghai: Rationale and Design of a Population-based Comparative Study

RATIONALE AND OBJECTIVES

To describe the rational and design of a population-based comparative study. The objective of the study is to assess the screening performance of volume-based management of CT-detected lung nodule in comparison to diameter-based management, and to improve the effectiveness of CT screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), in addition to lung cancer, based on quantitative measurement of CT imaging biomarkers in a Chinese screening setting.

MATERIALS AND METHODS

A population-based comparative study is being performed, including 10,000 asymptomatic participants between 40 and 74 years old from Shanghai urban population. Participants in the intervention group undergo a low-dose chest and cardiac CT scan at baseline and 1 year later, and are managed according to NELCIN-B3 protocol. Participants in the control group undergo a low-dose chest CT scan according to the routine CT protocol and are managed according to the clinical practice. Epidemiological data are collected through questionnaires. In the fourth year from baseline, the diagnosis of the three diseases will be collected.

RESULTS

The unnecessary referral rate will be compared between NELCIN-B3 and standard protocol for managing early-detected lung nodules. The effectiveness of quantitative measurement of CT imaging biomarkers for early detection of lung cancer, COPD and CVD will be evaluated.

CONCLUSION

We expect that the quantitative assessment of the CT imaging biomarkers will reduce the number of unnecessary referrals for early detected lung nodules, and will improve the early detection of COPD and CVD in a Chinese urban population.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03988322. Registered on 14 June 2019.

Coronary artery calcium scoring in individuals at risk for coronary artery disease: current status and future perspectives

The aim of this review is to provide clinicians with an overview of the role of coronary artery calcium (CAC) scoring across the spectrum ranging from asymptomatic individuals to chronic chest pain patients. We will briefly introduce the technical background of CAC scoring, summarize the major guidelines per type of patient at risk and discuss latest research with respect to CAC. Finally, the reader should be able to determine when CAC scoring is indicated or may be of added value.

Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing

OBJECTIVE. The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry). MATERIALS AND METHODS. A total of 141 patients (72 women, mean age ± SD of 66.46 ± 9.7 years [range, 23-86 years]; 69 men, mean age of 66.72 ± 11.4 years [range, 27-91 years]) who underwent both chest CT acquisition and spirometry within 6 months were retrospectively included. The spirometry-based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second to forced vital capacity) was used to measure emphysema severity; a value less than 0.7 was considered to indicate airway obstruction. Segmentation of the lung based on two different reconstruction methods was carried out by using a deep convolution image-to-image network. This multilayer convolutional neural network was combined with multilevel feature chaining and depth monitoring. To discriminate the output of the network from ground truth, an adversarial network was used during training. Emphysema was quantified using spatial filtering and attenuation-based thresholds. Emphysema quantification and TI were compared using the Spearman correlation coefficient. RESULTS. The mean TI for all patients was 0.57 ± 0.13. The mean percentages of emphysema using reconstruction methods 1 and 2 were 9.96% ± 11.87% and 8.04% ± 10.32%, respectively. AI-based emphysema quantification showed very strong correlation with TI (reconstruction method 1, ρ = -0.86; reconstruction method 2, ρ = -0.85; both p < 0.0001), indicating that AI-based emphysema quantification meaningfully reflects clinical pulmonary physiology. CONCLUSION. AI-based, fully automated emphysema quantification shows good correlation with TI, potentially contributing to an image-based diagnosis and quantification of emphysema severity.

The Association Between Marital Status, Coronary Computed Tomography Imaging Biomarkers, and Mortality in a Lung Cancer Screening Population

PURPOSE

The purpose of this study was to elucidate the impact of being unmarried on coronary computed tomography (CT) imaging biomarkers and mortality in a lung cancer screening population.

MATERIALS AND METHODS

In this retrospective case-control study, 5707 subjects (3777 married; mean age: 61.9±5.1 y and 1930 unmarried; mean age: 61.9±5.3 y) underwent low-dose CT as part of the National Lung Screening Trial (NLST). The median follow-up time was 6.5 (Q1-Q3: 5.6 to 6.9) years. Being unmarried was defined as never married, widowed, separated, or divorced. Being married was defined as married or living as married. Our primary endpoint was cardiovascular disease (CVD)-related death; our secondary endpoint was all-cause mortality. Coronary CT imaging biomarkers (calcium score, density, and volume) on low-dose chest CT scan were calculated using dedicated automatic software. Weighted Cox proportional-hazards regression was performed to examine the association between marital status and death. Kaplan-Meier curves were generated to visualize subject survival.

RESULTS

Being unmarried was significantly associated with an increased risk for CVD-related death (hazard ratio [HR]: 1.58; 95% confidence interval [CI]: 1.31-1.91) and all-cause mortality (HR: 1.39; 95% CI: 1.26-1.53), which remained significant even after adjusting for traditional cardiovascular risk factors (HR CVD death: 1.75; 1.44-2.12 and HR all-cause mortality: 1.58; 95% CI: 1.43-1.74) and coronary calcium score (HR CVD death: 1.58; 95% CI: 1.31-1.91 and HR all-cause mortality: 1.40; 95% CI: 1.27-1.54).

CONCLUSIONS

Being unmarried is associated with an increased CVD-related death and all-cause mortality mainly due to cardiovascular etiology. On the basis of this, marital status might be taken into consideration when assessing individuals' health status.

Early imaging biomarkers of lung cancer, COPD and coronary artery disease in the general population: rationale and design of the ImaLife (Imaging in Lifelines) Study

Lung cancer, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD) are expected to cause most deaths by 2050. State-of-the-art computed tomography (CT) allows early detection of lung cancer and simultaneous evaluation of imaging biomarkers for the early stages of COPD, based on pulmonary density and bronchial wall thickness, and of CAD, based on the coronary artery calcium score (CACS), at low radiation dose. To determine cut-off values for positive tests for elevated risk and presence of disease is one of the major tasks before considering implementation of CT screening in a general population. The ImaLife (Imaging in Lifelines) study, embedded in the Lifelines study, is designed to establish the reference values of the imaging biomarkers for the big three diseases in a well-defined general population aged 45 years and older. In total, 12,000 participants will undergo CACS and chest acquisitions with latest CT technology. The estimated percentage of individuals with lung nodules needing further workup is around 1–2%. Given the around 10% prevalence of COPD and CAD in the general population, the expected number of COPD and CAD is around 1000 each. So far, nearly 4000 participants have been included. The ImaLife study will allow differentiation between normal aging of the pulmonary and cardiovascular system and early stages of the big three diseases based on low-dose CT imaging. This information can be finally integrated into personalized precision health strategies in the general population.