Lung cancer risk prediction: Prostate, Lung, Colorectal And Ovarian Cancer Screening Trial models and validation

Development of a cancer risk model and mobile health application to inform the public about cancer risks and risk factors

OBJECTIVE

To develop and evaluate a mobile health application, the Cancer Risk Calculator (CRC), aimed at improving public health literacy by providing personalized information on cancer risks and preventive measures.

MATERIALS AND METHODS

The CRC was developed through a comprehensive process involving the identification of necessary content, integration of average cancer risks using data from reliable sources, creation of a novel risk model emphasizing modifiable factors, and the application's development for easy access. The application covers 38 cancer types, 18 subtypes, and approximately 790 risk factors, utilizing data from the Surveillance, Epidemiology, and End Results Program and scientific literature.

RESULTS

CRC offers users personalized risk assessments across a broad range of cancers, emphasizing modifiable risk factors to encourage preventive behaviors. It distinguishes itself by covering more cancer types and risk factors than existing tools, with preliminary user feedback indicating its utility in promoting health literacy and lifestyle changes.

DISCUSSION

The CRC application stands out as an innovative tool in health informatics, significantly enhancing public understanding of cancer risks. Its development underscores the potential of digital health technologies to bolster preventive healthcare strategies through improved health literacy.

CONCLUSION

The Cancer Risk Calculator is a pivotal development in mobile health technology, offering comprehensive and personalized insights into cancer risks and prevention. It serves as a valuable resource for public health education, facilitating informed decisions and lifestyle modifications for cancer prevention.

Risk Factors and Preventive Measures for Lung Cancer in the European Union

BACKGROUND

Lung cancer is worldwide one of the most common types of cancer with still very high mortality rates. The aim of this study was to identify and demonstrate correlations between lung cancer mortality rates and potential influencing factors in EU countries.

METHODS

This retrospective study investigated the connections between the mortality rates in the EU countries (n = 28) and potential influencing factors. The significant factors from the correlation analysis were identified using a stepwise multiple regression analysis.

RESULTS

The most important factors for both genders are the incidence of lung cancer, the price of tobacco, and the number of doctors per 100,000 inhabitants.

CONCLUSION

Lung cancer is a significant global health challenge. The study identified potential strategies for reducing the mortality rate from lung cancer. These strategies include an increase in the number of physicians, enhanced accessibility to cutting-edge antineoplastic medications, and state-funded coverage of the associated costs. It would be beneficial for politicians to consider implementing LDCT screening for the early detection of the disease. The implementation of uniform healthcare system optimization across the EU, combined with improvements in socio-economic conditions, has the potential to mitigate the risk of developing lung cancer.

Development of a novel nomogram for predicting delayed methotrexate excretion following high-dose methotrexate in adult patients with hematologic malignancies

PURPOSE

High-dose methotrexate (HDMTX) is integral in treating hematologic malignancies but carries risks of severe toxicities due to prolonged MTX exposure. However, knowledge of delayed MTX excretion is primarily derived from pediatric and adolescent cohorts, with the reported predictors being presented as rough dichotomous values. This study aimed to identify risk factors for delayed MTX excretion exclusively in adult patients with hematologic malignancies and develop a more applicable predictive nomogram based on continuous clinical and laboratory variables.

METHODS

517 HDMTX cycles in 194 patients were retrospectively analyzed. Delayed MTX excretion was defined as either MTX concentration ≥ 1.0 µmol/L at 48 h or ≥ 0.1 µmol/L at 72 h after HDMTX initiation. Multivariate logistic regression analysis was used to construct the nomogram internally validated with the bootstrap method.

RESULTS

Delayed MTX excretion was observed in 24.0% of cycles. Six significant predictors were identified: relapsed/refractory disease (Odds ratio [OR] 2.03), fewer HDMTX cycles (OR 0.771), treatment intent (OR 2.13), lower albumin (OR 0.563) and creatinine clearance levels (OR 0.993), and increased γ-glutamyl transpeptidase levels (OR 1.004, all P < 0.05). These were incorporated into a web-based nomogram as continuous variables with good prediction accuracy (area under the curve, 0.73) and without significant overfitting. Delayed MTX excretion increased risks of developing acute kidney injury, even solely at the 72 h timepoint (OR 2.57, P = 0.025), without providing any benefit of clinical outcomes.

CONCLUSION

This study comprehensively characterized MTX elimination failure following HDMTX in adult patients and could pave the way for individualized risk prediction.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging

Although lung cancer (LC) is one of the most common and lethal tumors, only 15% of patients are diagnosed at an early stage. Smoking is still responsible for more than 85% of cases. Lung cancer screening (LCS) with low-dose CT (LDCT) reduces LC-related mortality by 20%, and that reduction reaches 38% when LCS by LDCT is combined with smoking cessation. In the last decade, a number of countries have adopted population-based LCS as a public health recommendation. Albeit still incipient, discussion on this topic in Brazil is becoming increasingly broad and necessary. With the aim of increasing knowledge and stimulating debate on LCS, the Brazilian Society of Thoracic Surgery, the Brazilian Thoracic Association, and the Brazilian College of Radiology and Diagnostic Imaging convened a panel of experts to prepare recommendations for LCS in Brazil. The recommendations presented here were based on a narrative review of the literature, with an emphasis on large population-based studies, systematic reviews, and the recommendations of international guidelines, and were developed after extensive discussion by the panel of experts. The following topics were reviewed: reasons for screening; general considerations about smoking; epidemiology of LC; eligibility criteria; incidental findings; granulomatous lesions; probabilistic models; minimum requirements for LDCT; volumetric acquisition; risks of screening; minimum structure and role of the multidisciplinary team; practice according to the Lung CT Screening Reporting and Data System; costs versus benefits of screening; and future perspectives for LCS.

Using clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank

BACKGROUND

Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical variables.

METHODS

We used UK Biobank to train the best polygenic risk scores for 8 cancers (bladder, breast, colorectal, kidney, lung, ovarian, pancreatic, and prostate cancers) and select relevant clinical variables from 733 baseline traits through extreme gradient boosting (XGBoost). Combining polygenic risk scores and clinical variables, we developed Cox proportional hazards models for risk prediction in these cancers.

RESULTS

Our models achieved high prediction accuracy for 8 cancers, with areas under the curve ranging from 0.618 (95% confidence interval = 0.581 to 0.655) for ovarian cancer to 0.831 (95% confidence interval = 0.817 to 0.845) for lung cancer. Additionally, our models could identify individuals at a high risk for developing cancer. For example, the risk of breast cancer for individuals in the top 5% score quantile was nearly 13 times greater than for individuals in the lowest 10%. Furthermore, we observed a higher proportion of individuals with high polygenic risk scores in the early-onset group but a higher proportion of individuals at high clinical risk in the late-onset group.

