Cancer Stage Compared With Mortality as End Points in Randomized Clinical Trials of Cancer Screening: A Systematic Review and Meta-Analysis

Importance

Randomized clinical trials of cancer screening typically use cancer-specific mortality as the primary end point. The incidence of stage III-IV cancer is a potential alternative end point that may accelerate completion of randomized clinical trials of cancer screening.

Objective

To compare cancer-specific mortality with stage III-IV cancer as end points in randomized clinical trials of cancer screening.

Design, Setting, and Participants

This meta-analysis included 41 randomized clinical trials of cancer screening conducted in Europe, North America, and Asia published through February 19, 2024. Data extracted included numbers of participants, cancer diagnoses, and cancer deaths in the intervention and comparison groups. For each clinical trial, the effect of screening was calculated as the percentage reduction between the intervention and comparison groups in the incidence of participants with cancer-specific mortality and stage III-IV cancer.

Exposures

Randomization to a cancer screening test or to a comparison group in a clinical trial of cancer screening.

Main Outcomes and Measures

End points of cancer-specific mortality and incidence of stage III-IV cancer were compared using Pearson correlation coefficients with 95% CIs, linear regression, and fixed-effects meta-analysis.

Results

The included randomized clinical trials tested benefits of screening for breast (n = 6), colorectal (n = 11), lung (n = 12), ovarian (n = 4), prostate (n = 4), and other cancers (n = 4). Correlation between reductions in cancer-specific mortality and stage III-IV cancer varied by cancer type (I2 = 65%; P = .02). Correlation was highest for trials that screened for ovarian (Pearson ρ = 0.99 [95% CI, 0.51-1.00]) and lung (Pearson ρ = 0.92 [95% CI, 0.72-0.98]) cancers, moderate for breast cancer (Pearson ρ = 0.70 [95% CI, -0.26 to 0.96]), and weak for colorectal (Pearson ρ = 0.39 [95% CI, -0.27 to 0.80]) and prostate (Pearson ρ = -0.69 [95% CI, -0.99 to 0.81]) cancers. Slopes from linear regression were estimated as 1.15 for ovarian cancer, 0.75 for lung cancer, 0.40 for colorectal cancer, 0.28 for breast cancer, and -3.58 for prostate cancer, suggesting that a given magnitude of reduction in incidence of stage III-IV cancer produced different magnitudes of change in incidence of cancer-specific mortality (P for heterogeneity = .004).

Conclusions and Relevance

In randomized clinical trials of cancer screening, incidence of late-stage cancer may be a suitable alternative end point to cancer-specific mortality for some cancer types, but is not suitable for others. These results have implications for clinical trials of multicancer screening tests.

Implementation considerations for risk-tailored cancer screening in the population: A scoping review

BACKGROUND

Risk-tailored screening has emerged as a promising approach to optimise the balance of benefits and harms of existing population cancer screening programs. It tailors screening (e.g., eligibility, frequency, interval, test type) to individual risk rather than the current one-size-fits-all approach of most organised population screening programs. However, the implementation of risk-tailored cancer screening in the population is challenging as it requires a change of practice at multiple levels i.e., individual, provider, health system levels. This scoping review aims to synthesise current implementation considerations for risk-tailored cancer screening in the population, identifying barriers, facilitators, and associated implementation outcomes.

METHODS

Relevant studies were identified via database searches up to February 2023. Results were synthesised using Tierney et al. (2020) guidance for evidence synthesis of implementation outcomes and a multilevel framework.

RESULTS

Of 4138 titles identified, 74 studies met the inclusion criteria. Most studies in this review focused on the implementation outcomes of acceptability, feasibility, and appropriateness, reflecting the pre-implementation stage of most research to date. Only six studies included an implementation framework. The review identified consistent evidence that risk-tailored screening is largely acceptable across population groups, however reluctance to accept a reduction in screening frequency for low-risk informed by cultural norms, presents a major barrier. Limited studies were identified for cancer types other than breast cancer.

CONCLUSIONS

Implementation strategies will need to address alternate models of delivery, education of health professionals, communication with the public, screening options for people at low risk of cancer, and inequity in outcomes across cancer types.

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.

State of the science and future directions for research on HIV and cancer: Summary of a joint workshop sponsored by IARC and NCI

An estimated 38 million people live with human immunodeficiency virus (HIV) worldwide and are at excess risk for multiple cancer types. Elevated cancer risks in people living with HIV (PLWH) are driven primarily by increased exposure to carcinogens, most notably oncogenic viruses acquired through shared transmission routes, plus acceleration of viral carcinogenesis by HIV-related immunosuppression. In the era of widespread antiretroviral therapy (ART), life expectancy of PLWH has increased, with cancer now a leading cause of co-morbidity and death. Furthermore, the types of cancers occurring among PLWH are shifting over time and vary in their relative burden in different parts of the world. In this context, the International Agency for Research on Cancer (IARC) and the US National Cancer Institute (NCI) convened a meeting in September 2022 of multinational and multidisciplinary experts to focus on cancer in PLWH. This report summarizes the proceedings, including a review of the state of the science of cancer descriptive epidemiology, etiology, molecular tumor characterization, primary and secondary prevention, treatment disparities and survival in PLWH around the world. A consensus of key research priorities and recommendations in these domains is also presented.

Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer

Low-dose computed tomography (LDCT) screening for lung cancer substantially reduces mortality from lung cancer, as revealed in randomized controlled trials and meta-analyses. This review is based on the ninth CT screening symposium of the International Association for the Study of Lung Cancer, which focuses on the major themes pertinent to the successful global implementation of LDCT screening and develops a strategy to further the implementation of lung cancer screening globally. These recommendations provide a 5-year roadmap to advance the implementation of LDCT screening globally, including the following: (1) establish universal screening program quality indicators; (2) establish evidence-based criteria to identify individuals who have never smoked but are at high-risk of developing lung cancer; (3) develop recommendations for incidentally detected lung nodule tracking and management protocols to complement programmatic lung cancer screening; (4) Integrate artificial intelligence and biomarkers to increase the prediction of malignancy in suspicious CT screen-detected lesions; and (5) standardize high-quality performance artificial intelligence protocols that lead to substantial reductions in costs, resource utilization and radiologist reporting time; (6) personalize CT screening intervals on the basis of an individual's lung cancer risk; (7) develop evidence to support clinical management and cost-effectiveness of other identified abnormalities on a lung cancer screening CT; (8) develop publicly accessible, easy-to-use geospatial tools to plan and monitor equitable access to screening services; and (9) establish a global shared education resource for lung cancer screening CT to ensure high-quality reading and reporting.

