Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom

Using Routine Data to Improve Lesbian, Gay, Bisexual, and Transgender Health

The collection of sexual orientation in routine data, generated either from contacts with health services or in infrastructure data resources designed and collected for policy and research, has improved substantially in the United Kingdom in the last decade. Inclusive measures of gender and transgender status are now also beginning to be collected. This viewpoint considers current data collections, and their strengths and limitations, including accessing data, sample size, measures of sexual orientation and gender, measures of health outcomes, and longitudinal follow-up. The available data are considered within both sociopolitical and biomedical models of health for individuals who are lesbian, gay, bisexual, transgender, queer, or of other identities including nonbinary (LGBTQ+). Although most individual data sets have some methodological limitations, when put together, there is now a real depth of routine data for LGBTQ+ health research. This paper aims to provide a framework for how these data can be used to improve health and health care outcomes. Four practical analysis approaches are introduced-descriptive epidemiology, risk prediction, intervention development, and impact evaluation-and are discussed as frameworks for translating data into research with the potential to improve health.

Improving Lung Cancer Screening Selection: The HUNT Lung Cancer Risk Model for Ever-Smokers Versus the NELSON and 2021 United States Preventive Services Task Force Criteria in the Cohort of Norway: A Population-Based Prospective Study

Background

Improving the method for selecting participants for lung cancer (LC) screening is an urgent need. Here, we compared the performance of the Helseundersøkelsen i Nord-Trøndelag (HUNT) Lung Cancer Model (HUNT LCM) versus the Dutch-Belgian lung cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON)) and 2021 United States Preventive Services Task Force (USPSTF) criteria regarding LC risk prediction and efficiency.

Methods

We used linked data from 10 Norwegian prospective population-based cohorts, Cohort of Norway. The study included 44,831 ever-smokers, of which 686 (1.5%) patients developed LC; the median follow-up time was 11.6 years (0.01-20.8 years).

Results

Within 6 years, 222 (0.5%) individuals developed LC. The NELSON and 2021 USPSTF criteria predicted 37.4% and 59.5% of the LC cases, respectively. By considering the same number of individuals as the NELSON and 2021 USPSTF criteria selected, the HUNT LCM increased the LC prediction rate by 41.0% and 12.1%, respectively. The HUNT LCM significantly increased sensitivity (p < 0.001 and p = 0.028), and reduced the number needed to predict one LC case (29 versus 40, p < 0.001 and 36 versus 40, p = 0.02), respectively. Applying the HUNT LCM 6-year 0.98% risk score as a cutoff (14.0% of ever-smokers) predicted 70.7% of all LC, increasing LC prediction rate with 89.2% and 18.9% versus the NELSON and 2021 USPSTF, respectively (both p < 0.001).

Conclusions

The HUNT LCM was significantly more efficient than the NELSON and 2021 USPSTF criteria, improving the prediction of LC diagnosis, and may be used as a validated clinical tool for screening selection.

Lung Cancer Risk Prediction Models for Asian Ever-Smokers

INTRODUCTION

Although lung cancer prediction models are widely used to support risk-based screening, their performance outside Western populations remains uncertain. This study aims to evaluate the performance of 11 existing risk prediction models in multiple Asian populations and to refit prediction models for Asians.

METHODS

In a pooled analysis of 186,458 Asian ever-smokers from 19 prospective cohorts, we assessed calibration (expected-to-observed ratio) and discrimination (area under the receiver operating characteristic curve [AUC]) for each model. In addition, we developed the "Shanghai models" to better refine risk models for Asians on the basis of two well-characterized population-based prospective cohorts and externally validated them in other Asian cohorts.