CONCLUSION

Our models demonstrated the potential to predict cancer risk and identify high-risk individuals with great generalizability to different cancers. Our findings suggested that the polygenic risk score model is more predictive for the cancer risk of early-onset patients than for late-onset patients, while the clinical risk model is more predictive for late-onset patients. Meanwhile, combining polygenic risk scores and clinical risk factors has overall better predictive performance than using polygenic risk scores or clinical risk factors alone.

Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review

BACKGROUND

Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked.

METHODS

Electronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model's average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity.

DISCUSSION

The results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked.

SYSTEMATIC REVIEW REGISTRATION

This protocol has been registered in PROSPERO under the registration number CRD42023483824.

Adherence to Annual Lung Cancer Screening in a Centralized Academic Program

BACKGROUND

Adherence to lung cancer screening (LCS) protocols is critical for achieving mortality reductions. However, adherence rates, particularly for recommended annual screening among patients with low-risk findings, are often sub-optimal. We evaluated annual LCS adherence for patients with low-risk findings participating in a centralized screening program at a tertiary academic center.

PATIENTS AND METHODS

We conducted a retrospective, observational cohort study of a centralized lung cancer screening program launched in July 2018. We performed electronic medical review of 337 patients who underwent low-dose CT (LDCT) screening before February 1, 2021 (to ensure ≥ 15 months follow up) and had a low-risk Lung-RADS score of 1 or 2. Captured data included patient characteristics (smoking history, Fagerstrom score, environmental exposures, lung cancer risk score), LDCT imaging dates, and Lung-RADS results. The primary outcome measure was adherence to annual screening. We used multivariable logistic regression models to identify factors associated with adherence.

RESULTS

Overall, 337 patients had an initial Lung-RADS result of 1 (n = 189) or 2 (n = 148). Among this cohort, 139 (73.5%) of Lung-RADS 1 and 111 (75.0%) of Lung-RADS 2 patients completed the annual repeat LDCT within 15 months, respectively. The only patient characteristic associated with adherence was having Medicaid coverage; compared to having private insurance, Medicaid patients were less adherent (adjusted OR = 0.37, 95% CI = 0.15-0.92). No other patient characteristic was associated with adherence.

CONCLUSION

Our centralized screening program achieved a high initial annual adherence rate. Although LCS has first-dollar insurance coverage, other socioeconomic concerns may present barriers to annual screening for Medicaid recipients.

Risk factors for early-onset lung cancer in Korea: analysis of a nationally representative population-based cohort

OBJECTIVES

We examined the associations of socioeconomic factors, health behaviors, and comorbidities with early-onset lung cancer.

METHODS

The study included 6,794,287 individuals aged 20-39 years who participated in a Korean national health check-up program from 2009 to 2012. During the follow-up period, 4,684 participants developed lung cancer. Multivariable Cox regression analysis was used to estimate the independent associations of potential risk factors with incident lung cancer.

RESULTS

Older age (multivariable hazard ratio [mHR], 1.13; 95% confidence interval [CI], 1.12 to 1.14) and female sex (mHR, 1.62; 95% CI, 1.49 to 1.75) were associated with increased lung cancer risk. Current smoking was also associated with elevated risk (<10 pack-years: mHR, 1.12; 95% CI, 1.01 to 1.24; ≥10 pack-years: mHR, 1.30; 95% CI, 1.18 to 1.45), but past smoking was not. Although mild alcohol consumption (<10 g/day) was associated with lower lung cancer risk (mHR, 0.92; 95% CI, 0.86 to 0.99), heavier alcohol consumption (≥10 g/day) was not. Higher income (highest vs. lowest quartile: mHR, 0.86; 95% CI, 0.78 to 0.94), physical activity for at least 1,500 metabolic equivalent of task-min/wk (vs. non-exercisers: mHR, 0.83; 95% CI, 0.69 to 0.99) and obesity (vs. normal weight: mHR, 0.89; 95% CI, 0.83 to 0.96) were associated with lower lung cancer risk, whereas metabolic syndrome was associated with increased risk (mHR, 1.13; 95% CI, 1.03 to 1.24).

CONCLUSIONS

In young adults, age, female sex, smoking, and metabolic syndrome were risk factors for early-onset lung cancer, while high income, physical activity, and obesity displayed protective effects.

Low-Dose Computed Tomography Screening in Relatives With a Family History of Lung Cancer

INTRODUCTION

The role of a family history of lung cancer (LCFH) in screening using low-dose computed tomography (LDCT) has not been prospectively investigated with long-term follow-up.

METHODS

A multicenter prospective study with up to three rounds of annual LDCT screening was conducted to determine the detection rate of lung cancer (LC) in asymptomatic first- or second-degree relatives of LCFH.

RESULTS

From 2007 to 2011, there were 1102 participants enrolled, including 805 and 297 from simplex and multiplex families (MFs), respectively (54.2% women and 70.0% never-smokers). The last follow-up date was May 5, 2021. The overall LC detection rate was 4.5% (50 of 1102). The detection rate in MF was 9.4% (19 of 202) and 4.4% (4 of 91) in never-smokers and in those who smoked, respectively. The corresponding rates for simplex families were 3.7% (21 of 569) and 2.7% (6 of 223), respectively. Of these, 68.0% and 22.0% of cases with stage I and IV diseases, respectively. LC diagnoses within a 3-year interval from the initial screening tend to be younger, have a higher detection rate, and have stage I disease; thereafter, more stage III-IV disease and 66.7% (16 of 24) with negative or semipositive nodules in initial computed tomography scans. Within the 6-year interval, only maternal (modified rate ratio = 4.46, 95% confidence interval: 2.32-8.56) or maternal relative history of LC (modified rate ratio = 5.41, 95% confidence interval: 2.84-10.30) increased the risk of LC.

CONCLUSIONS

LCFH is a risk factor for LC and is increased with MF history, among never-smokers, younger adults, and those with maternal relatives with LC. Randomized controlled trials are needed to confirm the mortality benefit of LDCT screening in those with LCFH.

A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea

Purpose

Obstructive sleep apnea (OSA) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSA patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSA patients.

Patients and Methods

We performed a retrospective cohort study including 1920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSA. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSA. Moreover, we compared the performance of our model with the most commonly used screening tool, Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires.