Age at diagnosis for lung, colon, breast and prostate cancers: An international comparative study

Differences in the average age at cancer diagnosis are observed across countries. We therefore aimed to assess international variation in the median age at diagnosis of common cancers worldwide, after adjusting for differences in population age structure. We used IARC's Cancer Incidence in Five Continents (CI5) Volume XI database, comprising cancer diagnoses between 2008 and 2012 from population-based cancer registries in 65 countries. We calculated crude median ages at diagnosis for lung, colon, breast and prostate cancers in each country, then adjusted for population age differences using indirect standardization. We showed that median ages at diagnosis changed by up to 10 years after standardization, typically increasing in low- and middle-income countries (LMICs) and decreasing in high-income countries (HICs), given relatively younger and older populations, respectively. After standardization, the range of ages at diagnosis was 12 years for lung cancer (median age 61-Bulgaria vs 73-Bahrain), 12 years for colon cancer (60-the Islamic Republic of Iran vs 72-Peru), 10 years for female breast cancer (49-Algeria, the Islamic Republic of Iran, Republic of Korea vs 59-USA and others) and 10 years for prostate cancer (65-USA, Lithuania vs 75-Philippines). Compared to HICs, populations in LMICs were diagnosed with colon cancer at younger ages but with prostate cancer at older ages (both pLMICS-vs-HICs  < 0.001). In countries with higher smoking prevalence, lung cancers were diagnosed at younger ages in both women and men (both pcorr  < 0.001). Female breast cancer tended to be diagnosed at younger ages in East Asia, the Middle East and Africa. Our findings suggest that the differences in median ages at cancer diagnosis worldwide likely reflect population-level variation in risk factors and cancer control measures, including screening.

Kidney Function and Risk of Renal Cell Carcinoma

BACKGROUND

We evaluated the temporal association between kidney function, assessed by estimated glomerular filtration rate (eGFR), and the risk of incident renal cell carcinoma (RCC). We also evaluated whether eGFR could improve RCC risk discrimination beyond established risk factors.

METHODS

We analyzed the UK Biobank cohort, including 463,178 participants of whom 1,447 were diagnosed with RCC during 5,696,963 person-years of follow-up. We evaluated the temporal association between eGFR and RCC risk using flexible parametric survival models, adjusted for C-reactive protein and RCC risk factors. eGFR was calculated from creatinine and cystatin C levels.

RESULTS

Lower eGFR, an indication of poor kidney function, was associated with higher RCC risk when measured up to 5 years prior to diagnosis. The RCC HR per SD decrease in eGFR when measured 1 year before diagnosis was 1.26 [95% confidence interval (95% CI), 1.16-1.37], and 1.11 (95% CI, 1.05-1.17) when measured 5 years before diagnosis. Adding eGFR to the RCC risk model provided a small improvement in risk discrimination 1 year before diagnosis with an AUC of 0.73 (95% CI, 0.67-0.84) compared with the published model (0.69; 95% CI, 0.63-0.79).

CONCLUSIONS

This study demonstrated that kidney function markers are associated with RCC risk, but the nature of these associations are consistent with reversed causality. Markers of kidney function provided limited improvements in RCC risk discrimination beyond established risk factors.

IMPACT

eGFR may be of potential use to identify individuals in the extremes of the risk distribution.

Evaluating multi-cancer early detection tests: an argument for the outcome of recurrence-updated stage

The advent of multi-cancer early detection (MCED) tests has the potential to revolutionise the diagnosis of cancer, improving patient outcomes through early diagnosis and increased use of curative therapies. The ongoing NHS-Galleri trial is evaluating an MCED test developed by GRAIL, and is using as its primary endpoint the absolute incidence of late-stage cancer. Proponents of this outcome argue that if the test reduces the number of patients with advanced, incurable cancer, it can be reasonably assumed to be benefitting patients by reducing cancer mortality. Here, we argue that this assumption may not always hold due to the phenomenon of micro-metastatic disease, and propose an adjustment to the trial outcome so that it may better reflect the expected effect of the test on cancer mortality.

Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools

BACKGROUND

We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test.

METHODS

We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models' sensitivity. All tests were 2-sided.

RESULTS

The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model.

CONCLUSION

Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.

Circulating proteome for pulmonary nodule malignancy

BACKGROUND

Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules.

METHODS

Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated.

RESULTS

We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001).

CONCLUSIONS

Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.

Methods for Using Race and Ethnicity in Prediction Models for Lung Cancer Screening Eligibility

Importance

Using race and ethnicity in clinical prediction models can reduce or inadvertently increase racial and ethnic disparities in medical decisions.

Objective

To compare eligibility for lung cancer screening in a contemporary representative US population by refitting the life-years gained from screening-computed tomography (LYFS-CT) model to exclude race and ethnicity vs a counterfactual eligibility approach that recalculates life expectancy for racial and ethnic minority individuals using the same covariates but substitutes White race and uses the higher predicted life expectancy, ensuring that historically underserved groups are not penalized.

Design, Setting, and Participants

The 2 submodels composing LYFS-CT NoRace were refit and externally validated without race and ethnicity: the lung cancer death submodel in participants of a large clinical trial (recruited 1993-2001; followed up until December 31, 2009) who ever smoked (n = 39 180) and the all-cause mortality submodel in the National Health Interview Survey (NHIS) 1997-2001 participants aged 40 to 80 years who ever smoked (n = 74 842, followed up until December 31, 2006). Screening eligibility was examined in NHIS 2015-2018 participants aged 50 to 80 years who ever smoked. Data were analyzed from June 2021 to September 2022.

Exposure

Including and removing race and ethnicity (African American, Asian American, Hispanic American, White) in each LYFS-CT submodel.

Main Outcomes and Measures

By race and ethnicity: calibration of the LYFS-CT NoRace model and the counterfactual approach (ratio of expected to observed [E/O] outcomes), US individuals eligible for screening, predicted days of life gained from screening by LYFS-CT.

Results

The NHIS 2015-2018 included 25 601 individuals aged 50 to 80 years who ever smoked (2769 African American, 649 Asian American, 1855 Hispanic American, and 20 328 White individuals). Removing race and ethnicity from the submodels underestimated lung cancer death risk (expected/observed [E/O], 0.72; 95% CI, 0.52-1.00) and all-cause mortality (E/O, 0.90; 95% CI, 0.86-0.94) in African American individuals. It also overestimated mortality in Hispanic American (E/O, 1.08, 95% CI, 1.00-1.16) and Asian American individuals (E/O, 1.14, 95% CI, 1.01-1.30). Consequently, the LYFS-CT NoRace model increased Hispanic American and Asian American eligibility by 108% and 73%, respectively, while reducing African American eligibility by 39%. Using LYFS-CT with the counterfactual all-cause mortality model better maintained calibration across groups and increased African American eligibility by 13% without reducing eligibility for Hispanic American and Asian American individuals.

Conclusions and Relevance

In this study, removing race and ethnicity miscalibrated LYFS-CT submodels and substantially reduced African American eligibility for lung cancer screening. Under counterfactual eligibility, no one became ineligible, and African American eligibility increased, demonstrating the potential for maintaining model accuracy while reducing disparities.

Prevalence of oral and blood oncogenic human papillomavirus biomarkers among an enriched screening population: Baseline results of the MOUTH study

BACKGROUND

Human papillomavirus (HPV)-related oropharyngeal cancer screening is being explored in research studies, but strategies to identify an appropriate population are not established. The authors evaluated whether a screening population could be enriched for participants with oncogenic HPV biomarkers using risk factors for oral HPV.

METHODS

Participants were enrolled at Johns Hopkins Hospitals and Mount Sinai Icahn School of Medicine. Eligible participants were either men aged 30 years or older who had two or more lifetime oral sex partners and a personal history of anogenital dysplasia/cancer or partners of patients who had HPV-related cancer. Oral rinse and serum samples were tested for oncogenic HPV DNA, RNA, and E6 or E7 antibodies, respectively. Participants with any biomarker were considered at-risk.