RESULTS

Among the 11 models, the Lung Cancer Death Risk Assessment Tool yielded the highest AUC (AUC [95% confidence interval (CI)] = 0.71 [0.67-0.74] for lung cancer death and 0.69 [0.67-0.72] for lung cancer incidence) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model had good calibration overall (expected-to-observed ratio [95% CI] = 1.06 [0.90-1.25]). Nevertheless, these models substantially underestimated lung cancer risk among Asians who reported less than 10 smoking pack-years or stopped smoking more than or equal to 20 years ago. The Shanghai models were found to have marginal improvement overall in discrimination (AUC [95% CI] = 0.72 [0.69-0.74] for lung cancer death and 0.70 [0.67-0.72] for lung cancer incidence) but consistently outperformed the selected Western models among low-intensity smokers and long-term quitters.

CONCLUSIONS

The Shanghai models had comparable performance overall to the best existing models, but they improved much in predicting the lung cancer risk of low-intensity smokers and long-term quitters in Asia.

Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers

Current lung cancer screening (LCS) guidelines rely on age and smoking history. Despite its benefit, only 5%-15% of eligible patients receive LCS. Personalized screening strategies select individuals based on their lung cancer risk and may increase LCS's effectiveness. We assess current LCS practices and the acceptability of personalized LCS among primary care providers (PCP) in Texas. We surveyed 32,983 Texas-based PCPs on an existing network (Protocol 2019-1257; PI: Dr. Shete) and 300 attendees of the 2022 Texas Academy of Family Physicians (TAFP) conference. We analyzed the responses by subgroups of interest. Using nonparametric bootstrap, we derived an enriched dataset to develop logistic regression models to understand current LCS practices and acceptability of personalized LCS. Response rates were 0.3% (n = 91) and 15% (n = 60) for the 2019-1257 and TAFP surveys, respectively. Most (84%) respondents regularly assess LCS in their practice. Half of the respondents were interested in adopting personalized LCS. The majority (66%) of respondents expressed concerns regarding time availability with the personalized LCS. Most respondents would use biomarkers as an adjunct to assess eligibility (58%), or to help guide indeterminate clinical findings (63%). There is a need to enhance the engagement of Texas-based PCPs in LCS. Most of the respondents expressed interest in personalized LCS. Time availability was the main concern related to personalized LCS. Findings from this project highlight the need for better education of Texas-based PCPs on the benefits of LCS, and the development of efficient decision tools to ensure successful implementation of personalized LCS.

PREVENTION RELEVANCE

Personalized LCS facilitated by a risk model and/or a biomarker test is proposed as an alternative to existing programs. Acceptability of personalized approach among PCPs is unknown. The goal of this study is to assess the acceptability of personalized LCS among PCPs.

Association of Metabolic Syndrome With Risk of Lung Cancer: A Population-Based Prospective Cohort Study

BACKGROUND

Both the incidence of lung cancer and the prevalence of metabolic syndrome (MetS) have been increasing worldwide. The relationship between MetS and lung cancer remains controversial.

RESEARCH QUESTION

What is the risk of lung cancer associated with MetS and its components?

STUDY DESIGN AND METHODS

Multivariable Cox regression models were used to estimate the hazard ratio (HR) of MetS-related variables on lung cancer risk, both overall and by histologic subtype, in the UK Biobank. Stratified analyses were conducted by sex, tobacco use status, and use of medication. HR curves were used to test the nonlinear associations between the metabolic markers and the risk of lung cancer.

RESULTS

Of the 331,877 participants included in this study, a total of 77,173 participants had a diagnosis of MetS at enrollment. During a median follow-up of 10.9 years, lung cancer as the primary site developed in 2,425 participants. The HRs of MetS were 1.21 (95% CI, 1.09-1.33), 1.28 (95% CI, 1.10-1.50), and 1.16 (95% CI, 0.94-1.44) for the overall risk of lung cancer, adenocarcinoma, and squamous cell carcinoma, respectively. The HRs increased with the number of metabolic abnormalities from 1.11 to approximately 1.4 or 1.5 for those with one to five disorders. Positive association with lung cancer was observed for low high-density lipoprotein cholesterol (HDL-C), elevated waist circumference, and hyperglycemia. The relationship between MetS and lung cancer was modified by sex, with a stronger effect in female patients (P = .031). The risk of lung cancer resulting from MetS was elevated mainly among individuals who used tobacco, although the modification effect of tobacco use was not statistically significant. A nonlinear association was found between lung cancer and HDL-C, waist circumference, and glycated hemoglobin.