Results

Severe OSA was associated with male, BMI≥ 28 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥9.5×109/L, hemoglobin ≥175g/L, triglycerides ≥1.7 mmol/L. The AUC of the final model was 0.76 (95% CI: 0.74-0.78), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 73% and 66%. Among patients having the information of SBQ, the AUC of our model was statistically significantly greater than that of SBQ (0.78 vs 0.66, P = 0.002).

Conclusion

Based on common clinical examination of admission, we develop a novel model and a nomogram for identifying severe OSA from inpatient with suspected OSA, which provides physicians with a visual and easy-to-use tool for screening severe OSA.

[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.

Hemoglobin and neutrophil levels stratified according to International Metastatic Renal Cell Carcinoma Database Consortium risk predict the effectiveness of ipilimumab plus nivolumab in patients with advanced metastatic renal cell carcinoma

OBJECTIVE

To identify biomarkers associated with the effectiveness of ipilimumab plus nivolumab against advanced metastatic renal cell carcinoma.

METHODS

We retrospectively analyzed the data of 75 patients treated with ipilimumab plus nivolumab at seven hospitals between August 2018 and April 2021. Prognostic biomarkers were assessed prior to initiating treatment with ipilimumab plus nivolumab. Median overall survival and progression-free survival were examined using the Kaplan-Meier method. Univariate and multivariate analyses were performed to identify predictors of disease progression. The International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk factors most important for predicting disease progression were determined using classification and regression tree analysis.

RESULTS

Median overall survival and progression-free survival were longer in the intermediate IMDC risk group than in the poor IMDC risk group (overall: not reached vs. 18.3 months; progression-free: not reached vs. 13.5 months). The multivariate analysis identified poor IMDC risk as a risk factor for disease progression (hazard ratio 2.61, 95% confidence interval: 1.05-6.51). Based on the results of the classification and regression tree analysis, the cohort was divided into non-anemia, anemia + neutro-Low, and anemia + neutro-High groups. Median overall survival and progression-free survival were longer in the non-anemia and anemia + neutro-Low groups than in the anemia + neutro-High group (overall: not reached vs. 29.3 months vs. 4.3 months: progression-free: not reached vs. 29.0 months vs. 3.9 months).

CONCLUSION

Hemoglobin and neutrophil levels may represent crucial biomarkers for predicting the effectiveness of ipilimumab plus nivolumab therapy in patients with renal cell carcinoma.

The Inflammatory Profile Correlates with COVID-19 Severity and Mortality in Cancer Patients

BACKGROUND

The correlation of the inflammatory profile with the severity of the disease in neoplastic patients with SARS-CoV-2 infection was addressed.

METHODS

A database of 1537 patients hospitalized in the pneumology department was analyzed. After applying the inclusion and exclusion criteria, 83 patients (67% males, 33% females) were included.

RESULTS

Most of the analyzed patients were hospitalized with a moderate form of disease, explaining the significant percentage of 25% mortality. The frequency of the type of neoplasm was higher for lung cancer, followed by malignant colon tumor. We identified a significant association between the increased value of ferritin (p < 0.0001, OR = 22.31), fibrinogen (p = 0.009, OR = 13.41), and C-reactive protein (p = 0.01, OR = 7.65), respectively, and the level of severity of COVID-19. The results of the univariate logistic regression analysis for predicting the severity of the disease revealed that the increased values of ferritin (p = 0.001, OR = 22.31) and fibrinogen (p = 0.02, OR = 13.41) represent a risk for a serious negative prognosis of COVID-19.

CONCLUSIONS

Our study demonstrated that the value of the analyzed inflammatory parameters increased in direct proportion to the severity of the disease and that higher values were associated with increased mortality in the study group.

Lung cancer risk score for ever and never smokers in China

BACKGROUND

Most lung cancer risk prediction models were developed in European and North-American cohorts of smokers aged ≥ 55 years, while less is known about risk profiles in Asia, especially for never smokers or individuals aged < 50 years. Hence, we aimed to develop and validate a lung cancer risk estimate tool for ever and never smokers across a wide age range.

METHODS

Based on the China Kadoorie Biobank cohort, we first systematically selected the predictors and explored the nonlinear association of predictors with lung cancer risk using restricted cubic splines. Then, we separately developed risk prediction models to construct a lung cancer risk score (LCRS) in 159,715 ever smokers and 336,526 never smokers. The LCRS was further validated in an independent cohort over a median follow-up of 13.6 years, consisting of 14,153 never smokers and 5,890 ever smokers.

RESULTS

A total of 13 and 9 routinely available predictors were identified for ever and never smokers, respectively. Of these predictors, cigarettes per day and quit years showed nonlinear associations with lung cancer risk (Pnon-linear < 0.001). The curve of lung cancer incidence increased rapidly above 20 cigarettes per day and then was relatively flat until approximately 30 cigarettes per day. We also observed that lung cancer risk declined sharply within the first 5 years of quitting, and then continued to decrease but at a slower rate in the subsequent years. The 6-year area under the receiver operating curve for the ever and never smokers' models were respectively 0.778 and 0.733 in the derivation cohort, and 0.774 and 0.759 in the validation cohort. In the validation cohort, the 10-year cumulative incidence of lung cancer was 0.39% and 2.57% for ever smokers with low (< 166.2) and intermediate-high LCRS (≥ 166.2), respectively. Never smokers with a high LCRS (≥ 21.2) had a higher 10-year cumulative incidence rate than those with a low LCRS (< 21.2; 1.05% vs. 0.22%). An online risk evaluation tool (LCKEY; http://ccra.njmu.edu.cn/lckey/web) was developed to facilitate the use of LCRS.

CONCLUSIONS

The LCRS can be an effective risk assessment tool designed for ever and never smokers aged 30 to 80 years.

Systematic Review of Lung Cancer Screening: Advancements and Strategies for Implementation

Lung cancer is the leading cause of cancer-related deaths in Europe, with low survival rates primarily due to late-stage diagnosis. Early detection can significantly improve survival rates, but lung cancer screening is not currently implemented in Italy. Many countries have implemented lung cancer screening programs for high-risk populations, with studies showing a reduction in mortality. This review aimed to identify key areas for establishing a lung cancer screening program in Italy. A literature search was conducted in October 2022, using the PubMed and Scopus databases. Items of interest included updated evidence, approaches used in other countries, enrollment and eligibility criteria, models, cost-effectiveness studies, and smoking cessation programs. A literature search yielded 61 scientific papers, highlighting the effectiveness of low-dose computed tomography (LDCT) screening in reducing mortality among high-risk populations. The National Lung Screening Trial (NLST) in the United States demonstrated a 20% reduction in lung cancer mortality with LDCT, and other trials confirmed its potential to reduce mortality by up to 39% and detect early-stage cancers. However, false-positive results and associated harm were concerns. Economic evaluations generally supported the cost-effectiveness of LDCT screening, especially when combined with smoking cessation interventions for individuals aged 55 to 75 with a significant smoking history. Implementing a screening program in Italy requires the careful consideration of optimal strategies, population selection, result management, and the integration of smoking cessation. Resource limitations and tailored interventions for subpopulations with low-risk perception and non-adherence rates should be addressed with multidisciplinary expertise.