RESULTS

Of 1108 individuals, 7.3% had any oncogenic oral HPV DNA, and 22.9% had serum antibodies for oncogenic HPV E6 or E7. Seventeen participants (1.5%) had both oral and blood biomarkers. HPV type 16 (HPV16) biomarkers were rarer, detected in 3.7% of participants, including 20 with oral HPV16 DNA and 22 with HPV16 E6 serum antibodies (n = 1 had both). In adjusted analysis, living with HIV (adjusted odds ratio, 2.65; 95% CI, 1.60-4.40) and older age (66-86 vs. 24-45 years; adjusted odds ratio, 1.70; 95% CI, 1.07-2.70) were significant predictors of being at risk. Compared with the general population, the prevalence of oral HPV16 (1.8% vs. 0.9%), any oncogenic oral HPV DNA (7.3% vs. 3.5%), and HPV16 E6 antibodies (2.2% vs. 0.3%) was significantly elevated.

CONCLUSIONS

Enrichment by the eligibility criteria successfully identified a population with higher biomarker prevalence, including HPV16 biomarkers, that may be considered for screening trials. Most in this group are still expected to have a low risk of oropharyngeal cancer.

The blood proteome of imminent lung cancer diagnosis

Identification of risk biomarkers may enhance early detection of smoking-related lung cancer. We measured between 392 and 1,162 proteins in blood samples drawn at most three years before diagnosis in 731 smoking-matched case-control sets nested within six prospective cohorts from the US, Europe, Singapore, and Australia. We identify 36 proteins with independently reproducible associations with risk of imminent lung cancer diagnosis (all p < 4 × 10-5). These include a few markers (e.g. CA-125/MUC-16 and CEACAM5/CEA) that have previously been reported in studies using pre-diagnostic blood samples for lung cancer. The 36 proteins include several growth factors (e.g. HGF, IGFBP-1, IGFP-2), tumor necrosis factor-receptors (e.g. TNFRSF6B, TNFRSF13B), and chemokines and cytokines (e.g. CXL17, GDF-15, SCF). The odds ratio per standard deviation range from 1.31 for IGFBP-1 (95% CI: 1.17-1.47) to 2.43 for CEACAM5 (95% CI: 2.04-2.89). We map the 36 proteins to the hallmarks of cancer and find that activation of invasion and metastasis, proliferative signaling, tumor-promoting inflammation, and angiogenesis are most frequently implicated.

Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis

BACKGROUND

To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis.

METHODS

We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only.

FINDINGS

There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10-1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61-0.66), compared with 0.62 (95% CI: 0.59-0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: -0.003 to 0.035).

INTERPRETATION

Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information.

FUNDING

No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry.

Smoking Cessation After Diagnosis of Kidney Cancer Is Associated With Reduced Risk of Mortality and Cancer Progression: A Prospective Cohort Study

PURPOSE

To investigate whether postdiagnosis smoking cessation may affect the risk of death and disease progression in patients with renal cell carcinoma (RCC) who smoked at the time of diagnosis.

METHODS

Two hundred twelve patients with primary RCC were recruited between 2007 and 2016 from the Urological Department in N.N. Blokhin National Medical Research Center of Oncology, Moscow, Russia. Upon enrollment, a structured questionnaire was completed, and the patients were followed annually through 2020 to repeatedly assess their smoking status and disease progression. Survival probabilities and hazards for all-cause and cancer-specific mortality and disease progression were investigated using extended the Kaplan-Meier method, time-dependent Cox proportional hazards regression, and Fine-Gray competing-risk models.

RESULTS

Patients were followed for a median of 8.2 years. During this time, 110 cases of disease progression, 100 total deaths, and 77 cancer-specific deaths were recorded. Eighty-four patients (40%) quit smoking after diagnosis. The total person-years at risk for this analysis were 748.2 for continuing smoking and 611.2 for quitting smoking periods. At 5 years of follow-up, both overall survival (85% v 61%) and progression-free survival (80% v 57%) rates were higher during the quitting than continuing smoking periods (both P < .001). In the multivariable time-dependent models, quitting smoking was associated with lower risk of all-cause mortality (hazard ratio [HR], 0.51; 95% CI, 0.31 to 0.85), disease progression (HR, 0.45; 95% CI, 0.29 to 0.71), and cancer-specific mortality (HR, 0.54; 95% CI, 0.31 to 0.93). The beneficial effect of quitting smoking was evident across all subgroups, including light smokers versus moderate-heavy smokers and those with early-stage versus late-stage tumors.

CONCLUSION

Quitting smoking after RCC diagnosis may significantly improve survival and reduce the risk of disease progression and cancer mortality among patients who smoke.

Assessment of the EarlyCDT-Lung test as an early biomarker of lung cancer in ever-smokers: A retrospective nested case-control study in two prospective cohorts

The EarlyCDT-Lung test is a blood-based autoantibody assay intended to identify high-risk individuals for low-dose computed tomography lung cancer screening. However, there is a paucity of evidence on the performance of the EarlyCDT-Lung test in ever-smokers. We conducted a nested case-control study within two prospective cohorts to evaluate the risk-discriminatory performance of the EarlyCDT-Lung test using prediagnostic blood samples from 154 future lung cancer cases and 154 matched controls. Cases were selected from those who had ever smoked and had a prediagnostic blood sample <3 years prior to diagnosis. Conditional logistic regression was used to estimate the association between EarlyCDT-Lung test results and lung cancer risk. Sensitivity and specificity of the EarlyCDT-Lung test were calculated in all subjects and subgroups based on age, smoking history, lung cancer stage, sample collection time before diagnosis and year of sample collection. The overall lung cancer odds ratios were 0.89 (95% CI: 0.34-2.30) for a moderate risk EarlyCDT-Lung test result and 1.09 (95% CI: 0.48-2.47) for a high-risk test result compared to no significant test result. The overall sensitivity was 8.4% (95% CI: 4.6-14) and overall specificity was 92% (95% CI: 87-96) when considering a high-risk result as positive. Stratified analysis indicated higher sensitivity (17%, 95% CI: 7.2-32.1) in subjects with blood drawn up to 1 year prior to diagnosis. In conclusion, our study does not support a role of the EarlyCDT-Lung test in identifying the high-risk subjects in ever-smokers for lung cancer screening in the EPIC and NSHDS cohorts.

Survival and prognostic factors of early-stage non-small cell lung cancer in Central and Eastern Europe: A prospective cohort study

BACKGROUND

Although early diagnosis and surgical resection of the tumor have been shown to be the most important predictors of lung cancer survival, long-term survival for surgically-resected early-stage lung cancer remains poor.

AIMS

In this prospective study we aimed to investigate the survival and prognostic factors of surgically-resected early-stage non-small cell lung cancer (NSCLC) in Central and Eastern Europe.

METHODS

We recruited 2052 patients with stage I-IIIA NSCLC from 9 centers in Russia, Poland, Serbia, Czech Republic, and Romania, between 2007-2016 and followed them annually through 2020.

RESULTS

During follow-up, there were 1121 deaths (including 730 cancer-specific deaths). Median survival time was 4.9 years, and the 5-year overall survival was 49.5%. In the multivariable model, mortality was increased among older individuals (HR for each 10-year increase: 1.31 [95% CI: 1.21-1.42]), males (HR:1.24 [1.04-1.49]), participants with significant weight loss (HR:1.25 [1.03-1.52]), current smokers (HR:1.30 [1.04-1.62]), alcohol drinkers (HR:1.22 [1.03-1.44]), and those with higher stage tumors (HR stage IIIA vs. I: 5.54 [4.10 - 7.48]). However, education, chronic obstructive pulmonary diseases (COPD), and tumor histology were not associated with risk of death. All baseline indicators of smoking and alcohol drinking showed a dose-dependent association with the risk of cancer-specific mortality. This included pack-years of cigarettes smoked (p-trend = 0.04), quantity of smoking (p-trend = 0.008), years of smoking (p-trend = 0.010), gram-days of alcohol drank (p-trend = 0.002), frequency of drinking (p-trend = 0.006), and years of drinking (p-trend = 0.016).

