Development and validation of a lung cancer risk prediction model for African-Americans

Symbiotic microbial communities in various locations of the lung cancer respiratory tract along with potential host immunological processes affected

Lung cancer has the highest mortality rate among all cancers worldwide. The 5-year overall survival rate for non-small cell lung cancer (NSCLC) is estimated at around 26%, whereas for small cell lung cancer (SCLC), the survival rate is only approximately 7%. This disease places a significant financial and psychological burden on individuals worldwide. The symbiotic microbiota in the human body has been significantly associated with the occurrence, progression, and prognosis of various diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis. Studies have demonstrated that respiratory symbiotic microorganisms and their metabolites play a crucial role in modulating immune function and contributing to the pathophysiology of lung cancer through their interactions with the host. In this review, we provide a comprehensive overview of the microbial characteristics associated with lung cancer, with a focus on the respiratory tract microbiota from different locations, including saliva, sputum, bronchoalveolar lavage fluid (BALF), bronchial brush samples, and tissue. We describe the respiratory tract microbiota's biodiversity characteristics by anatomical region, elucidating distinct pathological features, staging, metastasis, host chromosomal mutations, immune therapies, and the differentiated symbiotic microbiota under the influence of environmental factors. Our exploration investigates the intrinsic mechanisms linking the microbiota and its host. Furthermore, we have also provided a comprehensive review of the immune mechanisms by which microbiota are implicated in the development of lung cancer. Dysbiosis of the respiratory microbiota can promote or inhibit tumor progression through various mechanisms, including DNA damage and genomic instability, activation and regulation of the innate and adaptive immune systems, and stimulation of epithelial cells leading to the upregulation of carcinogenesis-related pathways.

Lack of racial and ethnic diversity in lung cancer cell lines contributes to lung cancer health disparities

Lung cancer is the leading cause of cancer death in the United States and worldwide, and a major source of cancer health disparities. Lung cancer cell lines provide key in vitro models for molecular studies of lung cancer development and progression, and for pre-clinical drug testing. To ensure health equity, it is imperative that cell lines representing different lung cancer histological types, carrying different cancer driver genes, and representing different genders, races, and ethnicities should be available. This is particularly relevant for cell lines from Black men, who experience the highest lung cancer mortality in the United States. Here, we undertook a review of the available lung cancer cell lines and their racial and ethnic origin. We noted a marked imbalance in the availability of cell lines from different races and ethnicities. Cell lines from Black patients were strongly underrepresented, and we identified no cell lines from Hispanic/Latin(x) (H/L), American Indian/American Native (AI/AN), or Native Hawaiian or other Pacific Islander (NHOPI) patients. The majority of cell lines were derived from White and Asian patients. Also missing are cell lines representing the cells-of-origin of the major lung cancer histological types, which can be used to model lung cancer development and to study the effects of environmental exposures on lung tissues. To our knowledge, the few available immortalized alveolar epithelial cell lines are all derived from White subjects, and the race and ethnicity of a handful of cell lines derived from bronchial epithelial cells are unknown. The lack of an appropriately diverse collection of lung cancer cell lines and lung cancer cell-of-origin lines severely limits racially and ethnically inclusive lung cancer research. It impedes the ability to develop inclusive models, screen comprehensively for effective compounds, pre-clinically test new drugs, and optimize precision medicine. It thereby hinders the development of therapies that can increase the survival of minority and underserved patients. The noted lack of cell lines from underrepresented groups should constitute a call to action to establish additional cell lines and ensure adequate representation of all population groups in this critical pre-clinical research resource.

[Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort]

Objective

To identify the risk factors related to lifestyle behaviors that affect the incidence of lung cancer, to build a lung cancer risk prediction model to identify, in the population, individuals who are at high risk, and to facilitate the early detection of lung cancer.

Methods

The data used in the study were obtained from the UK Biobank, a database that contains information collected from 502 389 participants between March 2006 and October 2010. Based on domestic and international guidelines for lung cancer screening and high-quality research literature on lung cancer risk factors, high-risk population identification criteria were determined. Univariate Cox regression was performed to screen for risk factors of lung cancer and a multifactor lung cancer risk prediction model was constructed using Cox proportional hazards regression. Based on the comparison of Akaike information criterion and Schoenfeld residual test results, the optimal fitted model assuming proportional hazards was selected. The multiple factor Cox proportional hazards regression was performed to consider the survival time and the population was randomly divided into a training set and a validation set by a ratio of 7:3. The model was built using the training set and the performance of the model was internally validated using the validation set. The area under the receiver operating characteristic (ROC) curve ( AUC) was used to evaluate the efficacy of the model. The population was categorized into low-risk, moderate-risk, and high-risk groups based on the probability of occurrence of 0% to <25%, 25% to <75%, and 75% to 100%. The respective proportions of affected individuals in each risk group were calculated.

Results

The study eventually covered 453 558 individuals, and out of the cumulative follow-up of 5 505 402 person-years, a total of 2 330 cases of lung cancer were diagnosed. Cox proportional hazards regression was performed to identify 10 independent variables as predictors of lung cancer, including age, body mass index (BMI), education, income, physical activity, smoking status, alcohol consumption frequency, fresh fruit intake, family history of cancer, and tobacco exposure, and a model was established accordingly. Internal validation results showed that 8 independent variables (all the 10 independent variables screened out except for BMI and fresh fruit intake) were significant influencing factors of lung cancer ( P<0.05). The AUC of the training set for predicting lung cancer occurrence at one year, five years, and ten years were 0.825, 0.785, and 0.777, respectively. The AUC of the validation set for predicting lung cancer occurrence at one year, five years, and ten years were 0.857, 0.782, and 0.765, respectively. 68.38% of the individuals who might develop lung cancer in the future could be identified by screening the high-risk population.

Conclusion

We established, in this study, a model for predicting lung cancer risks associated with lifestyle behaviors of a large population. Showing good performance in discriminatory ability, the model can be used as a tool for developing standardized screening strategies for lung cancer.

Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study

BACKGROUND

It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally.

OBJECTIVE

The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women.

METHODS

Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set.

RESULTS

In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination.

CONCLUSIONS

We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.

Characterizing microbiota and metabolomics analysis to identify candidate biomarkers in lung cancer

BACKGROUND

Lung cancer is the leading malignant disease and cause of cancer-related death worldwide. Most patients with lung cancer had insignificant early symptoms so that most of them were diagnosed at an advanced stage. In addition to factors such as smoking, pollution, lung microbiome and its metabolites play vital roles in the development of lung cancer. However, the interaction between lung microbiota and carcinogenesis is lack of systematically characterized and controversial. Therefore, the purpose of this study was to excavate the features of the lung microbiota and metabolites in patients and verify potential biomarkers for lung cancer diagnosis.

METHODS

Lung tissue flushing solutions and bronchoalveolar lavage fluid samples came from patients with lung cancer and non-lung cancer. The composition and variations of the microbiota and metabolites in samples were explored using muti-omics technologies including 16S rRNA amplicon sequencing, metagenomics and metabolomics.