Evidence relating cigarette, cigar and pipe smoking to lung cancer and chronic obstructive pulmonary disease: Meta-analysis of recent data from three regions

BACKGROUND

There is a need to have up-to-date information for various diseases on the risk related to the use of different smoked products and the use of other nicotine-containing products. Here, we contribute to the information pool by presenting up-to-date quantitative evidence for North America, Europe and Japan and for both lung cancer and chronic obstructive pulmonary disease (COPD) on the relative risk (RR) relating to current vs never product use for each of the three smoked tobacco products, cigarettes, cigars and pipes.

AIM

To estimate lung cancer and COPD current smoking RRs for the three products using recent data for the three regions.

METHODS

Publications in English from 2010 to 2020 were considered that, based on epidemiological studies in the three regions, estimated the current smoking RR of lung cancer and/or COPD for one or more of the three products. The studies should involve at least 100 cases of the disease considered, not be restricted to specific lung cancer types or populations with specific medical conditions, and should be of cohort or nested case-control study design or randomized controlled trials. Literature searches were conducted on MEDLINE separately for lung cancer and for COPD, examining titles and abstracts initially, and then full texts. Additional papers were sought from reference lists of selected papers, reviews and meta-analyses. For each study identified, the most recent available data on each product were entered on current smoking, as well as on characteristics of the study and the RR estimates. Combined RR estimates were derived using random-effects meta-analysis. For cigarette smoking, where far more data were available, heterogeneity was studied by a wide range of factors. For cigar and pipe smoking, a more limited heterogeneity analysis was carried out. Results were compared with those from previous meta-analyses published since 2000.

RESULTS

Current cigarette smoking: For lung cancer, 44 studies (26 North American, 14 European, three Japanese, and one in multiple continents), gave an overall estimate of 12.14 [95% confidence interval (CI) 10.30-14.30]. The estimates were higher (heterogeneity P < 0.001) for North American (15.15, CI 12.77-17.96) and European studies (12.30, CI 9.77-15.49) than for Japanese studies (3.61, CI 2.87-4.55), consistent with previous evidence of lower RRs for Asia. RRs were higher (P < 0.05) for death (14.85, CI 11.99-18.38) than diagnosis (10.82, CI 8.61-13.60). There was some variation (P < 0.05) by study population, with higher RRs for international and regional studies than for national studies and studies of specific populations. RRs were higher in males, as previously reported, the within-study male/female ratio of RRs being 1.52 (CI 1.20-1.92). RRs did not vary significantly (P ≥ 0.05) by other factors. For COPD, RR estimates were provided by 18 studies (10 North American, seven European, and one Japanese). The overall estimate of 9.19 (CI 6.97-12.13), was based on heterogeneous data (P < 0.001), and higher than reported earlier. There was no (P > 0.1) variation by sex, region or exclusive use, but limited evidence (0.05 < P < 0.1) that RR estimates were greater where cases occurring shortly after baseline were ignored; where bronchiectasis was excluded from the COPD definition; and with greater confounder adjustment. Within-study comparisons showed adjusted RRs exceeded unadjusted RRs. Current cigar smoking: Three studies gave an overall lung cancer RR of 2.73 (CI 2.36-3.15), with no heterogeneity, lower than the 4.67 (CI 3.49-6.25) reported in an earlier review. Only one study gave COPD results, the RR (2.44, CI 0.98-6.05) being imprecise. Current pipe smoking: Four studies gave an overall lung cancer RR of 4.93 (CI 1.97-12.32), close to the 5.20 (CI 3.50-7.73) given earlier. However, the estimates were heterogeneous, with two above 10, and two below 3. Only one study gave COPD results, the RR (1.12, CI 0.29-4.40), being imprecise. For both diseases, the lower RR estimates for cigars and for pipes than for current smoking of cigarettes aligns with earlier published evidence.

CONCLUSION

Current cigarette smoking substantially increases lung cancer and COPD risk, more so in North America and Europe than Japan. Limited evidence confirms lower risks for cigars and pipes than cigarettes.

Prevalence and clinical characteristics of non-malignant CT detected incidental findings in the SUMMIT lung cancer screening cohort

BACKGROUND

Pulmonary and extrapulmonary incidental findings are frequently identified on CT scans performed for lung cancer screening. Uncertainty regarding their clinical significance and how and when such findings should be reported back to clinicians and participants persists. We examined the prevalence of non-malignant incidental findings within a lung cancer screening cohort and investigated the morbidity and relevant risk factors associated with incidental findings. We quantified the primary and secondary care referrals generated by our protocol.

METHODS

The SUMMIT study (NCT03934866) is a prospective observational cohort study to examine the performance of delivering a low-dose CT (LDCT) screening service to a high-risk population. Spirometry, blood pressure, height/weight and respiratory history were assessed as part of a Lung Health Check. Individuals at high risk of lung cancer were offered an LDCT and returned for two further annual visits. This analysis is a prospective evaluation of the standardised reporting and management protocol for incidental findings developed for the study on the baseline LDCT.

RESULTS

In 11 115 participants included in this analysis, the most common incidental findings were coronary artery calcification (64.2%) and emphysema (33.4%). From our protocolised management approach, the number of participants requiring review for clinically relevant findings in primary care was 1 in 20, and the number potentially requiring review in secondary care was 1 in 25.

CONCLUSIONS

Incidental findings are common in lung cancer screening and can be associated with reported symptoms and comorbidities. A standardised reporting protocol allows systematic assessment and standardises onward management.

Development and internal validation of nomograms based on plasma metabolites to predict non-small cell lung cancer risk in smoking and nonsmoking populations

BACKGROUND

Lung cancer has significantly higher incidence and mortality rates worldwide. In this study, we analyzed the metabolic profiles of non-small cell lung cancer (NSCLC) patients and constructed prediction models for smokers and nonsmokers with internal validation.

METHODS

Plasma was collected from all patients enrolled for metabolic profiling by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The total population was divided into two groups according to smoking or not. Statistical analysis of metabolites was performed separately for each group and prediction models were constructed.