CONCLUSION

This study shows that the 5-year survival rate of surgically-resected stage I-IIIA NSCLC is still around 50% in Central and Eastern Europe. In addition to non-modifiable prognostic factors, lifetime patterns of smoking and alcohol drinking affected the risk of death and disease progression in a dose-dependent manner in this population.

Abstract 1950: Socioeconomic status and lung cancer incidence: An analysis of data from 15 countries in the Lung Cancer Cohort Consortium

Background: Lung cancer is the leading cause of cancer death worldwide. We explored the relationship between socioeconomic status and lung cancer incidence across world regions, using educational level as a proxy for socioeconomic status.

Methods: We analyzed the harmonized database of the Lung Cancer Cohort Consortium (LC3). The current study included data from 18 prospective cohorts from 15 countries in the US, Europe, Asia, and Australia. Separately for participants who never or currently/formerly smoked, we estimated the association between educational level and incident lung cancer using Cox proportional hazards models. Information on education was harmonized using the International Standard Classification of Education and then modeled as an ordinal variable in 4 categories. Models were adjusted for age, sex, and for participants who currently/formerly smoked, smoking duration, cigarettes per day, and time since cessation.

Results: Among 2.6 million participants from 15 countries, 62,645 developed lung cancer during follow-up (median follow-up = 12.6 years). Among current/former smokers, increased educational level was associated with decreased lung cancer incidence in most cohorts after adjustment for age, sex, and detailed smoking information, with HRs ranging from 0.77 (95%CI: 0.42-1.41) per 1-unit increase in educational level in the Iranian Golestan Cohort Study to 1.02 (95%CI: 0.95-1.09) in the Australian Melbourne Collaborative Cohort Study. When grouping by world region, the association between education and lung cancer incidence among currently/formerly smoking participants was similar for the US (HRpooled=0.88, 95%CI: 0.87-0.89), Europe (HRpooled=0.89, 95%CI: 0.88-0.91), and Asia (HRpooled=0.91, 95%CI: 0.86-0.96), but attenuated in the Australian cohort (HR=1.02, 95%CI: 0.95-1.09). Among never smokers, after adjustment for age and sex, there was no statistically significant association between educational level and lung cancer incidence (p-trend&gt;0.05 in all cohorts), with the exception of the US Southern Community Cohort Study, which comprises primarily African-Americans and showed a HR of 0.75 (95%CI: 0.62-0.90).

Conclusion: Among currently and formerly smoking individuals, higher educational level showed a strikingly consistent decreased risk of incident lung cancer across cohorts from 4 continents, after detailed adjustment for smoking. In contrast, among people who never smoked, there was no association between education and lung cancer incidence in any cohort, with the exception of the Southern Community Cohort Study. Further research is needed to clarify the mechanisms, either related or unrelated to smoking, that contribute to the association between education and lung cancer risk.

Citation Format: Justina U. Onwuka, Hana Zahed, Xiaoshuang Feng, Karine Alcala, Mattias Johansson, Hilary A. Robbins, Lung Cancer Cohort Consortium. Socioeconomic status and lung cancer incidence: An analysis of data from 15 countries in the Lung Cancer Cohort Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1950.

Recalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung Cancer Screening Intervals

Importance

Annual low-dose computed tomographic (LDCT) screening reduces lung cancer mortality, but harms could be reduced and cost-effectiveness improved by reusing the LDCT image in conjunction with deep learning or statistical models to identify low-risk individuals for biennial screening.

Objective

To identify low-risk individuals in the National Lung Screening Trial (NLST) and estimate, had they been assigned a biennial screening, how many lung cancers would have been delayed 1 year in diagnosis.

Design, Setting, and Participants

This diagnostic study included participants with a presumed nonmalignant lung nodule in the NLST between January 1, 2002, and December 31, 2004, with follow-up completed on December 31, 2009. Data were analyzed for this study from September 11, 2019, to March 15, 2022.

Exposures

An externally validated deep learning algorithm that predicts malignancy in current lung nodules using LDCT images (Lung Cancer Prediction Convolutional Neural Network [LCP-CNN]; Optellum Ltd) was recalibrated to predict 1-year lung cancer detection by LDCT for presumed nonmalignant nodules. Individuals with presumed nonmalignant lung nodules were hypothetically assigned annual vs biennial screening based on the recalibrated LCP-CNN model, Lung Cancer Risk Assessment Tool (LCRAT + CT [a statistical model combining individual risk factors and LDCT image features]), and the American College of Radiology recommendations for lung nodules, version 1.1 (Lung-RADS).

Main Outcomes and Measures

Primary outcomes included model prediction performance, the absolute risk of a 1-year delay in cancer diagnosis, and the proportion of people without lung cancer assigned a biennial screening interval vs the proportion of cancer diagnoses delayed.

Results

The study included 10 831 LDCT images from patients with presumed nonmalignant lung nodules (58.7% men; mean [SD] age, 61.9 [5.0] years), of whom 195 were diagnosed with lung cancer from the subsequent screen. The recalibrated LCP-CNN had substantially higher area under the curve (0.87) than LCRAT + CT (0.79) or Lung-RADS (0.69) to predict 1-year lung cancer risk (P < .001). If 66% of screens with nodules were assigned to biennial screening, the absolute risk of a 1-year delay in cancer diagnosis would have been lower for recalibrated LCP-CNN (0.28%) than LCRAT + CT (0.60%; P = .001) or Lung-RADS (0.97%; P < .001). To delay only 10% of cancer diagnoses at 1 year, more people would have been safely assigned biennial screening under LCP-CNN than LCRAT + CT (66.4% vs 40.3%; P < .001).

Conclusions and Relevance

In this diagnostic study evaluating models of lung cancer risk, a recalibrated deep learning algorithm was most predictive of 1-year lung cancer risk and had least risk of 1-year delay in cancer diagnosis among people assigned biennial screening. Deep learning algorithms could prioritize people for workup of suspicious nodules and decrease screening intensity for people with low-risk nodules, which may be vital for implementation in health care systems.

A cross-sectional study of inflammatory markers as determinants of circulating kynurenines in the Lung Cancer Cohort Consortium

Circulating concentrations of metabolites (collectively called kynurenines) in the kynurenine pathway of tryptophan metabolism increase during inflammation, particularly in response to interferon-gamma (IFN-γ). Neopterin and the kynurenine/tryptophan ratio (KTR) are IFN-γ induced inflammatory markers, and together with C-reactive protein (CRP) and kynurenines they are associated with various diseases, but comprehensive data on the strength of associations of inflammatory markers with circulating concentrations of kynurenines are lacking. We measured circulating concentrations of neopterin, CRP, tryptophan and seven kynurenines in 5314 controls from 20 cohorts in the Lung Cancer Cohort Consortium (LC3). The associations of neopterin, KTR and CRP with kynurenines were investigated using regression models. In mixed models, one standard deviation (SD) higher KTR was associated with a 0.46 SD higher quinolinic acid (QA), and 0.31 SD higher 3-hydroxykynurenine (HK). One SD higher neopterin was associated with 0.48, 0.44, 0.36 and 0.28 SD higher KTR, QA, kynurenine and HK, respectively. KTR and neopterin respectively explained 24.1% and 16.7% of the variation in QA, and 11.4% and 7.5% of HK. CRP was only weakly associated with kynurenines in regression models. In summary, QA was the metabolite that was most strongly associated with the inflammatory markers. In general, the inflammatory markers were most strongly related to metabolites located along the tryptophan-NAD axis, which may support suggestions of increased production of NAD from tryptophan during inflammation.