RESULTS

The metabolomics analysis indicated that 40 different metabolites, such as 9,10-DHOME, sphingosine, and cysteinyl-valine, were statistically significant between two groups (VIP > 1 and P < 0.05). These metabolites were significantly enriched into 11 signal pathways including sphingolipid, autophagy and apoptosis signaling pathway (P < 0.05). The analysis of lung microbiota showed that significant changes reflected the decrease of microbial diversity, changes of distribution of microbial taxa, and variability of the correlation networks of lung microbiota in lung cancer patients. In particular, we found that oral commensal microbiota and multiple probiotics might be connected with the occurrence and progression of lung cancer. Moreover, our study found 3 metabolites and 9 species with significantly differences, which might be regarded as the potential clinical diagnostic markers associated with lung cancer.

CONCLUSIONS

Lung microbiota and metabolites might play important roles in the pathogenesis of lung cancer, and the altered metabolites and microbiota might have the potential to be clinical diagnostic markers and therapeutic targets associated with lung cancer.

Analysis of Eligibility for Lung Cancer Screening by Race After 2021 Changes to US Preventive Services Task Force Screening Guidelines

IMPORTANCE

Lung cancer incidence and mortality have disproportionate consequences for racial and ethnic minority populations. The extent to which the 2021 changes to the US Preventive Services Task Force (USPSTF) screening guidelines have reduced the racial disparity gap in lung cancer screening eligibility is not known.

OBJECTIVE

To assess the consequences of the changes in USPSTF low-dose computed tomography eligibility criteria for lung cancer screening between 2013 and 2021 among Black and White community-dwelling adults.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study analyzed data from the Reasons for Geographic and Racial Differences in Stroke study, a prospective longitudinal cohort study of community-dwelling Black and White adults 45 years and older who were initially recruited across the US between January 2003 and October 2007, with ongoing follow-up. All participants who would have been potentially eligible for lung cancer screening based on the 2021 USPSTF guidelines (N = 14 285) were included. Follow-up data for the current cohort study were collected and analyzed between January 2013 and December 2017, with final analysis performed in 2021.

EXPOSURES

Self-reported Black vs White race.

PRIMARY OUTCOMES AND MEASURES

Differences in the proportion of Black vs White participants eligible for lung cancer screening according to 2013 and 2021 guidelines were assessed using modified Poisson models with robust SEs. Associations between important covariates (demographic characteristics and social factors associated with health), including interaction and dissimilarity indices (2 measures of residential segregation), and differences in screening eligibility were also examined.

RESULTS

Among 14 285 participants (mean [SD] age, 64.7 [7.5] years; 7675 men [53.7%]), 5787 (40.5%) self-identified as Black and 8498 (59.5%) as White. Based on the 2013 USPSTF guidelines, 1109 of 5787 Black participants (19.2%) and 2313 of 8498 White participants (27.2%) were eligible for lung cancer screening (difference, -8.06 percentage points; 95% CI, -9.44 to -6.67 percentage points). Based on the 2021 guidelines, 1667 of 5787 Black participants (28.8%) and 2940 of 8498 White participants (34.6%) were eligible for screening (difference, -5.73 percentage points; 95% CI, -7.28 to -4.19 percentage points). After adjustment for differences in individual characteristics and residential segregation, the 2013 difference in screening eligibility among Black vs White participants was -12.66 percentage points (95% CI, -14.71 to -10.61 percentage points), and the 2021 difference was -12.15 percentage points (95% CI, -14.37 to -9.93 percentage points).

CONCLUSIONS AND RELEVANCE

In this study, 2021 changes to the USPSTF lung cancer screening guidelines were associated with reductions in but not elimination of existing eligibility disparities in lung cancer screening among Black and White adults. These findings suggest that accounting for factors beyond age and pack-years of smoking is needed when tailoring guidelines to improve screening eligibility among groups at high risk of lung cancer.

Low-dose computed tomography lung cancer screening: Clinical evidence and implementation research

Lung cancer causes more deaths than breast, cervical, and colorectal cancer combined. Nevertheless, population-based lung cancer screening is still not considered standard practice in most countries worldwide. Early lung cancer detection leads to better survival outcomes: patients diagnosed with stage 1A lung cancer have a >75% 5-year survival rate, compared to <5% at stage 4. Low-dose computed tomography (LDCT) thorax imaging for the secondary prevention of lung cancer has been studied at length, and has been shown to significantly reduce lung cancer mortality in high-risk populations. The US National Lung Screening Trial reported a 20% overall reduction in lung cancer mortality when comparing LDCT to chest X-ray, and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trial more recently reported a 24% reduction when comparing LDCT to no screening. Hence, the focus has now shifted to implementation research. Consequently, the 4-IN-THE-LUNG-RUN consortium based in five European countries, has set up a large-scale multicenter implementation trial. Successful implementation of and accessibility to LDCT lung cancer screening are dependent on many factors, not limited to population selection, recruitment strategy, computed tomography screening frequency, lung-nodule management, participant compliance, and cost effectiveness. This review provides an overview of current evidence for LDCT lung cancer screening, and draws attention to major factors that need to be addressed to successfully implement standardized, effective, and accessible screening throughout Europe. Evidence shows that through the appropriate use of risk-prediction models and a more personalized approach to screening, efficacy could be improved. Furthermore, extending the screening interval for low-risk individuals to reduce costs and associated harms is a possibility, and through the use of volumetric-based measurement and follow-up, false positive results can be greatly reduced. Finally, smoking cessation programs could be a valuable addition to screening programs and artificial intelligence could offer a solution to the added workload pressures radiologists are facing.

The respiratory microbiome associated with chronic obstructive pulmonary disease comorbidity in non-small cell lung cancer

Background

Research has shown that some microbiomes are linked to cancer. Hence, we hypothesize that alterations in the respiratory microbiome might be associated with lung cancer.

Methods

Through droplet digital polymerase chain reaction analysis, we investigated the abundance of Acidovorax in surgically resected primary tumors and corresponding nontumor lung tissues obtained from 50 Japanese patients with non‐small cell lung cancer.

Results

The rate of positivity for Acidovorax in tumor and nontumor tissues was 44 and 26%, respectively. The abundance of Acidovorax in tumor tissues was significantly higher in patients with nonsquamous cell carcinoma complicated by chronic obstructive pulmonary disease (COPD) and those who relapsed after surgical resection (p < 0.05). In tumor tissues, the results of the univariate and multivariate analyses revealed that only COPD exerted a direct effect on the abundance of Acidovorax (p < 0.05). Furthermore, the presence of Acidovorax was high in lung cancer patients with COPD comorbidity (65%) and TP53 gene mutation; only one of the nontumor tissues was positive for Acidovorax. In patients with lung cancer complicated by COPD, Acidovorax tended to be present in both the tumor and nontumor areas.

Conclusions

This study identified novel microbiota involved in lung cancer with COPD comorbidity. The results suggested that Acidovorax may be a useful biomarker in the screening for lung cancer. Further studies are warranted to validate the clinical significance of the microbiome in a larger independent patient cohort.

Computed Tomography Features of Lung Structure Have Utility for Differentiating Malignant and Benign Pulmonary Nodules

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a known comorbidity for lung cancer independent of smoking history. Quantitative computed tomography (qCT) imaging features related to COPD have shown promise in the assessment of lung cancer risk. We hypothesize that qCT features from the lung, lobe, and airway tree related to the location of the pulmonary nodule can be used to provide informative malignancy risk assessment.