INTERPRETATION

The increased risk of lung cancer associated with MetS suggests the importance of taking metabolic status and markers into consideration for the primary prevention of lung cancer and the selection of high-risk populations for lung cancer screening.

Clinical Adjuncts to Lung Cancer Screening: A Narrative Review

The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.

Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study

BACKGROUND

Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening.

METHODS AND FINDINGS

For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts.

CONCLUSIONS

We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.

Estimating the Effectiveness of Kidney Cancer Screening Within Lung Cancer Screening Programmes: A Validation in UK Biobank

In the absence of population-based screening, addition of screening for kidney cancer to lung cancer screening could provide an efficient and low-resource means to improve early detection. In this study, we used the UK Biobank cohort (n = 442 865) to determine the performance of the Yorkshire Lung Cancer Screening Trial (YLST) eligibility criteria for selecting individuals for kidney cancer screening. We measured the performance of two models widely used to determine eligibility for lung cancer screening (PLCO[m2012] and the Liverpool-Lung-Project-v2) and the performance of the combined YLST criteria. We found that the lung cancer models have discrimination (area under the receiver operating curve) between 0.60 and 0.68 for kidney cancer. In the UK, one in four cases (25%) of kidney cancer cases is expected to occur in those eligible for lung cancer screening, and one case of kidney cancer detected for every 200 people invited to lung cancer screening. These results suggest that adding kidney cancer screening to lung cancer screening would be an effective strategy to improve early detection rates of kidney cancer. However, most kidney cancers would not be picked up by this approach. This analysis does not address other important considerations about kidney cancer screening, such as overdiagnosis. PATIENT SUMMARY: It has been proposed that adding-on kidney cancer screening to lung cancer screening (both carried out by a computed tomography scan of the chest/abdomen) would be an easy and low-cost way of detecting cases of kidney cancer earlier, when these can be treated more easily. Lung cancer screening is usually targeted at people who are at a high risk (eg, older smokers); therefore, here we look at whether the same group of people are also at a high risk of kidney cancer. Our analysis shows that one in four people later diagnosed with kidney cancer are also at a high risk of lung cancer; hence, a combined screening programme could detect up to a quarter of kidney cancers.

Stroke burden and attributable risk factors in China, 1990-2019

Background and purpose

Understanding the temporal trends of stroke burden and its attributable risk factors are essential for targeted prevention strategies. We aimed to describe the temporal trends and attributable risk factors of stroke in China.

Methods

Data on the stroke burden [incidence, prevalence, mortality, and disability-adjusted life years (DALYs)] and the population-attributable fraction for stroke risk factors from 1990 to 2019 were obtained from the Global Burden of Disease Study 2019 (GBD 2019). We analyzed trends in the burden of stroke and its attributable risk factors from 1990 to 2019, and the characteristics of stroke-attributable risk factors by sex, age group, and stroke subtype.

Results

From 1990 to 2019, the age-standardized incidence, mortality, and DALY rates for total stroke decreased by 9.3% (3.3, 15.5), 39.8% (28.6, 50.7), and 41.6% (30.7, 50.9) respectively. The corresponding indicators all decreased for intracerebral hemorrhage and subarachnoid hemorrhage. The age-standardized incidence rate of ischemic stroke increased by 39.5% (33.5 to 46.2) for male patients and by 31.4% (24.7 to 37.7) for female patients, and the age-standardized mortality and DALY rates remained almost unchanged. The three leading stroke risk factors were high systolic blood pressure, ambient particulate matter pollution, and smoking. High systolic blood pressure has remained the leading risk factor since 1990. The attributable risk of ambient particulate matter pollution shows a clear upward trend. Smoking and alcohol consumption were important risk factors for men.