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.

Absolute Risk of Oropharyngeal Cancer After an HPV16-E6 Serology Test and Potential Implications for Screening: Results From the Human Papillomavirus Cancer Cohort Consortium

PURPOSE

Seropositivity for the HPV16-E6 oncoprotein is a promising marker for early detection of oropharyngeal cancer (OPC), but the absolute risk of OPC after a positive or negative test is unknown.

METHODS

We constructed an OPC risk prediction model that integrates (1) relative odds of OPC for HPV16-E6 serostatus and cigarette smoking from the human papillomavirus (HPV) Cancer Cohort Consortium (HPVC3), (2) US population risk factor data from the National Health Interview Survey, and (3) US sex-specific population rates of OPC and mortality.

RESULTS

The nine HPVC3 cohorts included 365 participants with OPC with up to 10 years between blood draw and diagnosis and 5,794 controls. The estimated 10-year OPC risk for HPV16-E6 seropositive males at age 50 years was 17.4% (95% CI, 12.4 to 28.6) and at age 60 years was 27.1% (95% CI, 19.2 to 45.4). Corresponding 5-year risk estimates were 7.3% and 14.4%, respectively. For HPV16-E6 seropositive females, 10-year risk estimates were 3.6% (95% CI, 2.5 to 5.9) at age 50 years and 5.5% (95% CI, 3.8 to 9.2) at age 60 years and 5-year risk estimates were 1.5% and 2.7%, respectively. Over 30 years, after a seropositive result at age 50 years, an estimated 49.9% of males and 13.3% of females would develop OPC. By contrast, 10-year risks among HPV16-E6 seronegative people were very low, ranging from 0.01% to 0.25% depending on age, sex, and smoking status.

CONCLUSION

We estimate that a substantial proportion of HPV16-E6 seropositive individuals will develop OPC, with 10-year risks of 17%-27% for males and 4%-6% for females age 50-60 years in the United States. This high level of risk may warrant periodic, minimally invasive surveillance after a positive HPV16-E6 serology test, particularly for males in high-incidence regions. However, an appropriate clinical protocol for surveillance remains to be established.

The influence of postscreening follow-up time and participant characteristics on estimates of overdiagnosis from lung cancer screening trials

We aimed to explore the underlying reasons that estimates of overdiagnosis vary across and within low-dose computed tomography (LDCT) lung cancer screening trials. We conducted a systematic review to identify estimates of overdiagnosis from randomised controlled trials of LDCT screening. We then analysed the association of Ps (the excess incidence of lung cancer as a proportion of screen-detected cases) with postscreening follow-up time using a linear random effects meta-regression model. Separately, we analysed annual Ps estimates from the US National Lung Screening Trial (NLST) and German Lung Cancer Screening Intervention Trial (LUSI) using exponential decay models with asymptotes. We conducted stratified analyses to investigate participant characteristics associated with Ps using the extended follow-up data from NLST. Among 12 overdiagnosis estimates from 8 trials, the postscreening follow-up ranged from 3.8 to 9.3 years, and Ps ranged from -27.0% (ITALUNG, 8.3 years follow-up) to 67.2% (DLCST, 5.0 years follow-up). Across trials, 39.1% of the variation in Ps was explained by postscreening follow-up time. The annual changes in Ps were -3.5% and -3.9% in the NLST and LUSI trials, respectively. Ps was predicted to plateau at 2.2% for NLST and 9.2% for LUSI with hypothetical infinite follow-up. In NLST, Ps increased with age from -14.9% (55-59 years) to 21.7% (70-74 years), and time trends in Ps varied by histological type. The findings suggest that differences in postscreening follow-up time partially explain variation in overdiagnosis estimates across lung cancer screening trials. Estimates of overdiagnosis should be interpreted in the context of postscreening follow-up and population characteristics.

Circulating Isovalerylcarnitine and Lung Cancer Risk: Evidence from Mendelian Randomization and Prediagnostic Blood Measurements

BACKGROUND

Tobacco exposure causes 8 of 10 lung cancers, and identifying additional risk factors is challenging due to confounding introduced by smoking in traditional observational studies.

MATERIALS AND METHODS

We used Mendelian randomization (MR) to screen 207 metabolites for their role in lung cancer predisposition using independent genome-wide association studies (GWAS) of blood metabolite levels (n = 7,824) and lung cancer risk (n = 29,266 cases/56,450 controls). A nested case-control study (656 cases and 1,296 matched controls) was subsequently performed using prediagnostic blood samples to validate MR association with lung cancer incidence data from population-based cohorts (EPIC and NSHDS).

RESULTS

An MR-based scan of 207 circulating metabolites for lung cancer risk identified that blood isovalerylcarnitine (IVC) was associated with a decreased odds of lung cancer after accounting for multiple testing (log10-OR = 0.43; 95% CI, 0.29-0.63). Molar measurement of IVC in prediagnostic blood found similar results (log10-OR = 0.39; 95% CI, 0.21-0.72). Results were consistent across lung cancer subtypes.

CONCLUSIONS

Independent lines of evidence support an inverse association of elevated circulating IVC with lung cancer risk through a novel methodologic approach that integrates genetic and traditional epidemiology to efficiently identify novel cancer biomarkers.

IMPACT

Our results find compelling evidence in favor of a protective role for a circulating metabolite, IVC, in lung cancer etiology. From the treatment of a Mendelian disease, isovaleric acidemia, we know that circulating IVC is modifiable through a restricted protein diet or glycine and L-carnatine supplementation. IVC may represent a modifiable and inversely associated biomarker for lung cancer.

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.

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.

Abstract PR-13: Potential effect on racial/ethnic disparities of removing racial/ethnic variables from risk models: The example of lung-cancer screening