METHODS

A total of 183 qCT features were extracted from 278 individuals with a solitary pulmonary nodule of known diagnosis (71 malignant, 207 benign). These included histogram and airway characteristics of the lungs, lobe, and segmental paths. Performances of the least absolute shrinkage and selection operator (LASSO) regression analysis and an ensemble of neural networks (ENN) were compared for feature set selection and classification on a testing cohort of 49 additional individuals (15 malignant, 34 benign).

RESULTS

The LASSO and ENN methods produced different feature sets for classification with LASSO selecting fewer qCT features (7) than the ENN (17). The LASSO model with the highest performing training area under the curve (AUC) (0.80) incorporated automatically extracted features and reader-measured nodule diameter with a testing AUC of 0.62. The ENN model with the highest performing AUC (0.77) also incorporated qCT and reader diameter but maintained higher testing performance AUC (0.79).

CONCLUSIONS

Automatically extracted qCT imaging features of the lung can be informative of the differentiation between individuals with malignant pulmonary nodules and those with benign pulmonary nodules, without requiring nodule segmentation and analysis.

Smoking Quit Rates Among Menthol vs Non-Menthol Smokers: Implications Regarding a US Ban on the Sale of Menthol Cigarettes

BACKGROUND

A ban on the sale of menthol cigarettes in the United States is currently under consideration. A justification is that menthol cigarettes are harder to quit, particularly for African American smokers who use menthols much more frequently than White smokers, but epidemiologic data are limited.

METHODS

In a cohort of 16,425 mostly low income African American and White current cigarette smokers enrolled during 2002-2009, we computed smoking quit and re-uptake rates at three follow ups conducted means of 4.6, 7.7 and 11 years after entry. Generalized estimation equations were used to compute odds ratios (OR) and 95% confidence intervals (CI) for quitting and resuming smoking for menthol vs non-menthol smokers adjusted for race, age, education, income, and smoking pack years.

RESULTS

Crude annual quit rates among current smokers were 4.3% for menthol and 4.5% for non-menthol smokers, with adjusted ORs of quitting for menthol vs non-menthol smokers of 1.01 (95% CI = 0.91-1.11) overall, 0.99 (95% CI = 0.87-1.12) among African American and 1.02 (95% CI = 0.88-1.20) among White smokers. Crude annual smoking re-uptake rates were somewhat higher among menthol (8.4%) than non-menthol smokers (7.1%), with an adjusted OR of 1.19 (95% CI = 0.97-1.47), but net quit rates remained similar (OR = 1.01 [95% CI = 0.90-1.13] overall; OR = 1.00 [95% CI = 0.86-1.15] among African American participants; and OR = 1.04 [95% CI = 0.87-1.24] among White participants).

CONCLUSIONS

This large-scale prospective survey revealed similar quit rates among menthol and non-menthol smokers. Results contribute to policy discussions, especially if, as a meta analysis suggests, lung cancer risk is higher for non-menthol smokers and a ban leads menthol smokers to switch to non-menthol cigarettes.

Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China

Background

About 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China.

Methods

A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set.

Results

A total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk.

Conclusions

We developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.

A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China

OBJECTIVE

Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer.

MATERIALS AND METHODS

Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively.

RESULTS

By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups.

CONCLUSIONS

We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.

Lung cancer screening: who pays? Who receives? The European perspectives

Lung cancer is the leading cause of cancer-related death worldwide, and its early detection is critical to achieving a curative treatment and to reducing mortality. Low-dose computed tomography (LDCT) is a highly sensitive technique for detecting noninvasive small lung tumors in high-risk populations. We here analyze the current status of lung cancer screening (LCS) from a European point of view. With economic burden of health care in most European countries resting on the state, it is important to reduce costs of screening and improve its effectiveness. Current cost-effectiveness analyses on LCS have indicated a favorable economic profile. The most recently published analysis reported an incremental cost-effectiveness ratio (ICER) of €3,297 per 1 life-year gained adjusted for the quality of life (QALY) and €2,944 per life-year gained, demonstrating a 90% probability of ICER being below €15,000 and a 98.1% probability of being below €25,000. Different risk models have been used to identify the target population; among these, the PLCOM2012 in particular allows for the selection of the population to be screened with high sensitivity. Risk models should also be employed to define screening intervals, which can reduce the general number of LDCT scans after the baseline round. Future perspectives of screening in a European scenario are related to the will of the policy makers to implement policy on a large scale and to improve the effectiveness of a broad screening of smoking-related disease, including cardiovascular prevention, by measuring coronary calcium score on LDCT. The employment of artificial intelligence (AI) in imaging interpretation, the use of liquid biopsies for the characterization of CT-detected undetermined nodules, and less invasive, personalized surgical treatments, will improve the effectiveness of LCS.

Racial disparities in intensity of smoke exposure and nicotine intake among low-dependence smokers

BACKGROUND

Compared to white smokers, Black smokers are at disproportionately higher risk for smoking-related disease, despite consuming fewer cigarettes per day (CPD). To examine racial disparities in biobehavioral influences on smoking and disease risk, we analyzed the relationship between self-reported tobacco dependence and intensity of tobacco smoke exposure per cigarette, on the one hand, and intensity of nicotine intake per cigarette, on the other.

METHODS

In 270 Black and 516 white smokers, smoke exposure was measured by expired carbon monoxide (CO), and nicotine intake was measured by plasma cotinine (COT) and cotinine+3'-hydroxycotinine ([COT + 3HC]). Using linear regression analyses, we analyzed how the Fagerström Test for Cigarette Dependence (FTCD) predicted intensity of smoke exposure per cigarette (CO/CPD) and intensity of nicotine intake per cigarette (COT/CPD; [COT + 3HC]/CPD), and how race moderated these relations.

RESULTS

Overall, Black smokers consumed fewer CPD than white smokers and had higher levels of CO/CPD, COT/CPD, and [COT + 3HC]/CPD. These elevations were most pronounced at lower levels of dependence: amongst Black smokers, FTCD negatively predicted intensity of smoke exposure as measured by CO/CPD (B = -0.12, 95% CI = -0.18, -0.05, p = 0.0003) and intensity of nicotine intake as measured by [COT + 3HC]/CPD (B = -1.31, 95% CI = -2.15, -0.46, p = 0.002).

CONCLUSIONS

Low-dependence Black smokers had higher intensities of both smoke exposure and nicotine intake per cigarette compared to similarly dependent white smokers, suggesting that measures of dependence, exposure, and intake underestimate incremental risk of each cigarette to Black smokers.

A 10-Year Probability Deep Neural Network Prediction Model for Lung Cancer

Cancer is the leading cause of death in Taiwan. According to the Cancer Registration Report of Taiwan's Ministry of Health and Welfare, a total of 13,488 people suffered from lung cancer in 2016, making it the second-most common cancer and the leading cancer in men. Compared with other types of cancer, the incidence of lung cancer is high. In this study, the National Health Insurance Research Database (NHIRDB) was used to determine the diseases and symptoms associated with lung cancer, and a 10-year probability deep neural network prediction model for lung cancer was developed. The proposed model could allow patients with a high risk of lung cancer to receive an earlier diagnosis and support the physicians' clinical decision-making. The study was designed as a cohort study. The subjects were patients who were diagnosed with lung cancer between 2000 and 2009, and the patients' disease histories were back-tracked for a period, extending to ten years before the diagnosis of lung cancer. As a result, a total of 13 diseases were selected as the predicting factors. A nine layers deep neural network model was created to predict the probability of lung cancer, depending on the different pre-diagnosed diseases, and to benefit the earlier detection of lung cancer in potential patients. The model is trained 1000 times, the batch size is set to 100, the SGD (Stochastic gradient descent) optimizer is used, the learning rate is set to 0.1, and the momentum is set to 0.1. The proposed model showed an accuracy of 85.4%, a sensitivity of 72.4% and a specificity of 85%, as well as an 87.4% area under ROC (AUROC) (95%, 0.8604-0.8885) model precision. Based on data analysis and deep learning, our prediction model discovered some features that had not been previously identified by clinical knowledge. This study tracks a decade of clinical diagnostic records to identify possible symptoms and comorbidities of lung cancer, allows early prediction of the disease, and assists more patients with early diagnosis.