Conclusion

This study reinforced the findings of an increased stroke burden in China. Precise stroke prevention strategies are needed to reduce the disease burden of stroke.

Public Preferences for Determining Eligibility for Screening in Risk-Stratified Cancer Screening Programs: A Discrete Choice Experiment

BACKGROUND

Risk stratification has been proposed to improve the efficiency of population-level cancer screening. We aimed to describe and quantify the relative importance of different attributes of potential screening programs among the public, focusing on stratifying eligibility.

METHODS

We conducted a discrete choice experiment in which respondents selected between 2 hypothetical screening programs in a series of 9 questions. We presented the risk factors used to determine eligibility (age, sex, or lifestyle or genetic risk scores) and anticipated outcomes based on eligibility criteria with different sensitivity and specificity levels. We performed conditional logit regression models and used the results to estimate preferences for different approaches. We also analyzed free-text comments on respondents' views on the programs.

RESULTS

A total of 1,172 respondents completed the survey. Sensitivity was the most important attribute (7 and 11 times more important than specificity and risk factors, respectively). Eligibility criteria based on age and sex or genetics were preferred over age alone and lifestyle risk scores. Phenotypic and polygenic risk prediction models would be more acceptable than screening everyone aged 55 to 70 y if they had high discrimination (area under the receiver-operating characteristic curve ≥0.75 and 0.80, respectively).

LIMITATIONS

Although our sample was representative with respect to age, sex, and ethnicity, it may not be representative of the UK population regarding other important characteristics. Also, some respondents may have not understood all the information provided to inform decision making.

CONCLUSIONS

The public prioritized lives saved from cancer over reductions in numbers screened or experiencing unnecessary follow-up. Incorporating personal-level risk factors into screening eligibility criteria is acceptable to the public if it increases sensitivity; therefore, maximizing sensitivity in model development and communication could increase uptake.

HIGHLIGHTS

The public prioritized lives saved when considering changing from age-based eligibility criteria to risk-stratified cancer screening over reductions in numbers of people being screened or experiencing unnecessary follow-up.The risk stratification strategy used to do this was the least important component, although age plus sex or genetics were relatively preferable to using age alone and lifestyle risk scores.Communication strategies that emphasize improvements in the numbers of cancers detected or not missed across the population are more likely to be salient than reductions in unnecessary investigations or follow-up among some groups.Future research should focus on developing implementation strategies that maximize gains in sensitivity within the context of resource constraints and how to present attributes relating to specificity to facilitate understanding and informed decision making.

Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

BACKGROUND

Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models.

METHODS

For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R²_D]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP]_v2, LLP_v3, Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO]_M2012, PLCO_M2014, Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria.

FINDINGS

There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R²_D in both sexes in the QResearch validation cohort and 59% of the R²_D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLP_v2 and PLCO_M2012), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk.

INTERPRETATION

The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme.

FUNDING

Innovate UK (UK Research and Innovation).

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

Evaluation of the accuracy of the PLCOm2012 6-year lung cancer risk prediction model among smokers in the CARTaGENE population-based cohort

BACKGROUND

The PLCO_m2012 prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCO_m2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies.

METHODS

We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCO_m2012 calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCO_m2012 detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec's pilot program (for people aged 55-74 yr and 50-74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively.

RESULTS

Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCO_m2012 tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59-0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679-0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%-59.8%) to 44.9% (95% CI 37.4%-52.6%), specificities ranged from 81.6% (95% CI 80.8%-82.3%) to 87.7% (95% CI 87.0%-88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%-5.1%) to 5.3% (95% CI 4.2%-6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCO_m2012 at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCO_m2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%).

INTERPRETATION

In a cohort of Quebec smokers, the PLCO_m2012 risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution.

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.

