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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.