Background: Some uses of “race correction” in clinical algorithms and prediction models unfairly reduce access to care, resulting in calls to remove racial/ethnic variables from all models and algorithms. However, for models that are based on unbiased, high-quality, and plentiful data, removing racial/ethnic variables may reduce prediction accuracy for minorities. We model racial/ethnic disparities in screening eligibility from augmenting USPSTF-2021 guidelines (ages 50-80, ≥20 pack-years, ≤15 quit-years) to also include individuals selected by an NCCN-recommended risk model that includes race (PLCOM2012) versus the same model with race/ethnicity removed (PLCOM2012_NoRace). Methods: We used previously published methodology to model the performance of lung cancer screening using 6915 ever-smokers ages 50-80 from the US-representative 2015 National Health Interview Survey (NHIS). Individuals were considered eligible for screening if they are eligible by USPSTF-2021 guidelines or by PLCOM2012 (“USPSTF+PLCOM2012”), versus being eligible by USPSTF-2021 or PLCOM2012_NoRace (“USPSTF+PLCOM2012_NoRace”). Both models used the NCCN-recommended ≥1.3% 6-year risk-threshold for eligibility. We evaluated model accuracy (average percent over/under-estimation) by race/ethnicity, estimated the proportion of life-years gainable achieved by each eligible cohort (LYG), and evaluated the LYG disparity (difference in LYG between whites and each minority). Results: USPSTF+PLCOM2012 and USPSTF+PLCOM2012_NoRace identified similar numbers of minorities as eligible for screening (~2.7 million). However, USPSTF+PLCOM2012_NoRace selected 125% more Hispanic-Americans and 31% less African-Americans. LYG disparities decreased using USPSTF+PLCOM2012_NoRace versus USPSTF+PLCOM2012 for Hispanic Americans (LYG: 33% to 29%). However, LYG disparities for African Americans increased (LYG: 16% to 18%). PLCOM2012 underestimated lung cancer risk by 49% for Hispanic-Americans, whereas PLCOM2012_NoRace performed well (4% overestimation). However, PLCOM2012underestimated risk in African-Americans by only 6%, PLCOM2012_NoRace underestimated risk in African-Americans by 36%. Conclusion: The model that was most accurate for a minority group was projected to reduce disparities the most for that group. Removing race from the PLCOM2012 model substantially underestimated risk for African-Americans and may increase disparities. Inexplicably, PLCOM2012 substantially underestimated risk in Hispanic-Americans despite including race/ethnicity, which was alleviated by removing race/ethnicity. Great care must be taken when removing racial/ethnic variables from models, because this will assign minorities risk estimates that may be largely, or entirely, based on the majority population.

Citation Format: Corey D. Young, Li C. Cheung, Christine D. Berg, Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki, Rebecca Landy. Potential effect on racial/ethnic disparities of removing racial/ethnic variables from risk models: The example of lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR-13.

A modeling analysis to compare eligibility strategies for lung cancer screening in Brazil

BACKGROUND

Country-specific evidence is needed to guide decisions regarding whether and how to implement lung cancer screening in different settings. For this study, we estimated the potential numbers of individuals screened and lung cancer deaths prevented in Brazil after applying different strategies to define screening eligibility.

METHODS

We applied the Lung Cancer Death Risk Assessment Tool (LCDRAT) to survey data on current and former smokers (ever-smokers) in 15 Brazilian state capital cities that comprise 18% of the Brazilian population. We evaluated three strategies to define eligibility for screening: (1) pack-years and cessation time (≥30 pack-years and <15 years since cessation); (2) the LCDRAT risk model with a fixed risk threshold; and (3) LCDRAT with age-specific risk thresholds.

FINDINGS

Among 2.3 million Brazilian ever-smokers aged 55-79 years, 21,459 (95%CI 20,532-22,387) lung cancer deaths were predicted over 5 years without screening. Applying the fixed risk-based eligibility definition would prevent more lung cancer deaths than the pack-years definition [2,939 (95%CI 2751-3127) vs. 2,500 (95%CI 2318-2681) lung cancer deaths], and with higher screening efficiency [NNS=177 (95%CI 170-183) vs. 205 (95%CI 194-216)], but would tend to screen older individuals [mean age 67.8 (95%CI 67.5-68.2) vs. 63.4 (95%CI 63.0-63.9) years]. Applying age-specific risk thresholds would allow younger ever-smokers to be screened, although these individuals would be at lower risk. The age-specific thresholds strategy would avert three-fifths (60.1%) of preventable lung cancer deaths [N = 2629 (95%CI 2448-2810)] by screening 21.9% of ever-smokers.

INTERPRETATION

The definition of eligibility impacts the efficiency of lung cancer screening and the mean age of the eligible population. As implementation of lung screening proceeds in different countries, our analytical framework can be used to guide similar analyses in other contexts. Due to limitations of our models, more research would be needed.

Using Prediction Models to Reduce Persistent Racial and Ethnic Disparities in the Draft 2020 USPSTF Lung Cancer Screening Guidelines

We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung cancer screening recommendations "partially ameliorate racial disparities in screening eligibility" compared with the 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial-like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%-33.4% = 15.0% to 64.5%-48.5% = 16.0%; Asian Americans: 48.3%-35.6% = 12.7% to 64.5%-45.2% = 19.3%; Hispanic Americans: 48.3%-24.8% = 23.5% to 64.5%-37.0% = 27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%-75.5% = 1.2%) and improved screening efficiency for Asian and Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). The draft USPSTF-2020 guidelines increased the number of eligible minorities vs USPSTF-2013 but may inadvertently increase racial and ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit regardless of race and ethnicity.

Epidemiology of 40 blood biomarkers of one-carbon metabolism, vitamin status, inflammation, and renal and endothelial function among cancer-free older adults

Imbalances of blood biomarkers are associated with disease, and biomarkers may also vary non-pathologically across population groups. We described variation in concentrations of biomarkers of one-carbon metabolism, vitamin status, inflammation including tryptophan metabolism, and endothelial and renal function among cancer-free older adults. We analyzed 5167 cancer-free controls aged 40-80 years from 20 cohorts in the Lung Cancer Cohort Consortium (LC3). Centralized biochemical analyses of 40 biomarkers in plasma or serum were performed. We fit multivariable linear mixed effects models to quantify variation in standardized biomarker log-concentrations across four factors: age, sex, smoking status, and body mass index (BMI). Differences in most biomarkers across most factors were small, with 93% (186/200) of analyses showing an estimated difference lower than 0.25 standard-deviations, although most were statistically significant due to large sample size. The largest difference was for creatinine by sex, which was - 0.91 standard-deviations lower in women than men (95%CI - 0.98; - 0.84). The largest difference by age was for total cysteine (0.40 standard-deviation increase per 10-year increase, 95%CI 0.36; 0.43), and by BMI was for C-reactive protein (0.38 standard-deviation increase per 5-kg/m2 increase, 95%CI 0.34; 0.41). For 31 of 40 markers, the mean difference between current and never smokers was larger than between former and never smokers. A statistically significant (p < 0.05) association with time since smoking cessation was observed for 8 markers, including C-reactive protein, kynurenine, choline, and total homocysteine. We conclude that most blood biomarkers show small variations across demographic characteristics. Patterns by smoking status point to normalization of multiple physiological processes after smoking cessation.

Tobacco Smoking and Risk of Second Primary Lung Cancer

INTRODUCTION

Lung cancer survivors are at high risk of developing a second primary lung cancer (SPLC). However, SPLC risk factors have not been established and the impact of tobacco smoking remains controversial. We examined the risk factors for SPLC across multiple epidemiologic cohorts and evaluated the impact of smoking cessation on reducing SPLC risk.

METHODS

We analyzed data from 7059 participants in the Multiethnic Cohort (MEC) diagnosed with an initial primary lung cancer (IPLC) between 1993 and 2017. Cause-specific proportional hazards models estimated SPLC risk. We conducted validation studies using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (N = 3423 IPLC cases) and European Prospective Investigation into Cancer and Nutrition (N = 4731 IPLC cases) cohorts and pooled the SPLC risk estimates using random effects meta-analysis.