Economic impact of using risk models for eligibility selection to the International lung screening Trial

OBJECTIVES

Using risk models as eligibility criteria for lung screening can reduce race and sex-based disparities. We used data from the International Lung Screening Trial(ILST; NCT02871856) to compare the economic impact of using the PLCOm2012 risk model or the US Preventative Services' categorical age-smoking history-based criteria (USPSTF-2013).

MATERIALS AND METHODS

The cost-effectiveness of using PLCOm2012 versus USPSTF-2013 was evaluated with a decision analytic model based on the ILST and other screening trials. The primary outcomes were costs in 2020 International Dollars ($), quality-adjusted life-years (QALY) and incremental net benefit (INB, in $ per QALY). Secondary outcomes were selection characteristics and cancer detection rates (CDR).

RESULTS

Compared with the USPSTF-2013 criteria, the PLCOm2012 risk model resulted in $355 of cost savings per 0.2 QALYs gained (INB=$4294 at a willingness-to-pay threshold of $20 000/QALY (95 %CI: $4205-$4383). Using the risk model was more cost-effective in females at both a 1.5 % and 1.7 % 6-year risk threshold (INB=$6616 and $6112, respectively), compared with males ($5221 and $695). The PLCOm2012 model selected more females, more individuals with fewer years of formal education, and more people with other respiratory illnesses in the ILST. The CDR with the risk model was higher in females compared with the USPSTF-2013 criteria (Risk Ratio = 7.67, 95 % CI: 1.87-31.38).

CONCLUSION

The PLCOm2012 model saved costs, increased QALYs and mitigated socioeconomic and sex-based disparities in access to screening.

OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations

BACKGROUND

A reliable risk prediction model is critically important for identifying individuals with high risk of developing lung cancer as candidates for low-dose chest computed tomography (LDCT) screening. Leveraging a cutting-edge machine learning technique that accommodates a wide list of questionnaire-based predictors, we sought to optimize and validate a lung cancer prediction model.

METHODS

We developed an Optimized early Warning model for Lung cancer risk (OWL) using the XGBoost algorithm with 323,344 participants from the England area in UK Biobank (training set), and independently validated it with 93,227 participants from UKB Scotland and Wales area (validation set 1), as well as 70,605 and 66,231 participants in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial (PLCO) control and intervention subpopulations, respectively (validation sets 2 & 3) and 23,138 and 18,669 participants in the United States National Lung Screening Trial (NLST) control and intervention subpopulations, respectively (validation sets 4 & 5). By comparing with three competitive prediction models, i.e., PLCO modified 2012 (PLCO_m2012), PLCO modified 2014 (PLCO_all2014), and the Liverpool Lung cancer Project risk model version 3 (LLPv3), we assessed the discrimination of OWL by the area under receiver operating characteristic curve (AUC) at the designed time point. We further evaluated the calibration using relative improvement in the ratio of expected to observed lung cancer cases (RI_EO), and illustrated the clinical utility by the decision curve analysis.

FINDINGS

For general population, with validation set 1, OWL (AUC = 0.855, 95% CI: 0.829-0.880) presented a better discriminative capability than PLCO_all2014 (AUC = 0.821, 95% CI: 0.794-0.848) (p < 0.001); with validation sets 2 & 3, AUC of OWL was comparable to PLCO_all2014 (AUC_PLCOall2014-AUC_OWL < 1%). For ever-smokers, OWL outperformed PLCO_m2012 and PLCO_all2014 among ever-smokers in validation set 1 (AUC_OWL = 0.842, 95% CI: 0.814-0.871; AUC_PLCOm2012 = 0.792, 95% CI: 0.760-0.823; AUC_PLCOall2014 = 0.791, 95% CI: 0.760-0.822, all p < 0.001). OWL remained comparable to PLCO_m2012 and PLCO_all2014 in discrimination (AUC difference from -0.014 to 0.008) among the ever-smokers in validation sets 2 to 5. In all the validation sets, OWL outperformed LLPv3 among the general population and the ever-smokers. Of note, OWL showed significantly better calibration than PLCO_m2012, PLCO_all2014 (RI_EO from 43.1% to 92.3%, all p < 0.001), and LLPv3 (RI_EO from 41.4% to 98.7%, all p < 0.001) in most cases. For clinical utility, OWL exhibited significant improvement in average net benefits (NB) over PLCO_all2014 in validation set 1 (NB improvement: 32, p < 0.001); among ever smokers of validation set 1, OWL (average NB = 289) retained significant improvement over PLCO_m2012 (average NB = 213) (p < 0.001). OWL had equivalent NBs with PLCO_m2012 and PLCO_all2014 in PLCO and NLST populations, while outperforming LLPv3 in the three populations.