RESULTS

A total of 1723 patients (1109 NSCLC patients and 614 healthy controls) were enrolled from the affiliated hospital during 2018 to 2021. After grouping by smoking history, each group was statistically analyzed and prediction models were constructed, which resulted in eight indicators (propionylcarnitine, arginine, citrulline, etc.) significantly associated with lung cancer risk for smokers and eight indicators (dodecanoylcarnitine, hydroxybutyrylcarnitine, asparagine, etc.) for nonsmokers (p < 0.05). The smoker model indicated an AUC of 0.860 in the training set and 0.850 in the validation set. The nonsmoker model showed an AUC of 0.783 in the training set and 0.762 in the validation set. Further calibration tests for both models indicated excellent goodness-of-fit results.

CONCLUSIONS

In this study, we found a series of metabolites significantly associated with lung cancer incidence and constructed respectively prediction models for NSCLC risk in smokers and nonsmokers, with internal validation to confirm the efficiency to discriminate lung cancer risk in both smoking and nonsmoking states.

Development and validation of a simple general population lung cancer risk model including AHRR-methylation

INTRODUCTION

Screening reduces lung cancer mortality of high-risk populations. Currently proposed screening eligibility criteria only identify half of those individuals, who later develop lung cancer. This study aimed to develop and validate a sensitive and simple model for predicting 10-year lung cancer risk.

METHODS

Using the 1991-94 examination of The Copenhagen City Heart Study in Denmark, 6,820 former or current smokers from the general population were followed for lung cancer within 10 years after examination. Logistic regression of baseline variables (age, sex, education, chronic obstructive pulmonary disease, family history of lung cancer, smoking status and cumulative smoking, secondhand smoking, occupational exposures to dust and fume, body mass index, lung function, plasma C-reactive protein, and AHRR(cg05575921) methylation) identified the best predictive model. The model was validated among 3,740 former or current smokers from the 2001-03 examination, also followed for 10 years. A simple risk chart was developed with Poisson regression.

RESULTS

Age, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation identified 65 of 88 individuals who developed lung cancer in the validation cohort. The highest risk group, consisting of less educated men aged >65 with current smoking status and cumulative smoking >20 pack-years, had absolute 10-year risks varying from 4% to 16% by AHRR(cg05575921) methylation.

CONCLUSION

A simple risk chart including age, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation, identifies individuals with 10-year lung cancer risk from below 1% to 16%. Including AHRR(cg05575921) methylation in the eligibility criteria for screening identifies smokers who would benefit the most from screening.

Using the Past to Understand the Future of U.S. and Global Smoking Disparities: A Birth Cohort Perspective

U.S. smoking-related disparities persist, but data evaluating how smoking patterns across diverse populations have changed by birth cohort are lacking. Worldwide, smoking continues to exact harm, especially to low- and middle-income nations with less historical data for smoking analyses. The Cancer Intervention and Surveillance Modeling Network (CISNET) Lung Working Group previously generated smoking histories for the whole U.S. population using an age, period, and birth cohort (APC) methodological framework. These inputs have been used in numerous models to simulate future patterns of smoking and to evaluate the potential impact of policies. However, the absence of detailed model-ready inputs on smoking behaviors for diverse U.S. populations has been a barrier to research evaluating future trends in smoking-related disparities or the projected impacts of policies across sociodemographic groups. This supplement issue provides new estimates of smoking behaviors with detailed historical data by race/ethnicity, educational attainment, family income, and for each of the 50 U.S. states and Washington, DC. All-cause mortality relative risks associated with smoking by race/ethnicity and educational attainment are also available for the first time. Finally, the supplement issue presents comprehensive smoking histories for Brazil, demonstrating the application of this methodology to resource-limited settings. Collectively, these data aim to offer insight into future U.S. and global smoking disparities and accelerate research on tobacco control policies that advance health equity. This effort will allow tobacco simulation models to account comprehensively for population diversity, thereby enabling researchers to develop more sophisticated analyses of tobacco use and control interventions.

Patterns of Birth Cohort‒Specific Smoking Histories by Race and Ethnicity in the U.S

INTRODUCTION

U.S. smoking prevalence varies greatly by race/ethnicity. However, little is known about how smoking initiation, cessation, and intensity vary by birth cohort and race/ethnicity.

METHODS

Adult smoking data were obtained from the 1978-2018 National Health Interview Surveys. Age‒period‒cohort models with constrained natural splines were developed to estimate historical smoking patterns among non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian and Pacific Islander, and non-Hispanic American Indian and Alaskan Native individuals. Annual smoking prevalence and probabilities of smoking initiation, cessation, and intensity by age, year, gender, and race/ethnicity were estimated for the 1900 to 2000 birth cohorts. Analysis was conducted in 2020-2021.

RESULTS

Smoking initiation probabilities were highest for the American Indian and Alaskan Native population, second highest among the non-Hispanic White population, and lowest among Asian and Pacific Islander and Hispanic populations across birth cohorts. Historically, initiation probabilities among non-Hispanic Black populations were comparable with those among non-Hispanic White populations but have decreased since the 1970 birth cohort. Cessation probabilities were lowest among American Indian and Alaskan Native and non-Hispanic Black populations and highest among non-Hispanic White and Asian and Pacific Islander populations across cohorts and ages. Initiation and cessation probabilities produce observed patterns of smoking where prevalence among American Indian and Alaskan Native populations is highest across all ages and cohorts. Across cohorts, smoking prevalence among non-Hispanic Black populations, particularly males, is lower than among non-Hispanic White populations at younger ages but higher at older ages.

CONCLUSIONS

There are important and persistent racial/ethnic differences in smoking prevalence, initiation, cessation, and intensity across U.S. birth cohorts. Targeted interventions should address widening smoking disparities by race/ethnicity, particularly for American Indian and Alaskan Native and non-Hispanic Black populations.

Lung cancer screening

Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.

Growing small solid nodules in lung cancer screening: safety and efficacy of a 200 mm3 minimum size threshold for multidisciplinary team referral

The optimal management of small but growing nodules remains unclear. The SUMMIT study nodule management algorithm uses a specific threshold volume of 200 mm3 before referral of growing solid nodules to the multidisciplinary team for further investigation is advised, with growing nodules below this threshold kept under observation within the screening programme. Malignancy risk of growing solid nodules of size >200 mm3 at initial 3-month interval scan was 58.3% at a per-nodule level, compared with 13.3% in growing nodules of size ≤200 mm3 (relative risk 4.4, 95% CI 2.17 to 8.83). The positive predictive value of a combination of nodule growth (defined as percentage volume change of ≥25%), and size >200 mm3 was 65.9% (29/44) at a cancer-per-nodule basis, or 60.5% (23/38) on a cancer-per-participant basis. False negative rate of the protocol was 1.9% (95% CI 0.33% to 9.94%). These findings support the use of a 200 mm3 minimum volume threshold for referral as effective at reducing unnecessary multidisciplinary team referrals for small growing nodules, while maintaining early-stage lung cancer diagnosis.