RESULTS

Overall, 163 MEC cases (2.3%) developed SPLC. Smoking pack-years (hazard ratio [HR] = 1.18 per 10 pack-years, p < 0.001) and smoking intensity (HR = 1.30 per 10 cigarettes per day, p < 0.001) were significantly associated with increased SPLC risk. Individuals who met the 2013 U.S. Preventive Services Task Force's screening criteria at IPLC diagnosis also had an increased SPLC risk (HR = 1.92; p < 0.001). Validation studies with the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial and European Prospective Investigation into Cancer and Nutrition revealed consistent results. Meta-analysis yielded pooled HRs of 1.16 per 10 pack-years (pmeta < 0.001), 1.25 per 10 cigarettes per day (pmeta < 0.001), and 1.99 (pmeta < 0.001) for meeting the U.S. Preventive Services Task Force's criteria. In MEC, smoking cessation after IPLC diagnosis was associated with an 83% reduction in SPLC risk (HR = 0.17; p < 0.001).

CONCLUSIONS

Tobacco smoking is a risk factor for SPLC. Smoking cessation may reduce the risk of SPLC. Additional strategies for SPLC surveillance and screening are warranted.

Risk factors for melanoma by anatomical site: an evaluation of aetiological heterogeneity

BACKGROUND

Melanoma aetiology has been proposed to have two pathways, which are determined by naevi and type of sun exposure and related to the anatomical site where melanoma develops.

OBJECTIVES

We examined associations with melanoma by anatomical site for a comprehensive set of risk factors including pigmentary and naevus phenotypes, ultraviolet radiation exposure and polygenic risk.

METHODS

We analysed harmonized data from 2617 people with incident first invasive melanoma and 975 healthy controls recruited through two population-based case-control studies in Australia and the UK. Questionnaire data were collected by interview using a single protocol, and pathway-specific polygenic risk scores were derived from DNA samples. We estimated adjusted odds ratios using unconditional logistic regression that compared melanoma cases at each anatomical site with all controls.

RESULTS

When cases were compared with control participants, there were stronger associations for many naevi vs. no naevi for melanomas on the trunk, and upper and lower limbs than on the head and neck (P-heterogeneity < 0·001). Very fair skin (vs. olive/brown skin) was more weakly related to melanoma on the trunk than to melanomas at other sites (P-heterogeneity = 0·04). There was no significant difference by anatomical site for polygenic risk. Increased weekday sun exposure was positively associated with melanoma on the head and neck but not on other sites.

CONCLUSIONS

We found evidence of aetiological heterogeneity for melanoma, supporting the dual pathway hypothesis. These findings enhance understanding of risk factors for melanoma and can guide prevention and skin examination education and practices.

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

BACKGROUND

The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK.

METHODS

We analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC).

RESULTS

Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%).

CONCLUSION

In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.

Validation of multivariable lung cancer risk prediction models for the personalized assignment of optimal screening frequency: a retrospective analysis of data from the German Lung Cancer Screening Intervention Trial (LUSI)

BACKGROUND

Current guidelines for lung cancer screening via low-dose computed tomography recommend annual screening for all candidates meeting basic eligibility criteria. However, lung cancer risk of eligible screening participants can vary widely, and further risk stratification could be used to individually optimize screening intervals in view of expected benefits, possible harms and financial costs. To this effect, models have been developed in the US National Lung Screening Trial based on self-reported lung cancer risk factors and imaging data. We evaluated these models using data from an independent screening trial in Germany.

METHODS

We examined the Polynomial model by Schreuder et al., the Lung Cancer Risk Assessment Tool extended by CT characteristics (LCRAT + CT) by Robbins et al., and a criterion of presence vs. absence of pulmonary nodules ≥4 mm (Patz et al.), applied to sub-sets of screening participants according to eligibility criteria. Discrimination was evaluated via the receiver operating characteristic curve. Delayed diagnoses and false positive results were calculated at various thresholds of predicted risk. Model calibration was assessed by comparing mean predicted risk versus observed incidence.

RESULTS

One thousand five hundred and six participants were eligible for the validation of the LCRAT + CT model, and 1,889 for the validation of the Polynomial model and Patz criterion, yielding areas under the receiver operating characteristic curve of 0.73 (95% CI: 0.63, 0.82), 0.75 (0.67, 0.83), and 0.56 (0.53, 0.72) respectively. Skipping 50% annual screenings (participants within the 5 lowest risk deciles by LCRAT + CT in any round or by the Polynomial model; baseline screening round), would have avoided 75% (21.9%, 98.7%) and 40% (21.8%, 61.1%) false positive screen tests and delayed 10% (1.8%, 33.1%) or no (0%, 32.1%) diagnoses, respectively. Using the Patz criterion, referring 63.2% (61.0% to 65.4%) of participants to biennial screening would have avoided 4% (0.2% to 22.3%) of false positive screen tests but delayed 55% (24.6% to 81.9%) diagnoses.

CONCLUSIONS

In this German trial, the LCRAT + CT and Polynomial models showed useful discrimination of screening participants for one-year lung cancer risk following CT examination. Our results illustrate the remaining heterogeneity in risk within screening-eligible subjects and the trade-off between a low-frequency screening approach and delayed detection.

Abstract PO-247: Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening

Background: For the same age and smoking history as whites, minorities have substantially different lung-cancer risk. However, current US Preventive Services Task Force (USPSTF) lung-cancer screening recommendations make no allowance for race/ethnicity and may induce health disparities. Incorporating individualized prediction-models into USPSTF guidelines may reduce racial/ethnic disparities in lung-cancer screening eligibility. We examine whether expanding current USPSTF lung cancer screening eligibility to include ever-smokers whose risk (calculated by an individualized prediction model) exceeded a threshold would reduce racial/ethnic disparities induced by current USPSTF guidelines. Methods: We used the US- representative 2015 National Health Interview Survey to examine screening eligibility. We identified the thresholds for each of 5 models: lung-cancer risk (Bach, PLCOM2012 and LCRAT models), lung-cancer death risk (LCDRAT model), and life- years gained by attending screening (LYFS-CT model), which select the same number of ever-smokers aged 50-80yrs as USPSTF guidelines. We defined 5 cohorts of ever- smokers as eligible for screening if they were eligible by each screening model or USPSTF guidelines. Among each race/ethnicity, we calculated the number eligible for screening, proportion of preventable lung-cancer deaths prevented (LCD sensitivity), proportion of gainable life-years gained (LYG sensitivity) and screening effectiveness (the number needed to screen to prevent one lung-cancer death). Results: USPSTF criteria performed best for whites (20% eligible, preventing 55% of preventable lung- cancer deaths). Asian-Americans had the least effective screening (NNS=419), only 13% of African-Americans were eligible despite having the most effective screening (NNS=135), and Hispanic-Americans had the lowest percentages eligible (9%) and deaths preventable (30%). Augmenting USPSTF criteria with LCDRAT or LYFS-CT prediction-models nearly equalized the performance of screening for African- Americans with that of whites, doubling the number of African-Americans eligible and increasing the number of preventable deaths and life-years gained by nearly 80%, although at a 25% loss in effectiveness. Prediction-models improved all screening metrics for Asian-Americans and Hispanic-Americans. However models estimated risk more accurately for whites than minorities. Conclusions: Augmenting USPSTF criteria with the LCDRAT or LYFS-CT prediction-models nearly eliminated the white/African-American disparity. All screening metrics were substantially improved for Asian/Hispanic-Americans.

Citation Format: Rebecca Landy, Corey D. Young, Martin Skarzynski, Li C. Cheung, Christine D. Berg, M. Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki. Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-247.