INTERPRETATION

OWL, with a high degree of predictive accuracy and robustness, is a general framework with scientific justifications and clinical utility that can aid in screening individuals with high risks of lung cancer.

FUNDING

National Natural Science Foundation of China, the US NIH.

[China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2023 Version)]

Lung cancer is the leading cause of cancer-related death in China. The effectiveness of low-dose computed tomography (LDCT) screening has been further validated in recent years, and significant progress has been made in research on identifying high-risk individuals, personalizing screening interval, and management of screen-detected findings. The aim of this study is to revise China national lung cancer screening guideline with LDCT (2018 version). The China Lung Cancer Early Detection and Treatment Expert Group (CLCEDTEG) designated by the China's National Health Commission, and China Lung Oncolgy Group experts, jointly participated in the revision of Chinese lung cancer screening guideline (2023 version). This revision is based on the recent advances in LDCT lung cancer screening at home and abroad, and the epidemiology of lung cancer in China. The following aspects of the guideline were revised: (1) lung cancer risk factors besides smoking were considered for the identification of high risk population; (2) LDCT scan parameters were further classified; (3) longer screening interval is recommended for individuals who had negative LDCT screening results for two consecutive rounds; (4) the follow-up interval for positive nodules was extended from 3 months to 6 months; (5) the role of multi-disciplinary treatment (MDT) in the management of positive nodules, diagnosis and treatment of lung cancer were emphasized. This revision clarifies the screening, intervention and treatment pathways, making the LDCT screening guideline more appropriate for China. Future researches based on emerging technologies, including biomarkers and artificial intelligence, are needed to optimize LDCT screening in China in the future.
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Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program

The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize low-dose CT (LDCT) lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1161 proteins in a nested-case control study within 2 prospective cohorts (n = 252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n = 479 cases and 479 controls). Eligible participants had a current or former history of smoking and cases were diagnosed up to 3 years following blood draw. The Nodule Malignancy project measured 1078 proteins among participants with a heavy smoking history within four LDCT screening studies (n = 425 cases diagnosed up to 5 years following blood draw, 430 benign-nodule controls, and 398 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n = 1696 cases and 2926 subcohort representatives), and in the Nodule Malignancy project within five LDCT screening studies (n = 675 cases, 680 benign-nodule controls, and 648 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.

Comparison of discrimination performance of 11 lung cancer risk models for predicting lung cancer in a prospective cohort of screening-age adults from Germany followed over 17 years

Randomized trials have demonstrated considerable reduction in lung cancer (LC) mortality by screening pre-selected heavy smokers with low-dose computed tomography (LDCT). Newer screening guidelines recommend refined LC risk models for selecting the target population for screening. We aimed to evaluate and compare the discrimination performance of LC risk models and previously used trial criteria in predicting LC incidence and mortality in a large German cohort of screening-age adults. Within ESTHER, a population-based prospective cohort study conducted in Saarland, Germany, 4812 ever smokers aged 50-75 years were followed up with respect to LC incidence and mortality for up to 17 years. We quantified the performance of 11 different LC risk models by the area under the curve (AUC) and compared the proportion of correctly predicted LC cases between the best performing models and the LDCT trial criteria. Risk prediction of LC incidence in the ESTHER ever smokers was best for the Bach model, LCRAT and LCDRAT with AUCs ranging from 0.782 to 0.787, from 0.770 to 0.774, and from 0.765 to 0.771 for the follow-up time periods of cases identified at 6, 11, and 17 years, respectively. At cutoffs yielding comparable positivity rates as the LDCT trial criteria, these models would have identified between 11.8 (95% CI 3.0-20.5) and 17.6 (95% CI 10.1-25.2) percent units higher proportions of LC cases occurring during the initial 6 years of follow-up. Use of LC risk models is expected to result in substantially greater potential to identify people at highest risk of LC, suggesting enhanced potential for reducing LC mortality by LC screening.