Lung Screening Benefits and Challenges: A Review of The Data and Outline for Implementation

Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for almost a fifth of all cancer-related deaths. Annual computed tomographic lung cancer screening (CTLS) detects lung cancer at earlier stages and reduces lung cancer-related mortality among high-risk individuals. Many medical organizations, including the U.S. Preventive Services Task Force, recommend annual CTLS in high-risk populations. However, fewer than 5% of individuals worldwide at high risk for lung cancer have undergone screening. In large part, this is owing to delayed implementation of CTLS in many countries throughout the world. Factors contributing to low uptake in countries with longstanding CTLS endorsement, such as the United States, include lack of patient and clinician awareness of current recommendations in favor of CTLS and clinician concerns about CTLS-related radiation exposure, false-positive results, overdiagnosis, and cost. This review of the literature serves to address these concerns by evaluating the potential risks and benefits of CTLS. Review of key components of a lung screening program, along with an updated shared decision aid, provides guidance for program development and optimization. Review of studies evaluating the population considered "high-risk" is included as this may affect future guidelines within the United States and other countries considering lung screening implementation.

Evaluating the predictive performance of malaria antibodies and FCGR3B gene polymorphisms on Plasmodium falciparum infection outcome: a prospective cohort study

Background

Malaria antigen-specific antibodies and polymorphisms in host receptors involved in antibody functionality have been associated with different outcomes of Plasmodium falciparum infections. Thus, to identify key prospective malaria antigens for vaccine development, there is the need to evaluate the associations between malaria antibodies and antibody dependent host factors with more rigorous statistical methods. In this study, different statistical models were used to evaluate the predictive performance of malaria-specific antibodies and host gene polymorphisms on P. falciparum infection in a longitudinal cohort study involving Ghanaian children.

Methods

Models with different functional forms were built using known predictors (age, sickle cell status, blood group status, parasite density, and mosquito bed net use) and malaria antigen-specific immunoglobulin (Ig) G and IgG subclasses and FCGR3B polymorphisms shown to mediate antibody-dependent cellular functions. Malaria antigens studied were Merozoite surface proteins (MSP-1 and MSP-3), Glutamate Rich Protein (GLURP)-R0, R2, and the Apical Membrane Antigen (AMA-1). The models were evaluated through visualization and assessment of differences between the Area Under the Receiver Operating Characteristic Curve and Brier Score estimated by suitable internal cross-validation designs.

Results

This study found that the FCGR3B-c.233C>A genotype and IgG against AMA1 were relatively better compared to the other antibodies and FCGR3B genotypes studied in classifying or predicting malaria risk among children.

Conclusions

The data supports the P. falciparum, AMA1 as an important malaria vaccine antigen, while FCGR3B-c.233C>A under the additive and dominant models of inheritance could be an important modifier of the effect of malaria protective antibodies.

Genetic Variant in CHRNA5 and Response to Varenicline and Combination Nicotine Replacement in a Randomized Placebo-Controlled Trial

It is unclear if genetic variants affect smoking cessation treatment response. This study tested whether variants in the cholinergic receptor nicotinic alpha 5 subunit (CHRNA5) predict response to smoking cessation medication by directly comparing the two most effective smoking cessation pharmacotherapies. In this genotype-stratified randomized, double-blind, placebo-controlled clinical trial (May 2015-August 2019 in St Louis, Missouri), smokers were randomized by genotype in blocks of six (1:1:1 ratio) to three conditions: 12 weeks of placebo (n = 273), combination nicotine patch and lozenge (combination nicotine replacement therapy, cNRT, n = 275), or varenicline (n = 274). All participants received counseling and were followed for 12 months. The primary end point was biochemically verified 7-day point prevalence abstinence at the end of treatment (EOT, week 12). Trial registration and eligibility criteria are on clinicaltrials.gov (https://clinicaltrials.gov/) (NCT02351167). We conducted the genetic analyses separately for 516 European ancestry (EA) smokers and 306 non-EA smokers (including 270 African American smokers). In African American smokers, there was a genotype-by-treatment interaction for EOT abstinence (χ²  = 10.7, degrees of freedom = 2. P = 0.0049): specifically, cNRT was more effective in smokers with rs16969968 GG genotype than was placebo, while varenicline was more effective in smokers of GA/AA genotypes. In EA ancestry smokers, there was no significant genotype-by-treatment interaction. In the whole sample, although both were effective at EOT, only varenicline, and not cNRT, was significantly effective relative to placebo at 6-month follow-up. Importantly, this study suggests that genetic information can further enhance smoking cessation treatment effectiveness.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.

Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population

BACKGROUND

Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population.

METHODS

During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with P_entry  = .15 and P_stay  = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability.

RESULTS

A total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (P_HL  = .689) and all subgroups.

CONCLUSIONS

We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.

Accounting for established predictors with the multistep elastic net

Multivariable models for prediction or estimating associations with an outcome are rarely built in isolation. Instead, they are based upon a mixture of covariates that have been evaluated in earlier studies (eg, age, sex, or common biomarkers) and covariates that were collected specifically for the current study (eg, a panel of novel biomarkers or other hypothesized risk factors). For that context, we present the multistep elastic net (MSN), which considers penalized regression with variables that can be qualitatively grouped based upon their degree of prior research support: established predictors vs unestablished predictors. The MSN chooses between uniform penalization of all predictors (the standard elastic net) and weaker penalization of the established predictors in a cross-validated framework and includes the option to impose zero penalty on the established predictors. In simulation studies that reflect the motivating context, we show the comparability or superiority of the MSN over the standard elastic net, the Integrative LASSO with Penalty Factors, the sparse group lasso, and the group lasso, and we investigate the importance of not penalizing the established predictors at all. We demonstrate the MSN to update a prediction model for pediatric ECMO patient mortality.

Will That Pulmonary Nodule Become Cancerous? A Risk Prediction Model for Incident Lung Cancer

This prospective investigation derived a prediction model for identifying risk of incident lung cancer among patients with visible lung nodules identified on computed tomography (CT). Among 2,924 eligible patients referred for evaluation of a pulmonary nodule to the Stony Brook Lung Cancer Evaluation Center between January 1, 2002 and December 31, 2015, 171 developed incident lung cancer during the observation period. Cox proportional hazard models were used to model time until disease onset. The sample was randomly divided into discovery (n = 1,469) and replication (n = 1,455) samples. In the replication sample, concordance was computed to indicate predictive accuracy and risk scores were calculated using the linear predictions. Youden index was used to identify high-risk versus low-risk patients and cumulative lung cancer incidence was examined for high-risk and low-risk groups. Multivariable analyses identified a combination of clinical and radiologic predictors for incident lung cancer including ln-age, ln-pack-years smoking, a history of cancer, chronic obstructive pulmonary disease, and several radiologic markers including spiculation, ground glass opacity, and nodule size. The final model reliably detected patients who developed lung cancer in the replication sample (C = 0.86, sensitivity/specificity = 0.73/0.81). Cumulative incidence of lung cancer was elevated in high-risk versus low-risk groups [HR = 14.34; 95% confidence interval (CI), 8.17-25.18]. Quantification of reliable risk scores has high clinical utility, enabling physicians to better stratify treatment protocols to manage patient care. The final model is among the first tools developed to predict incident lung cancer in patients presenting with a concerning pulmonary nodule.

