Socio-Economic Inequalities in Lung Cancer Outcomes: An Overview of Systematic Reviews

In the past decade, evidence has accumulated about socio-economic inequalities in very diverse lung cancer outcomes. To better understand the global effects of socio-economic factors in lung cancer, we conducted an overview of systematic reviews. Four databases were searched for systematic reviews reporting on the relationship between measures of socio-economic status (SES) (individual or area-based) and diverse lung cancer outcomes, including epidemiological indicators and diagnosis- and treatment-related variables. AMSTAR-2 was used to assess the quality of the selected systematic reviews. Eight systematic reviews based on 220 original studies and 8 different indicators were identified. Compared to people with a high SES, people with a lower SES appear to be more likely to develop and die from lung cancer. People with lower SES also have lower cancer survival, most likely due to the lower likelihood of receiving both traditional and next-generation treatments, higher rates of comorbidities, and the higher likelihood of being admitted as emergency. People with a lower SES are generally not diagnosed at later stages, but this may change after broader implementation of lung cancer screening, as early evidence suggests that there may be socio-economic inequalities in its use.

Participation and Yield of a Lung Cancer Screening Program in Hebei, China

ObjectiveLung cancer screening has been widely conducted in Western countries. However, population-based lung cancer screening programs in Hebei in China are sparse. Our study aimed to assess the participation rate and detection rate of positive nodules and lung cancer in Hebei province.MethodIn total, 228 891 eligible participants aged 40–74 years were enrolled in the Cancer Screening Program in Hebei from 2013 to 2019. A total of 54 846 participants were evaluated as the lung cancer high-risk population by a risk score system which basically followed the Harvard Risk Index and was adjusted for the characteristics of the Chinese population. Then this high-risk population was recommended for low-dose computed tomography (LDCT) screening. And all participants attended annual passive follow-up, and the active follow-up interval was based on radiologist’s suggestion. All participants were followed-up until December 31, 2020. The overall, group-specific participation rates were calculated, and its associated factors were analyzed by a multivariable logistic regression model. Participation rates and detection of positive nodules and lung cancer were reported.ResultsThe overall participation rate was 52.69%, where 28 899 participants undertook LDCT screening as recommended. The multivariable logistic regression model demonstrated that a high level of education, having disease history, and occupational exposure were found to be associated with the participation in LDCT screening. The median follow-up time was 3.56 person-years. Overall, the positive identification of lung nodules and suspected lung cancer were 12.73% and 1.46% through LDCT screening. After the native and passive follow-up, 257 lung cancer cases were diagnosed by lung cancer screening, and the detection rate of lung cancer was 0.89% in the screening group. And its incidence density was 298.72 per 100,000. Positive lung nodule rate and detection rate were increased with age.ConclusionOur study identified personal and epidemiological factors that could affect the participation rate. Our findings could provide the guideline for precise prevention and control of lung cancer in the future.

Cost-effectiveness and health impact of lung cancer screening with low-dose computed tomography for never smokers in Japan and the United States: a modelling study

Background Never smokers in Asia have a higher incidence of lung cancer than in Europe and North America. We aimed to assess the cost-effectiveness of lung cancer screening with low-dose computed tomography (LDCT) for never smokers in Japan and the United States. Methods We developed a state-transition model for three strategies: LDCT, chest X-ray (CXR), and no screening, using a healthcare payer perspective over a lifetime horizon. Sensitivity analyses were also performed. Main outcomes were costs, quality-adjusted life-years (QALYs), life expectancy life-years (LYs), incremental cost-effectiveness ratios (ICERs), and deaths from lung cancer. The willingness-to-pay level was US$100,000 per QALY gained. Results LDCT yielded the greatest benefits with the lowest cost in Japan, but the ICERs of LDCT compared with CXR were US$3,001,304 per QALY gained for American men and US$2,097,969 per QALY gained for American women. Cost-effectiveness was sensitive to the incidence of lung cancer. Probabilistic sensitivity analyses demonstrated that LDCT was cost-effective 99.3–99.7% for Japanese, no screening was cost-effective 77.7% for American men, and CXR was cost-effective 93.2% for American women. Compared with CXR, LDCT has the cumulative lifetime potential for 60-year-old Japanese to save US$117 billion, increase 2,339,349 QALYs and 3,020,102 LYs, and reduce 224,749 deaths, and the potential for 60-year-old Americans to cost US$120 billion, increase 48,651 QALYs and 67,988 LYs, and reduce 2,309 deaths. Conclusions This modelling study suggests that LDCT screening for never smokers has the greatest benefits and cost savings in Japan, but is not cost-effective in the United States. Assessing the risk of lung cancer in never smokers is important for introducing population-based LDCT screening.