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.

Explaining differences in the frequency of lung cancer detection between the National Lung Screening Trial and community-based screening in Manchester, UK

BACKGROUND

The frequency of lung cancer detection in the Manchester Lung Health Checks (MLHCs), a community-based screening service, was higher than in the National Lung Screening Trial (NLST) over two screening rounds. We aimed to identify the potential reasons for this difference.

METHODS

We analyzed individual-level data from NLST and MLHCs, restricting to MLHCs participants who met NLST eligibility criteria. We calculated 'detection ratios' comparing the frequency of lung cancer detection in MLHCs vs NLST, first after excluding NLST participants ineligible by MLHC eligibility criteria (6-year lung cancer risk ≥ 1.51 %), and then after standardization to remove the influence of different distributions of baseline lung cancer risk.

RESULTS

Among the 1,079 MLHCs participants who met NLST eligibility criteria, 4.7% were diagnosed with lung cancer over two screening rounds compared with 1.7% in NLST, giving an initial detection ratio of 2.6 (95%CI 2.2-3.0). This was reduced to 2.2 (95%CI 1.3-2.3) after imposing the MLHCs eligibility criterion on NLST, and further to 1.6 (95%CI 1.2-2.1) after removing the influence of different risk distributions. In stratified analyses, the standardized detection ratio was particularly elevated in individuals who were older, living in areas of high socioeconomic disadvantage, or had an FEV/FVC ratio less than 60.

CONCLUSIONS

The 2.6-fold higher lung cancer detection in the community-based MLHCs vs NLST is partly explained by differences in eligibility criteria and baseline risk distributions. The residual 60% increase may relate to higher detection in certain risk groups, including older participants, those with more obstructive lung disease, and those living in areas of socioeconomic disadvantage.

The Future of Lung Cancer Screening: Current Challenges and Research Priorities

Lung cancer is the leading cause of cancer-related deaths worldwide, primarily because most people present when the stage is too advanced to offer any reasonable chance of cure. Over the last two decades, evidence has accumulated to show that early detection of lung cancer, using low-radiation dose computed tomography, in people at higher risk of the condition reduces their mortality. Many countries are now making progress with implementing programmes, although some have concerns about cost-effectiveness. Lung cancer screening is complex, and many factors influence clinical and cost-effectiveness. It is important to develop strategies to optimise each element of the intervention from selection and participation through optimal scanning, management of findings and treatment. The overall aim is to maximise benefits and minimise harms. Additional integrated interventions must include at least smoking cessation. In this review, we summarize the evidence that has accumulated to guide optimisation of lung cancer screening, discuss the remaining open questions about the best approach and identify potential barriers to successful implementation.

Management of Lung Cancer Screening Results Based on Individual Prediction of Current and Future Lung Cancer Risks

OBJECTIVES

We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and low-dose computed tomography (LDCT) image features into calculations of immediate and next-screen (1-y) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals.

METHODS

We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen result. To develop the abnormal screen portion, we analyzed 18,129 abnormal LDCT results in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n = 649) or at the next screen (n = 235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal).

RESULTS

Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The Lung Cancer Risk Assessment Tool+Computed Tomography (LCRAT+CT) risk model, with a threshold of less than 0.95% cumulative lung cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of less than 1.7% cumulative lung cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol.

CONCLUSIONS

Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.