CT screening for lung cancer: Are we ready to implement in Europe?

Lung cancer screening with low-dose CT (LDCT) is already available in certain parts of the world, such as the United States, but not yet in Europe. The recently published European position statement on lung cancer screening has recommended planning for implementation of screening to start within 18-months [1]. Pilot European programmes are already underway, primarily in the United Kingdom (UK), delivering lung cancer screening to their local populations. This review article acknowledges the evidence base for LDCT screening and will discuss the challenges that still need to be overcome in an attempt to answer the question: are we ready to implement in Europe?

Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine

BACKGROUND

Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate.

OBJECTIVE

The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population.

METHODS

Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018.

RESULTS

The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer.

CONCLUSIONS

We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.

Risk prediction models for lung cancer: Perspectives and dissemination

Objective

The objective was to systematically assess lung cancer risk prediction models by critical evaluation of methodology, transparency and validation in order to provide a direction for future model development.

Methods

Electronic searches (including PubMed, EMbase, the Cochrane Library, Web of Science, the China National Knowledge Infrastructure, Wanfang, the Chinese BioMedical Literature Database, and other official cancer websites) were completed with English and Chinese databases until April 30th, 2018. Main reported sources were input data, assumptions and sensitivity analysis. Model validation was based on statements in the publications regarding internal validation, external validation and/or cross-validation.

Results

Twenty-two studies (containing 11 multiple-use and 11 single-use models) were included. Original models were developed between 2003 and 2016. Most of these were from the United States. Multivariate logistic regression was widely used to identify a model. The minimum area under the curve for each model was 0.57 and the largest was 0.87. The smallest C statistic was 0.59 and the largest 0.85. Six studies were validated by external validation and three were cross-validated. In total, 2 models had a high risk of bias, 6 models reported the most used variables were age and smoking duration, and 5 models included family history of lung cancer.

Conclusions

The prediction accuracy of the models was high overall, indicating that it is feasible to use models for high-risk population prediction. However, the process of model development and reporting is not optimal with a high risk of bias. This risk affects prediction accuracy, influencing the promotion and further development of the model. In view of this, model developers need to be more attentive to bias risk control and validity verification in the development of models.

Racial disparities in eligibility for low-dose computed tomography lung cancer screening among older adults with a history of smoking

PURPOSE

Lung cancer early detection screening has been demonstrated to decrease lung cancer mortality among high-risk smokers. This study aimed to examine whether current screening guidelines may disproportionately exclude African American smokers who are at higher overall risk for lung cancer.

METHODS

Data from the 2014 Health and Retirement Study were analyzed. Older African Americans and Whites with a history of smoking were included in the analyses (n = 7,348). Eligibility criteria established by the U.S. Preventive Services Task Force (USPSTF) for LDCT lung cancer screening were used. Multivariate logistic regression analyses were conducted to examine racial differences in eligibility for LDCT lung cancer screening.

RESULTS

Overall, 21.1% of current and 10.5% of former smokers met USPSTF's eligibility criteria for LDCT screening. In multivariate logistic regression analyses, African American smokers were less likely to be eligible for LDCT lung cancer screening compared to Whites (odds ratio = 0.5; p < 0.001).

CONCLUSION

African American smokers were less likely to meet established lung cancer screening eligibility criteria compared to Whites. Current lung cancer screening criteria may not adequately capture African Americans at risk and may widen the health disparities in African Americans. Further longitudinal studies are needed to evaluate the efficacy of current lung cancer screening guideline.

Lung tissue microbial profile in lung cancer is distinct from emphysema

OBJECTIVES

The composition and structure of site-specific microbiota have been investigated as potential biomarkers for a variety of chronic inflammatory diseases and cancers. While many studies have focused on the changes in the airway microbiota using respiratory specimens from patients with various respiratory diseases, more research is needed to explore the microbial profiles within the distal lung parenchyma in smokers with lung cancer and/or emphysema.

MATERIALS AND METHODS

To describe and contrast lung tissue-associated microbial signatures in smokers with lung cancer and/or emphysema, we employed culture-independent pyrosequencing of 16S rRNA gene hypervariable V4 region and compositional analysis in non-malignant lung tissue samples obtained from 40 heavy smokers, including 10 emphysema-only, 11 lung cancer-only, and 19 with both lung cancer and emphysema.

RESULTS AND CONCLUSION

The emphysema-only group presented a lower bacterial community evenness defined by a significantly lower Shannon diversity index compared to the lung cancer patients with or without emphysema (P = 0.006). Furthermore, community compositions of lung cancer patients with or without emphysema were characterized by a significantly lower abundance of Proteobacteria (primary the genera Acinetobacter and Acidovorax) and higher prevalence of Firmicutes (Streptococcus) and Bacteroidetes (Prevotella), compared to emphysema-only patients. In conclusion, the lung microbial composition and communities structures of smokers with lung cancer are distinct from the emphysema-only patients. Although preliminary, our findings suggest that lung microbiome changes could be a biomarker of lung cancer that could eventually be used to help screening for the disease.

Influence of Demographic Determinants on the Number of Deaths Caused by Circulatory System Diseases in Comparison to the Number of Deaths Caused by Neoplasms in Slovak Regions from 1996-2014

OBJECTIVES

The objective of our study was to evaluate the influence of available demographic determinants on the number of deaths caused by circulatory system diseases as compared to deaths caused by neoplasms in Slovakia in 1996-2014.

METHODS

Mortality data were kindly provided by the National Health Information Centre in Slovakia. The first method was trend curve fitting of death ratios caused by circulatory system diseases (Chapter IX) and of deaths caused by neoplasms (Chapter II) as a function of age for both sexes. The second method comprised a decision tree for classification between deaths caused by Chapter IX and Chapter II diseases. Input variables were available demographic indicators: age, sex, marital status, region, and calendar year of death. Statistical data analyses were performed by IBM SPSS version 19 statistical software.

RESULTS

We found that the odds ratios of deaths caused by circulatory system diseases (Chapter IX) in comparison with deaths caused by neoplasms (Chapter II) were non-decreasing. At first, the values of odds ratios are constant until they reach a critical sex-dependent value with a subsequent steady increase. In the case of men the odds ratio was greater than in the 60 years age-group where the odds ratio value increased slowly (from 1.14 at age 60 to 7.25 at age 90 years). The relative increase was 6.36 (7.25/1.14). The odds ratio in the women group was smaller but increased more rapidly (from 0.81 at age 60 to 12.27 at age 90 years). The relative increase was 15.15 in women (12.27/0.81). Hence, the odds ratio of death caused by Chapter IX diseases vs. Chapter II was greater in the older women group (i.e. higher age values). Utilizing the decision tree model, we have found that the most significant demographic determinant of death counts in both ICD Chapters was the age of the deceased, followed by marital status and finally gender. The last two predictors (year and region) were relatively negligible though formally significant.