Analysis of lung cancer risk model (PLCOM2012 and LLPv2) performance in a community-based lung cancer screening programme

INTRODUCTION

Low-dose CT (LDCT) screening of high-risk smokers reduces lung cancer (LC) specific mortality. Determining screening eligibility using individualised risk may improve screening effectiveness and reduce harm. Here, we compare the performance of two risk prediction models (PLCOM2012 and Liverpool Lung Project model (LLPv2)) and National Lung Screening Trial (NLST) eligibility criteria in a community-based screening programme.

METHODS

Ever-smokers aged 55-74, from deprived areas of Manchester, were invited to a Lung Health Check (LHC). Individuals at higher risk (PLCOM2012 score ≥1.51%) were offered annual LDCT screening over two rounds. LLPv2 score was calculated but not used for screening selection; ≥2.5% and ≥5% thresholds were used for analysis.

RESULTS

PLCOM2012 ≥1.51% selected 56% (n=1429) of LHC attendees for screening. LLPv2 ≥2.5% also selected 56% (n=1430) whereas NLST (47%, n=1188) and LLPv2 ≥5% (33%, n=826) selected fewer. Over two screening rounds 62 individuals were diagnosed with LC; representing 87% (n=62/71) of 6-year incidence predicted by mean PLCOM2012 score (5.0%). 26% (n=16/62) of individuals with LC were not eligible for screening using LLPv2 ≥5%, 18% (n=11/62) with NLST criteria and 7% (n=5/62) with LLPv2 ≥2.5%. NLST eligible Manchester attendees had 2.5 times the LC detection rate than NLST participants after two annual screens (≈4.3% (n=51/1188) vs 1.7% (n=438/26 309); p<0.0001). Adverse measures of health, including airflow obstruction, respiratory symptoms and cardiovascular disease, were positively correlated with LC risk. Coronary artery calcification was predictive of LC (adjOR 2.50, 95% CI 1.11 to 5.64; p=0.028).

CONCLUSION

Prospective comparisons of risk prediction tools are required to optimise screening selection in different settings. The PLCOM2012 model may underestimate risk in deprived UK populations; further research focused on model calibration is required.

Identification of Candidates for Longer Lung Cancer Screening Intervals Following a Negative Low-Dose Computed Tomography Result

Lengthening the annual low-dose computed tomography (CT) screening interval for individuals at lowest risk of lung cancer could reduce harms and improve efficiency. We analyzed 23 328 participants in the National Lung Screening Trial who had a negative CT screen (no ≥4-mm nodules) to develop an individualized model for lung cancer risk after a negative CT. The Lung Cancer Risk Assessment Tool + CT (LCRAT+CT) updates "prescreening risk" (calculated using traditional risk factors) with selected CT features. At the next annual screen following a negative CT, risk of cancer detection was reduced among the 70% of participants with neither CT-detected emphysema nor consolidation (median risk = 0.2%, interquartile range [IQR] = 0.1%-0.3%). However, risk increased for the 30% with CT emphysema (median risk = 0.5%, IQR = 0.3%-0.8%) and the 0.6% with consolidation (median = 1.6%, IQR = 1.0%-2.5%). As one example, a threshold of next-screen risk lower than 0.3% would lengthen the interval for 57.8% of screen-negatives, thus averting 49.8% of next-screen false-positives among screen-negatives but delaying diagnosis for 23.9% of cancers. Our results support that many, but not all, screen-negatives might reasonably lengthen their CT screening interval.

Insights for Management of Ground-Glass Opacities From the National Lung Screening Trial

BACKGROUND

In the National Lung Screening Trial (NLST), screen-detected cancers that would not have been identified by the Lung Computed Tomographic Screening Reporting and Data System (Lung-RADS) nodule management guidelines were frequently ground-glass opacities (GGOs). Lung-RADS suggests that GGOs with diameter less than 20 mm return for annual screening, and GGOs greater than or equal to 20 mm receive 6-month follow-up. We examined whether this 20-mm threshold gives consistent management of GGOs compared with solid nodules.

METHODS

First, we calculated diameter-specific malignancy probabilities for GGOs and solid nodules in the NLST. Using the solid-nodule malignancy risks as benchmarks, we suggested risk-based management categories for GGOs based on their probability of malignancy. Second, we compared lung-cancer mortality between GGOs and solid nodules in the same risk-based category.

RESULTS

Using the Lung-RADS v1.0 classifications, malignancy probability is higher for GGOs than solid nodules within the same category. A risk-based classification of GGOs would assign annual screening for GGOs 4 to 5 mm (0.4% malignancy risk); 6-month follow-up for GGOs 6 to 7 mm (1.1%), 8 to 14 mm (3.0%), and 15 to 19 mm (5.2%); and 3-month follow-up for greater than or equal to 20 mm (10.9%). This reclassification would have assigned similarly fatal cancers to 3-month follow-up (hazard ratio = 2.0 for lung-cancer death in GGOs versus solid-nodule cancers, 95% confidence interval: 0.4-8.7), but for 6-month follow-up, mortality was lower in GGO cancers (hazard ratio = 0.18, 95% confidence interval: 0.05-0.67).

CONCLUSIONS

If Lung-RADS categories for GGOs were based on malignancy probability, then 6- to 19-mm GGOs would receive 6-month follow-up and greater than or equal to 20-mm GGOs would receive 3-month follow-up. Such risk-based management for GGOs could improve the sensitivity of Lung-RADS, especially for large GGO cancers. However, small GGO cancers were less aggressive than their solid-nodule counterparts.

Timing of HPV16-E6 antibody seroconversion before OPSCC: findings from the HPVC3 consortium

BACKGROUND

Human papillomavirus type 16 (HPV16)-E6 antibodies are detectable in peripheral blood before diagnosis in the majority of HPV16-driven oropharyngeal squamous cell carcinoma (OPSCC), but the timing of seroconversion is unknown.

PATIENTS AND METHODS

We formed the HPV Cancer Cohort Consortium which comprises nine population cohorts from Europe, North America and Australia. In total, 743 incident OPSCC cases and 5814 controls provided at least one pre-diagnostic blood sample, including 111 cases with multiple samples. Median time between first blood collection and OPSCC diagnosis was 11.4 years (IQR = 6-11 years, range = 0-40 years). Antibodies against HPV16-E6 were measured by multiplex serology (GST fusion protein based Luminex assay).

RESULTS

HPV16-E6 seropositivity was present in 0.4% of controls (22/5814; 95% CI 0.2% to 0.6%) and 26.2% (195/743; 95% CI 23.1% to 29.6%) of OPSCC cases. HPV16-E6 seropositivity increased the odds of OPSCC 98.2-fold (95% CI 62.1-155.4) in whites and 17.2-fold (95% CI 1.7-170.5) in blacks. Seropositivity in cases was more frequent in recent calendar periods, ranging from 21.9% pre-1996 to 68.4% in 2005 onwards, in those with blood collection near diagnosis (lead time <5 years). HPV16-E6 seropositivity increased with lead time: 0.0%, 13.5%, 23.7%, and 38.9% with lead times of >30 years (N = 24), 20-30 years (N = 148), 10-20 years (N = 228), and <10 years (N = 301 cases) (p-trend < 0.001). Of the 47 HPV16-E6 seropositive cases with serially-collected blood samples, 17 cases seroconverted during follow-up, with timing ranging from 6 to 28 years before diagnosis. For the remaining 30 cases, robust seropositivity was observed up to 25 years before diagnosis.

CONCLUSIONS

The immune response to HPV16-driven tumorigenesis is most often detectable several decades before OPSCC diagnosis. HPV16-E6 seropositive individuals face increased risk of OPSCC over several decades.