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.

Utilisation of primary care electronic patient records for identification and targeted invitation of individuals to a lung cancer screening programme

Lung cancer screening (LCS) eligibility is largely determined by tobacco consumption. Primary care smoking data could guide LCS invitation and eligibility assessment. We present observational data from the SUMMIT Study, where individual self-reported smoking status was concordant with primary care records in 75.3%. However, 10.3% demonstrated inconsistencies between historic and most recent smoking status documentation. Quantified tobacco consumption was frequently missing, precluding direct LCS eligibility assessment. Primary care recorded "ever-smoker" status, encompassing both recent and historic documentation, can be used to target LCS invitation. Identifying those with missing or erroneous "never-smoker" smoking status is crucial for equitable invitation to LCS.

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.

Contemporary Concise Review 2021: Pulmonary nodules from detection to intervention

The US Preventive Task Force (USPSTF) has updated screening criteria by expanding age range and reducing smoking history required for eligibility; the International Lung Screen Trial (ILST) data have shown that PLCOM2012 performs better for eligibility than USPSTF criteria. Screening adherence is low (4%-6% of potential eligible candidates in the United States) and depends upon multiple system and patient/candidate-related factors. Smoking cessation in lung cancer improves survival (past prospective trial data, updated meta-analysis data); smoking cessation is an essential component of lung cancer screening. Circulating biomarkers are emerging to optimize screening and early diagnosis. COVID-19 continues to affect lung cancer treatment and screening through delays and disruptions; specific operational challenges need to be met. Over 70% of suspected malignant lesions develop in the periphery of the lungs. Bronchoscopic navigational techniques have been steadily improving to allow greater accuracy with target lesion approximation and therefore diagnostic yield. Fibre-based imaging techniques provide real-time microscopic tumour visualization, with potential diagnostic benefits. With significant advances in peripheral lung cancer localization, bronchoscopically delivered ablative therapies are an emerging field in limited stage primary and oligometastatic disease. In advanced stage lung cancer, small-volume samples acquired through bronchoscopic techniques yield material of sufficient quantity and quality to support clinically relevant biomarker assessment.

Lung cancer incidence among world trade center rescue and recovery workers

Background

Many World Trade Center disaster (WTC) rescue and recovery workers (WTC RRWV) were exposed to toxic inhalable particles. The impact of WTC exposures on lung cancer risk is unclear.

Methods

Data from the WTC Health Program General Responders Cohort (WTCGRC) were linked to health information from a large New York City health system to identify incident lung cancer cases. Incidence rates for lung cancer were then calculated. As a comparison group, we created a microsimulation model that generated expected lung cancer incidence rates for a WTC‐ and occupationally‐unexposed cohort with similar characteristics. We also fitted a Poisson regression model to determine specific lung cancer risk factors for WTC RRWV.

Results

The incidence of lung cancer for WTC RRWV was 39.5 (95% confidence interval [CI]: 30.7–49.9) per 100,000 person‐years. When compared to the simulated unexposed cohort, no significant elevation in incidence was found among WTC RRWV (incidence rate ratio [IRR] 1.34; 95% CI: 0.92–1.96). Predictors of lung cancer incidence included age, smoking intensity, and years since quitting for former smokers. In adjusted models evaluating airway obstruction and individual pre‐WTC occupational exposures, only mineral dust work was associated with lung cancer risk (IRR: 2.03; 95% CI: 1.07–3.86).

Discussion

In a sample from a large, prospective cohort of WTC RRWV we found a lung cancer incidence rate that was similar to that expected of a WTC‐ and occupationally‐unexposed cohort with similar individual risk profiles. Guideline‐concordant lung cancer surveillance and periodic evaluations of population‐level lung cancer risk should continue in this group.

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

OBJECTIVE

Lung 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.

METHOD

In 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.

RESULTS

The 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.

CONCLUSION

Our 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.

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.

Statistical biopsy: An emerging screening approach for early detection of cancers

Despite large investment cancer continues to be a major source of mortality and morbidity throughout the world. Traditional methods of detection and diagnosis such as biopsy and imaging, tend to be expensive and have risks of complications. As data becomes more abundant and machine learning continues advancing, it is natural to ask how they can help solve some of these problems. In this paper we show that using a person's personal health data it is possible to predict their risk for a wide variety of cancers. We dub this process a "statistical biopsy." Specifically, we train two neural networks, one predicting risk for 16 different cancer types in females and the other predicting risk for 15 different cancer types in males. The networks were trained as binary classifiers identifying individuals that were diagnosed with the different cancer types within 5 years of joining the PLOC trial. However, rather than use the binary output of the classifiers we show that the continuous output can instead be used as a cancer risk allowing a holistic look at an individual's cancer risks. We tested our multi-cancer model on the UK Biobank dataset showing that for most cancers the predictions generalized well and that looking at multiple cancer risks at once from personal health data is a possibility. While the statistical biopsy will not be able to replace traditional biopsies for diagnosing cancers, we hope there can be a shift of paradigm in how statistical models are used in cancer detection moving to something more powerful and more personalized than general population screening guidelines.

Artificial intelligence: opportunities in lung cancer

PURPOSE OF REVIEW

In this article, we focus on the role of artificial intelligence in the management of lung cancer. We summarized commonly used algorithms, current applications and challenges of artificial intelligence in lung cancer.

RECENT FINDINGS

Feature engineering for tabular data and computer vision for image data are commonly used algorithms in lung cancer research. Furthermore, the use of artificial intelligence in lung cancer has extended to the entire clinical pathway including screening, diagnosis and treatment. Lung cancer screening mainly focuses on two aspects: identifying high-risk populations and the automatic detection of lung nodules. Artificial intelligence diagnosis of lung cancer covers imaging diagnosis, pathological diagnosis and genetic diagnosis. The artificial intelligence clinical decision-support system is the main application of artificial intelligence in lung cancer treatment. Currently, the challenges of artificial intelligence applications in lung cancer mainly focus on the interpretability of artificial intelligence models and limited annotated datasets; and recent advances in explainable machine learning, transfer learning and federated learning might solve these problems.