CONCLUSIONS

The proposed method could be useful for prognostic classification of patients and primarily beneficial for hospitals in human or financial resources planning.

Associations between history of chronic lung disease and non-small cell lung carcinoma in Maryland: variations by sex and race

PURPOSE

Lung cancer is a multifactorial malignancy for which some risk factors, such as chronic lung diseases, their interactions with smoking, and how they differ by race and sex, are not fully understood. We investigated the associations between chronic inflammatory lung disease and non-small cell lung carcinoma (NSCLC) and how sex and race may affect such associations.

METHODS

Using logistic regression, we analyzed 1660 lung cancer cases and 1959 population controls and estimated adjusted odds ratios (AORs) and 95% confidence intervals (CIs).

RESULTS

Chronic lung disease was significantly associated with higher odds of having NSCLC in never (AOR = 1.99; 95% CI = 1.19-3.34), former (AOR = 1.68; 95% CI = 1.29-2.20), and current smokers (AOR = 2.40; 95% CI = 1.62-3.57), after adjustment for relevant covariates. For each 5-year increment in chronic lung disease duration, the risk of lung cancer increased only among females (AOR = 1.07; 95% CI = 1.02-1.13). Females, but not males, with asthma were at risk for NSCLC (AOR = 2.08; 95% CI = 1.40-3.10).

CONCLUSIONS

This study provides support for chronic lung inflammation as a potential contributing factor to lung cancer risk and possible sex difference in the inflammatory events underlying disease mechanisms.

Lung cancer risk to personalise annual and biennial follow-up computed tomography screening

BACKGROUND

All lung cancer CT screening trials used fixed follow-up intervals, which may not be optimal. We developed new lung cancer risk models for personalising screening intervals to 1 year or 2 years, and compared these with existing models.

METHODS

We included participants in the CT arm of the National Lung Screening Trial (2002-2010) who underwent a baseline scan and a first annual follow-up scan and were not diagnosed with lung cancer in the first year. True and false positives and the area under the curve of each model were calculated. Internal validation was performed using bootstrapping.

RESULTS

Data from 24 542 participants were included in the analysis. The accuracy was 0.785, 0.693, 0.697, 0.666 and 0.727 for the polynomial, patient characteristics, diameter, Patz and PanCan models, respectively. Of the 24 542 participants included, 174 (0.71%) were diagnosed with lung cancer between the first and the second annual follow-ups. Using the polynomial model, 2558 (10.4%, 95% CI 10.0% to 10.8%), 7544 (30.7%, 30.2% to 31.3%), 10 947 (44.6%, 44.0% to 45.2%), 16 710 (68.1%, 67.5% to 68.7%) and 20 023 (81.6%, 81.1% to 92.1%) of the 24 368 participants who did not develop lung cancer in the year following the first follow-up screening round could have safely skipped it, at the expense of delayed diagnosis of 0 (0.0%, 0.0% to 2.7%), 8 (4.6%, 2.2% to 9.2%), 17 (9.8%, 6.0% to 15.4%), 44 (25.3%, 19.2% to 32.5%) and 70 (40.2%, 33.0% to 47.9%) of the 174 lung cancers, respectively.

CONCLUSIONS

The polynomial model, using both patient characteristics and baseline scan morphology, was significantly superior in assigning participants to 1-year or 2-year screening intervals. Implementing personalised follow-up intervals would enable hundreds of participants to skip a screening round per lung cancer diagnosis delayed.

Chronic obstructive pulmonary disease among lung cancer-free smokers: The importance of healthy controls

BACKGROUND

The prevalence of chronic obstructive pulmonary disease (COPD) in smokers enrolled as "healthy" controls in studies is 10-50%. The COPD status of ideal smoker populations for lung cancer case-control studies should be checked via spirometry; however, this is often not feasible, because no medical indications exist for asymptomatic smokers to undergo spirometry prior to study enrollment. Therefore, there is an unmet need for robust, cost effective assays for identifying undiagnosed lung disease among asymptomatic smokers. Such assays would help excluding unhealthy smokers from lung cancer case-control studies.

METHODS

We used the cytokinesis-blocked micronucleus (CBMN) assay (a measure of genetic instability) to identify undiagnosed lung disease among asymptomatic smokers. We used a convenience population from an on-going lung cancer case-control study including smokers with lung cancer (n = 454), smoker controls (n = 797), and a self-reported COPD (n = 200) contingent within the smoker controls.

RESULTS

Significant differences for all CBMN endpoints were observed when comparing lung cancer to All controls (which included COPD) and Healthy controls (with no COPD). The risk ratio (RR) was increased in the COPD group vs. Healthy controls for nuclear buds (RR 1.28, 95% confidence interval 1.01-1.62), and marginally increased for micronuclei (RR 1.06, 0.98-1.89) and nucleoplasmic bridges (RR 1.07, 0.97-1.15).

CONCLUSION

These findings highlight the importance of using truly healthy controls in studies geared toward assessment of lung cancer risk. Using genetic instability biomarkers would facilitate the identification of smokers susceptible to tobacco smoke carcinogens and therefore predisposed to either disease.

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

Purpose of review

Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice.

Recent findings

Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening.

Summary

Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study

BACKGROUND

Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation) as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer.

METHODS AND FINDINGS

Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST) participants (1,925 lung cancer cases and 884 lung cancer deaths) and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths). Six-year lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphically) by comparing the agreement between the predicted and the observed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (death), and (3) clinical usefulness (net benefit in decision curve analysis) by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81). The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher sensitivity for all models and a slightly higher specificity for some models. The PLCOm2012, Bach, and Two-Stage Clonal Expansion incidence models had the best overall performance, with AUCs >0.68 in the NLST and >0.77 in the PLCO. These three models had the highest sensitivity and specificity for predicting 6-y lung cancer incidence in the PLCO chest radiography arm, with sensitivities >79.8% and specificities >62.3%. In contrast, the NLST eligibility criteria yielded a sensitivity of 71.4% and a specificity of 62.2%. Limitations of this study include the lack of identification of optimal risk thresholds, as this requires additional information on the long-term benefits (e.g., life-years gained and mortality reduction) and harms (e.g., overdiagnosis) of risk-based screening strategies using these models. In addition, information on some predictor variables included in the risk prediction models was not available.

CONCLUSIONS

Selection of individuals for lung cancer screening using individual risk is superior to selection criteria based on age and pack-years alone. The benefits, harms, and feasibility of implementing lung cancer screening policies based on risk prediction models should be assessed and compared with those of current recommendations.

Evaluating efficacy of current lung cancer screening guidelines

Objective

Current lung cancer screening criteria based primarily on outcomes from the National Lung Screening Trial may not adequately capture all subgroups of the population at risk. We aimed to evaluate the efficacy of lung cancer screening criteria recommended by the United States Preventive Services Task Force, Centers for Medicare and Medicaid Services, and the National Comprehensive Cancer Network in identifying known cases of lung cancer.