Development, validation, and evaluation of prediction models to identify individuals at high risk of lung cancer for screening in the English primary care population using the QResearch database: research protocol and statistical analysis plan

Background and research aim: Lung cancer is a research priority in the UK. Early diagnosis of lung cancer can improve patients' survival outcomes. The DART-QResearch project is part of a larger academic-industrial collaborative initiative, using big data and artificial intelligence to improve patient outcomes with thoracic diseases. There are two general research aims in the DART-QResearch project: (1) to understand the natural history of lung cancer, (2) to develop, validate, and evaluate risk prediction models to select patients at high risk for lung cancer screening. Methods: This population-based cohort study uses the QResearch database (version 45) and includes patients aged between 25 and 84 years old and without a diagnosis of lung cancer at cohort entry (study period: 1 January 2005 to 31 December 2020). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. The following statistical techniques will be used for different research objectives, including descriptive statistics, multi-level modelling, multiple imputation for missing data, fractional polynomials to explore non-linear relationships between continuous variables and the outcome, and Cox regression for the prediction model. We will update our QCancer (lung, 10-year risk) algorithm, and compare it with the other two mainstream models (LLP and PLCOM2012) for lung cancer screening using the same dataset. We will evaluate the discrimination, calibration, and clinical usefulness of the prediction models, and recommend the best one for lung cancer screening for the English primary care population. Discussion: The DART-QResearch project focuses on both symptomatic presentation and asymptomatic patients in the lung cancer care pathway. A better understanding of the patterns, trajectories, and phenotypes of symptomatic presentation may help GPs consider lung cancer earlier. Screening asymptomatic patients at high risk is another route to achieve earlier diagnosis of lung cancer. The strengths of this study include using large-scale representative population-based clinical data, robust methodology, and a transparent research process. This project has great potential to contribute to the national cancer strategic plan and yields substantial public and societal benefits through earlier diagnosis of lung cancer.

Prediction of invasive adenocarcinomas manifesting as pure ground-glass nodules based on radiomic signature of low-dose CT in lung cancer screening

Objectives: To develop a radiomic model based on low-dose CT (LDCT) to distinguish invasive adenocarcinomas (IAs) from adenocarcinoma in situ/minimally invasive adenocarcinomas (AIS/MIAs) manifesting as pure ground-glass nodules (pGGNs) and compare its performance with conventional quantitative and semantic features of LDCT, radiomic model of standard-dose CT, and intraoperative frozen section (FS). Methods: A total of 147 consecutive pathologically confirmed pGGNs were divided into primary cohort (43 IAs and 60 AIS/MIAs) and validation cohort (19 IAs and 25 AIS/MIAs). Logistic regression models were built using conventional quantitative and semantic features, selected radiomic features of LDCT and standard-dose CT, and intraoperative FS diagnosis, respectively. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. Results: The AUCs of quantitative-semantic model, radiomic model of LDCT, radiomic model of standard-dose CT, and FS model were 0.879 (95% CI, 0.801–0.935), 0.929 (95% CI, 0.862–0.971), 0.941 (95% CI, 0.876–0.978), and 0.884 (95% CI, 0.805–0.938) in the primary cohort and 0.897 (95% CI, 0.768–0.968), 0.933 (95% CI, 0.815–0.986), 0.901 (95% CI, 0.773–0.970), and 0.828 (95% CI, 0.685–0.925) in the validation cohort. No significant difference of the AUCs was found among these models in both the primary and validation cohorts (all p > 0.05). Conclusions: The LDCT-based quantitative-semantic score and radiomic signature, with good predictive performance, can be preoperative and non-invasive biomarkers for assessing the invasive risk of pGGNs in lung cancer screening. Advances in knowledge: The LDCT-based quantitative-semantic score and radiomic signature, with the equivalent performance to the radiomic model of standard-dose CT, can be preoperative predictors for assessing the invasiveness of pGGNs in lung cancer screening and reducing excess examination and treatment.

European lung cancer screening: valuable trial evidence for optimal practice implementation

Lung cancer screening (LCS) by low-dose computed tomography is a strategy for secondary prevention of lung cancer. In the last two decades, LCS trials showed several options to practice secondary prevention in association with primary prevention, however, the translation from trial to practice is everything but simple. In 2020, the European Society of Radiology and European Respiratory Society published their joint statement paper on LCS. This commentary aims to provide the readership with detailed description about hurdles and potential solutions that could be encountered in the practice of LCS.

Blood-Based Biomarker Panel for Personalized Lung Cancer Risk Assessment

PURPOSE To investigate whether a panel of circulating protein biomarkers would improve risk assessment for lung cancer screening in combination with a risk model on the basis of participant characteristics. METHODS A blinded validation study was performed using prostate lung colorectal ovarian (PLCO) Cancer Screening Trial data and biospecimens to evaluate the performance of a four-marker protein panel (4MP) consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in combination with a lung cancer risk prediction model (PLCOm2012) compared with current US Preventive Services Task Force (USPSTF) screening criteria. The 4MP was assayed in 1,299 sera collected preceding lung cancer diagnosis and 8,709 noncase sera. RESULTS The 4MP alone yielded an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77 to 0.82) for case sera collected within 1-year preceding diagnosis and 0.74 (95% CI, 0.72 to 0.76) among the entire specimen set. The combined 4MP + PLCOm2012 model yielded an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.82 to 0.88) for case sera collected within 1 year preceding diagnosis. The benefit of the 4MP in the combined model resulted from improvement in sensitivity at high specificity. Compared with the USPSTF2021 criteria, the combined 4MP + PLCOm2012 model exhibited statistically significant improvements in sensitivity and specificity. Among PLCO participants with ≥ 10 smoking pack-years, the 4MP + PLCOm2012 model would have identified for annual screening 9.2% more lung cancer cases and would have reduced referral by 13.7% among noncases compared with USPSTF2021 criteria. CONCLUSION A blood-based biomarker panel in combination with PLCOm2012 significantly improves lung cancer risk assessment for lung cancer screening.