SUMMARY

Artificial intelligence shows great potential in many aspects of the management of lung cancer, especially in screening and diagnosis. Future studies on interpretability and privacy are needed for further application of artificial intelligence in lung cancer.

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.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.

Lung cancer risk following previous abnormal chest radiographs: A 27-year follow-up study of a Chinese lung screening cohort

Background

Chest radiograph (CXR) is still one of the most commonly used diagnostic tools for chest diseases. In this cohort study, we attempted to investigate the magnitude and temporal pattern of lung cancer risk following abnormal CXR findings.

Methods

We conducted an extended follow‐up of an occupational screening cohort in Yunnan, China. The associations between abnormal CXR results from baseline screening, the first four consecutive rounds of CXR screening, all previous rounds of screening and lung cancer risk were analyzed using time‐varying coefficient Cox regression model. The associations of lung cancer risk and previous CXR‐screening results according to histology were also considered. Sensitivity analyses were conducted to assess the robustness of the previous abnormal CXR findings on subsequent lung cancer risk.

Results

Abnormal CXR findings were associated with a significantly increased lung cancer risk. This relative hazard significantly decreased over time. Compared to negative screening results, the adjusted hazard ratios (HR) of baseline abnormal CXR results, and at least one abnormal result in the first four consecutive screening rounds during the first 5 years of follow‐up were 17.06 (95% CI: 11.74–24.79) and 13.77 (95%: 9.58–17.79), respectively. This significantly increased lung cancer risk continued over the next 5 years. These associations were stronger for persistent abnormal findings, and abnormal findings identified in recent screening rounds.

Conclusions

The increased risk was significant for both squamous cell carcinoma and adenocarcinoma. Although decreased over time, an increased lung cancer risk relative to abnormal CXR findings can continue for 10 years.

Establishing a Cohort and a Biorepository to Identify Biomarkers for Early Detection of Lung Cancer: The Nashville Lung Cancer Screening Trial Cohort

Rationale: A prospective longitudinal cohort of individuals at high risk of developing lung cancer was established to build a biorepository of carefully annotated biological specimens and low-dose computed tomography (LDCT) chest images for derivation and validation of candidate biomarkers for early detection of lung cancer.Objectives: The goal of this study is to characterize individuals with high risk for lung cancer, accumulating valuable biospecimens and LDCT chest scans longitudinally over 5 years.Methods: Participants 55-80 years of age with a 5-year estimated risk of developing lung cancer >1.5% were recruited and enrolled from clinics at the Vanderbilt University Medical Center, Veteran Affairs Medical Center, and Meharry Medical Center. Individual demographic characteristics were assessed via questionnaire at baseline. Participants underwent an LDCT scan, spirometry, sputum cytology, and research bronchoscopy at the time of enrollment. Participants will be followed yearly for 5 years. Positive LDCT scans are followed-up according to standard of care. The clinical, imaging, and biospecimen data are collected prospectively and stored in a biorepository. Participants are offered smoking cessation counseling at each study visit.Results: A total of 480 participants were enrolled at study baseline and consented to sharing their data and biospecimens for research. Participants are followed with yearly clinic visits to collect imaging data and biospecimens. To date, a total of 19 cancers (13 adenocarcinomas, four squamous cell carcinomas, one large cell neuroendocrine, and one small-cell lung cancer) have been identified.Conclusions: We established a unique prospective cohort of individuals at high risk for lung cancer, enrolled at three institutions, for whom full clinical data, well-annotated LDCT scans, and biospecimens are being collected longitudinally. This repository will allow for the derivation and independent validation of clinical, imaging, and molecular biomarkers of risk for diagnosis of lung cancer.Clinical trial registered with ClinicalTrials.gov (NCT01475500).

Individual 5-Year Lung Cancer Risk Prediction Model in Korea Using a Nationwide Representative Database

Early detection of lung cancer by screening has contributed to reduce lung cancer mortality. Identifying high risk subjects for lung cancer is necessary to maximize the benefits and minimize the harms followed by lung cancer screening. In the present study, individual lung cancer risk in Korea was presented using a risk prediction model. Participants who completed health examinations in 2009 based on the Korean National Health Insurance (KNHI) database (DB) were eligible for the present study. Risk scores were assigned based on the adjusted hazard ratio (HR), and the standardized points for each risk factor were calculated to be proportional to the b coefficients. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability assessed by plotting the mean predicted probability against the mean observed probability of lung cancer. Among candidate predictors, age, sex, smoking intensity, body mass index (BMI), presence of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis (TB), and type 2 diabetes mellitus (DM) were finally included. Our risk prediction model showed good discrimination (c-statistic, 0.810; 95% CI: 0.801-0.819). The relationship between model-predicted and actual lung cancer development correlated well in the calibration plot. When using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding lung cancer screening or lifestyle modification, including smoking cessation.

Utility of incorporating a gene-based lung cancer risk test on uptake and adherence in a community-based lung cancer screening pilot study

Based on the results of randomized control trials, screening for lung cancer using computed tomography (CT) is now widely recommended. However, adherence to screening remains an issue outside the clinical trial setting. This study examines the utility of biomarker-based risk assessment on uptake and subsequent adherence in a community screening study.

In a single arm pilot study, current or former smokers > 50 years old with 20 + pack year history were recruited following local advertising. One hundred and fifty seven participants volunteered to participate in the study that offered an optional gene-based lung cancer risk assessment followed by low-dose CT according to a standardised screening protocol.

All 157 volunteers who attended visit 1 underwent the gene-based risk assessment comprising of a clinical questionnaire and buccal swab. Of this group, 154 subsequently attended for CT screening (98%) and were followed prospectively for a median of 2.7 years. A participant’s adherence to screening was influenced by their baseline lung cancer risk category, with overall adherence in those with a positive scan being significantly greater in the “very high” risk group compared to “moderate” and “high” risk categories (71% vs 52%, Odds ratio = 2.27, 95% confidence interval of 1.02–5.05, P = 0.047). Those in the “moderate” risk group were not different to those in the “high” risk group (52% and 52%, P > 0.05).

In this proof-of-concept study, personalised gene-based lung cancer risk assessment was well accepted, associated with a 98% uptake for screening and increased adherence for those in the highest risk group.

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.