Methods

An investigation of the Stony Brook Cancer Center Lung Cancer Evaluation Center's database identified 1207 eligible, biopsy-proven lung cancer cases diagnosed between January 1996 and March 2016. Age at diagnosis, smoking history, and other known risk factors for lung cancer were used to determine the proportion of cases that would have met current United States Preventive Services Task Force, Centers for Medicare and Medicaid Services, and National Comprehensive Cancer Network eligibility requirements for lung cancer screening.

Results

Of the 1046 ever smokers in the study, 40% did not meet the National Lung Screening Trial age requirements, 20% did not have a ≥30 pack year smoking history, and approximately one-third quit smoking >15 years before diagnosis, thus deeming them ineligible for screening. Applying the United States Preventive Services Task Force, Centers for Medicare and Medicaid Services, and National Comprehensive Cancer Network eligibility criteria to the Stony Brook Cancer Center's Lung Cancer Evaluation Center cases, 49.2, 46.3, and 69.8%, respectively, would have met the current lung cancer screening guidelines.

Conclusions

The United States Preventive Services Task Force and Centers for Medicare and Medicaid Services eligibility criteria for lung cancer screening captured less than 50% of lung cancer cases in this investigation. These findings highlight the need to reevaluate the efficacy of current guidelines and may have major public health implications.

Focused Analysis of Exome Sequencing Data for Rare Germline Mutations in Familial and Sporadic Lung Cancer

INTRODUCTION

The association between smoking-induced chronic obstructive pulmonary disease (COPD) and lung cancer (LC) is well documented. Recent genome-wide association studies (GWAS) have identified 28 susceptibility loci for LC, 10 for COPD, 32 for smoking behavior, and 63 for pulmonary function, totaling 107 nonoverlapping loci. Given that common variants have been found to be associated with LC in genome-wide association studies, exome sequencing of these high-priority regions has great potential to identify novel rare causal variants.

METHODS

To search for disease-causing rare germline mutations, we used a variation of the extreme phenotype approach to select 48 patients with sporadic LC who reported histories of heavy smoking-37 of whom also exhibited carefully documented severe COPD (in whom smoking is considered the overwhelming determinant)-and 54 unique familial LC cases from families with at least three first-degree relatives with LC (who are likely enriched for genomic effects).

RESULTS

By focusing on exome profiles of the 107 target loci, we identified two key rare mutations. A heterozygous p.Arg696Cys variant in the coiled-coil domain containing 147 (CCDC147) gene at 10q25.1 was identified in one sporadic and two familial cases. The minor allele frequency (MAF) of this variant in the 1000 Genomes database is 0.0026. The p.Val26Met variant in the dopamine β-hydroxylase (DBH) gene at 9q34.2 was identified in two sporadic cases; the minor allele frequency of this mutation is 0.0034 according to the 1000 Genomes database. We also observed three suggestive rare mutations on 15q25.1: iron-responsive element binding protein neuronal 2 (IREB2); cholinergic receptor, nicotinic, alpha 5 (neuronal) (CHRNA5); and cholinergic receptor, nicotinic, beta 4 (CHRNB4).

CONCLUSIONS

Our results demonstrated highly disruptive risk-conferring CCDC147 and DBH mutations.

Disorder-related risk factors for revision total hip arthroplasty after hip hemiarthroplasty in displaced femoral neck fracture patients: a nationwide population-based cohort study

Background

The choice of primary hip hemiarthroplasty or total hip arthroplasty for displaced femoral neck fracture is still controversial. Revision hip arthroplasty not only increases risk and cost but also could result in worse outcome. Determining the risk factors for revision can help inform medical decision-making and aid in risk stratification of publicly reported outcomes. Therefore, we conducted a nationwide population-based study to identify the disease-related risk factors and construct a risk score nomogram to predict revision surgery.

Methods

Records of all 68,030 femoral neck fracture patients receiving partial hemiarthroplasty (HA) in 2000–2010, with no total hip arthroplasty (THA) or revision HA history, were collected from the National Health Insurance Research Database. Cox proportional hazard regression was used to estimate the risk of revision hip replacement (RHA). The score of each risk factor was the quotient of the regression coefficient of the variable by the regression coefficient for a 10-year increase in age. The predictive accuracy was tested using the area under the receiver operating characteristic curve (AUROC).

Results

The revision risk for hemiarthroplasty increased in male, those with schizophrenia and end-stage renal disease patients had 1.58-, 1.88-, and 1.74-fold revision HA risk (95 % confidence interval (CI) = 1.40–1.78, 1.26–2.79, and 1.29–2.34, respectively). In a predictive model, the cumulative risk score ranged from 0 to 13 with a 5.08 to 91.82 % 10-year predicted RHA risk. The percentage of AUROC for 10-year RHA risk in nomogram was 61.9 (95 % CI = 60.0–63.4).

Conclusions

Males, schizophrenia and end-stage renal disease patients have higher risk of revision surgery after hemiarthroplasty for femoral neck fracture.

Why We Must Continue to Investigate Menthol's Role in the African American Smoking Paradox

BACKGROUND

The disproportionate burden of tobacco use among African Americans is largely unexplained. The unexplained disparities, referred to as the African American smoking paradox, includes several phenomena. Despite their social disadvantage, African American youth have lower smoking prevalence rates, initiate smoking at older ages, and during adulthood, smoking rates are comparable to whites. Smoking frequency and intensity among African American youth and adults are lower compared to whites and American Indian and Alaska Natives, but tobacco-caused morbidity and mortality rates are disproportionately higher. Disease prediction models have not explained disease causal pathways in African Americans. It has been hypothesized that menthol cigarette smoking, which is disproportionately high among African Americans, may help to explain several components of the African American smoking paradox.

PURPOSE

This article provides an overview of the potential role that menthol plays in the African American smoking paradox. We also discuss the research needed to better understand this unresolved puzzle.

METHODS

We examined prior synthesis reports and reviewed the literature in PubMed on the menthol compound and menthol cigarette smoking in African Americans.

RESULTS

The pharmacological and physiological effects of menthol and their interaction with biological and genetic factors may indirectly contribute to the disproportionate burden of cigarette use and diseases among African Americans.

CONCLUSIONS

Future studies that examine taste sensitivity, the menthol compound, and their effects on smoking and chronic disease would provide valuable information on how to reduce the tobacco burden among African Americans.

IMPLICATIONS

Our study highlights four counterintuitive observations related to the smoking risk profiles and chronic disease outcomes among African Americans. The extant literature provides strong evidence of their existence and shows that long-standing paradoxes have been largely unaffected by changes in the social environment. African Americans smoke menthols disproportionately, and menthol's role in the African American smoking paradox has not been thoroughly explored. We propose discrete hypotheses that will help to explain the phenomena and encourage researchers to empirically test menthol's role in smoking initiation, transitions to regular smoking and chronic disease outcomes in African Americans.

Gene by Environment Investigation of Incident Lung Cancer Risk in African-Americans

BACKGROUND

Genome-wide association studies have identified polymorphisms linked to both smoking exposure and risk of lung cancer. The degree to which lung cancer risk is driven by increased smoking, genetics, or gene-environment interactions is not well understood.

METHODS

We analyzed associations between 28 single nucleotide polymorphisms (SNPs) previously associated with smoking quantity and lung cancer in 7156 African-American females in the Women's Health Initiative (WHI), then analyzed main effects of top nominally significant SNPs and interactions between SNPs, cigarettes per day (CPD) and pack-years for lung cancer in an independent, multi-center case-control study of African-American females and males (1078 lung cancer cases and 822 controls).