Ongoing challenges in implementation of lung cancer screening

Lung cancer is the leading cause of cancer deaths in Europe and around the world. Although available therapies have undergone considerable development in the past decades, the five-year survival rate for lung cancer remains low. This sobering outlook results mainly from the advanced stages of cancer most patients are diagnosed with. As the population at risk is relatively well defined and early stage disease is potentially curable, lung cancer outcomes may be improved by screening. Several studies already show that lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality. However, for a successful implementation of LCS programmes, several challenges have to be overcome: selection of high-risk individuals, standardization of nodule classification and measurement, specific training of radiologists, optimization of screening intervals and screening duration, handling of ancillary findings are some of the major points which should be addressed. Last but not least, the psychological impact of screening on screened individuals and the impact of potential false positive findings should not be neglected. The aim of this review is to discuss the different challenges of implementing LCS programmes and to give some hints on how to overcome them. Finally, we will also discuss the psychological impact of screening on quality of life and the importance of smoking cessation.

Low positive predictive value of computed tomography screening for lung cancer irrespective of commonly employed definitions of target population

Screening for lung cancer (LC) by low-dose computed tomography (LDCT) has been demonstrated to reduce LC mortality in randomized clinical trials (RCTs), and its implementation is in preparation in many countries. However, definition of the target population, which was based on various combinations of age ranges and definitions of heavy smoking in the RCTs, is subject to ongoing debate. Using epidemiological data from Germany, we aimed to estimate prevalence of preclinical LC and positive predictive value (PPV) of LDCT in potential target populations defined by age and smoking history. Populations aged 50 to 69, 55 to 69, 50 to 74 and 55 to 79 years were considered in this analysis. Sex-specific prevalence of preclinical LC was estimated using LC incidence data within those age ranges and annual transition rates from preclinical to clinical LC obtained by meta-analysis. Prevalence of preclinical LC among heavy smokers (defined by various pack-year thresholds) within those age ranges was estimated by combining LC prevalence in the general population with proportions of heavy smokers and relative risks for LC among them derived from epidemiological studies. PPVs were calculated by combining these prevalences with sensitivity and specificity estimates of LDCT. Estimated prevalence of LC was 0.3% to 0.5% (men) and 0.2% to 0.3% (women) in the general population and 0.8% to 1.7% in target populations of heavy smokers. Estimates of PPV of LDCT were <20% for all definitions of target populations of heavy smokers. Refined preselection of target populations would be highly desirable to increase PPV and efficiency of LDCT screening and to reduce numbers of false-positive LDCT findings.

Protocol and Rationale for the International Lung Screening Trial

Rationale: The NLST (National Lung Screening Trial) reported a 20% reduction in lung cancer mortality with low-dose computed tomography screening; however, important questions on how to optimize screening remain, including which selection criteria are most accurate at detecting lung cancers and what nodule management protocol is most efficient. The PLCO_m2012 (Prostate, Lung, Colorectal and Ovarian) Cancer Screening Trial 6-year and PanCan (Pan-Canadian Early Detection of Lung Cancer) nodule malignancy risk models are two of the better validated risk prediction models for screenee selection and nodule management, respectively. Combined use of these models for participant selection and nodule management could significantly improve screening efficiency.

Objectives: The ILST (International Lung Screening Trial) is a prospective cohort study with two primary aims: 1) Compare the accuracy of the PLCO_m2012 model against U.S. Preventive Services Task Force (USPSTF) criteria for detecting lung cancers and 2) evaluate nodule management efficiency using the PanCan nodule probability calculator-based protocol versus Lung-RADS.

Methods: ILST will recruit 4,500 participants who meet USPSTF and/or PLCO_m2012 risk ≥1.51%/6-year selection criteria. Participants will undergo baseline and 2-year low-dose computed tomography screening. Baseline nodules are managed according to PanCan probability score. Participants will be followed up for a minimum of 5 years. Primary outcomes for aim 1 are the proportion of individuals selected for screening, proportion of lung cancers detected, and positive predictive values of either selection criteria, and outcomes for aim 2 include comparing distributions of individuals and the proportion of lung cancers in each of three management groups: next surveillance scan, early recall scan, or diagnostic evaluation recommended. Statistical powers to detect differences in the four components of primary study aims were ≥82%.

Conclusions: ILST will prospectively evaluate the comparative accuracy and effectiveness of two promising multivariable risk models for screenee selection and nodule management in lung cancer screening.

Clinical trial registered with www.clinicaltrials.gov (NCT02871856).

Risk Perception Among a Lung Cancer Screening Population

BACKGROUND

A successful lung cancer screening program requires a patient cohort at sufficient risk of developing cancer who are willing to participate. Among other factors, a patient's lung cancer risk perception may inform their attitudes toward screening and smoking cessation programs.

RESEARCH QUESTION

This study analyzed data from the Pan-Canadian Early Detection of Lung Cancer (PanCan) Study to address the following questions: Which factors are associated with the perception of lung cancer risk? Is there an association between risk perception for lung cancer and actual calculated risk? Is there an association between risk perception for lung cancer and the intent to quit smoking? Are there potential targets for lung cancer screening awareness?

STUDY DESIGN AND METHODS

The PanCan study recruited current or former smokers aged 50 to 75 years who had at least a 2% risk of developing lung cancer over 6 years to undergo low-dose screening CT. Risk perception and worry about lung cancer were captured on a baseline questionnaire. Cumulative logistic regression analysis was used to assess the relationship between baseline risk variables and both lung cancer risk perception and worry.

RESULTS

Among the 2,514 individuals analyzed, a higher perceived risk of lung cancer was positively associated with calculated risk (P = .032). Younger age, being a former smoker, respiratory symptoms, lower FEV₁, COPD, and a family history of lung cancer were associated with higher perceived risk. Conversely, a consistent relationship between calculated risk and worry was not identified. There was a positive association between risk perception and lung cancer worry and reported intent to quit smoking.

INTERPRETATION

Individuals' lung cancer risk perception correlated positively with calculated risk in a screening population. Promotion of screening programs may benefit from focusing on factors associated with higher risk perception; conversely, harnessing worry seemingly holds less value.

2020 Innovation-Based Optimism for Lung Cancer Outcomes

Lung cancer is the leading cause of cancer death in both males and females in the U.S. and worldwide. Owing to advances in prevention, screening/early detection, and therapy, lung cancer mortality rates are decreasing and survival rates are increasing. These innovations are based on scientific discoveries in imaging, diagnostics, genomics, molecular therapy, and immunotherapy. Outcomes have improved in all histologies and stages. This review provides information on the clinical implications of these innovations that are practical for the practicing physicians, especially oncologists of all specialities who diagnose and treat patients with lung cancer. IMPLICATIONS FOR PRACTICE: Lung cancer survival rates have improved because of new prevention, screening, and therapy methods. This work provides a review of current standards for each of these areas, including targeted and immunotherapies. Treatment recommendations are provided for all stages of lung cancer.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.