FINDINGS

Nine nominally significant SNPs for CPD in WHI were associated with incident lung cancer (corrected p-values from 0.027 to 6.09 × 10(-5)). CPD was found to be a nominally significant effect modifier between SNP and lung cancer for six SNPs, including CHRNA5 rs2036527[A](betaSNP*CPD = - 0.017, p = 0.0061, corrected p = 0.054), which was associated with CPD in a previous genome-wide meta-analysis of African-Americans.

INTERPRETATION

These results suggest that chromosome 15q25.1 variants are robustly associated with CPD and lung cancer in African-Americans and that the allelic dose effect of these polymorphisms on lung cancer risk is most pronounced in lighter smokers.

Epidemiology of Lung Cancer

Lung cancer continues to be one of the most common causes of cancer death despite understanding the major cause of the disease: cigarette smoking. Smoking increases lung cancer risk 5- to 10-fold with a clear dose-response relationship. Exposure to environmental tobacco smoke among nonsmokers increases lung cancer risk about 20%. Risks for marijuana and hookah use, and the new e-cigarettes, are yet to be consistently defined and will be important areas for continued research as use of these products increases. Other known environmental risk factors include exposures to radon, asbestos, diesel, and ionizing radiation. Host factors have also been associated with lung cancer risk, including family history of lung cancer, history of chronic obstructive pulmonary disease and infections. Studies to identify genes associated with lung cancer susceptibility have consistently identified chromosomal regions on 15q25, 6p21 and 5p15 associated with lung cancer risk. Risk prediction models for lung cancer typically include age, sex, cigarette smoking intensity and/or duration, medical history, and occupational exposures, however there is not yet a risk prediction model currently recommended for general use. As lung cancer screening becomes more widespread, a validated model will be needed to better define risk groups to inform screening guidelines.

Risk Prediction Models for Lung Cancer: A Systematic Review

Many lung cancer risk prediction models have been published but there has been no systematic review or comprehensive assessment of these models to assess how they could be used in screening. We performed a systematic review of lung cancer prediction models and identified 31 articles that related to 25 distinct models, of which 11 considered epidemiological factors only and did not require a clinical input. Another 11 articles focused on models that required a clinical assessment such as a blood test or scan, and 8 articles considered the 2-stage clonal expansion model. More of the epidemiological models had been externally validated than the more recent clinical assessment models. There was varying discrimination, the ability of a model to distinguish between cases and controls, with an area under the curve between 0.57 and 0.879 and calibration, the model's ability to assign an accurate probability to an individual. In our review we found that further validation studies need to be considered; especially for the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial 2012 Model Version (PLCOM2012) and Hoggart models, which recorded the best overall performance. Future studies will need to focus on prediction rules, such as optimal risk thresholds, for models for selective screening trials. Only 3 validation studies considered prediction rules when validating the models and overall the models were validated using varied tests in distinct populations, which made direct comparisons difficult. To improve this, multiple models need to be tested on the same data set with considerations for sensitivity, specificity, model accuracy, and positive predictive values at the optimal risk thresholds.

Screening for lung cancer using low-dose computed tomography: concerns about the application in low-risk individuals

Low-dose computed tomography (LDCT) has been increasingly accepted as an efficient screening method for high-risk individuals to reduce lung cancer mortality. However, there remains a gap of knowledge in the practical implementation of screening on a larger scale, especially for low-risk individuals. The aim of this study is to initiate discussion through an evidence-based analysis and provide valuable suggestions on LDCT screening for lung cancer in clinical practice. Among previously published randomized controlled trials (RCTs), the National Lung Screening Trial (NLST) is the only one demonstrating positive results in a high-risk population of old age and heavy smokers. It is also shown that the potential harms include false-positive findings, radiation exposure etc., but its magnitude is uncertain. In the meantime, the current risk stratification system is inadequate, and is difficult to define selection criteria. Thus, the efficacy of LDCT in lung cancer screening needs to be confirmed in future trials, and the procedure should not be proposed to individuals without comparable risk to those in the NLST. Furthermore, there is a lack of evidence to support the expansion of LDCT screening to low-risk individuals. Therefore, recommendation of LDCT screening for these patients could be premature in clinical practice although some of them might be missed based on current definition of risk factors. Further studies and advances in risk assessment tools are urgently needed to address the concerns about lung cancer screening in order to improve the outcomes of lung cancer.

LLPi: Liverpool Lung Project Risk Prediction Model for Lung Cancer Incidence

Identification of high-risk individuals will facilitate early diagnosis, reduce overall costs, and also improve the current poor survival from lung cancer. The Liverpool Lung Project prospective cohort of 8,760 participants ages 45 to 79 years, recruited between 1998 and 2008, was followed annually through the hospital episode statistics until January 31, 2013. Cox proportional hazards models were used to identify risk predictors of lung cancer incidence. C-statistic was used to assess the discriminatory accuracy of the models. Models were internally validated using the bootstrap method. During mean follow-up of 8.7 years, 237 participants developed lung cancer. Age [hazard ratio (HR), 1.04; 95% confidence interval (CI), 1.02-1.06], male gender (HR, 1.48; 95% CI, 1.10-1.98), smoking duration (HR, 1.04; 95% CI, 1.03-1.05), chronic obstructive pulmonary disease (HR, 2.43; 95% CI, 1.79-3.30), prior diagnosis of malignant tumor (HR, 2.84; 95% CI, 2.08-3.89), and early onset of family history of lung cancer (HR, 1.68; 95% CI, 1.04-2.72) were associated with the incidence of lung cancer. The LLPi risk model had a good calibration (goodness-of-fit χ(2) 7.58, P = 0.371). The apparent C-statistic was 0.852 (95% CI, 0.831-0.873) and the optimism-corrected bootstrap resampling C-statistic was 0.849 (95% CI, 0.829-0.873). The LLPi risk model may assist in identifying individuals at high risk of developing lung cancer in population-based screening programs.

Lung cancer incidence trends by gender, race and histology in the United States, 1973-2010

Background

Lung cancer (LC) incidence in the United States (US) continues to decrease but with significant differences by histology, gender and race. Whereas squamous, large and small cell carcinoma rates have been decreasing since the mid-80s, adenocarcinoma rates remain stable in males and continue to increase in females, with large racial disparities. We analyzed LC incidence trends by histology in the US with an emphasis on gender and racial differences.

Methods

LC incidence rates from 1973–2010 were obtained from the SEER cancer registry. Age-adjusted incidence trends of five major histological types by gender and race were evaluated using joinpoint regression. Trends of LC histology and stage distributions from 2005–2010 were analyzed.

Results

US LC incidence varies by histology. Squamous, large and small cell carcinoma rates continue to decrease for all gender/race combinations, whereas adenocarcinoma rates remain relatively constant in males and increasing in females. An apparent recent increase in the incidence of squamous cell carcinoma and adenocarcinoma since 2005 can be explained by a concomitant decrease in the number of cases classified as other non-small cell carcinoma. Black males continue to be disproportionally affected by squamous LCs, and blacks continue to be diagnosed with more advanced cancers than whites.

Conclusions

LC incidence by histology continues to change over time. Additional variations are expected as screening becomes disseminated. It is important to continue to monitor LC rates to evaluate the impact of screening on current trends, assess the continuing benefits of tobacco control, and focus efforts on reducing racial disparities.