National Lung Screening Trial Research Articles

Increasing power in screening trials by testing control-arm specimens: Application to multicancer detection screening.

Cancer screening trials have required large sample-sizes and long time-horizons to demonstrate cancer mortality reductions, the primary goal of cancer screening. We examine assumptions and potential power gains from exploiting information from testing control-arm specimens, which we call the "Intended Effect" (IE) analysis that we explain in detail herein. The IE analysis is particularly suited to tests that can be conducted on stored specimens in the control-arm, such as stored blood for multicancer detection (MCD) tests. We simulated hypothetical MCD screening trials to compare power and sample-size for the standard vs IE analysis. Under two assumptions that we detail herein, we projected the IE analysis for 3 existing screening trials (National Lung Screening Trial (NLST), Minnesota Colon Cancer Control Study (MINN-FOBT-A), and Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial-colorectal component (PLCO-CRC)). Compared to the standard analysis for the 3 existing trials, the IE design could have reduced cancer-specific mortality p-values 5-fold (NLST), 33-fold (MINN-FOBT-A), or 14,160-fold (PLCO-CRC), or alternately, reduced sample-size (90% power) by 26% (NLST), 48% (MINN-FOBT-A), or 59% (PLCO-CRC). For potential MCD trial designs requiring 100,000 subjects per-arm to achieve 90% power for multi-cancer mortality for the standard analysis, the IE analysis achieves 90% power for only 37,500-50,000 per arm, depending on assumptions concerning control-arm test-positives. Testing stored specimens in the control arm of screening trials to conduct the IE analysis could substantially increase power to reduce sample-size or accelerate trials, and provide particularly strong power gains for MCD tests.

Association of Lung Cancer Risk With the Presence of Both Lung Nodules and Emphysema in a Lung Cancer Screening Trial.

The coexistence of emphysema and lung nodules could interact with each other and then lead to potential higher lung cancer risk. The study aimed to explore the association between emphysema combined with lung nodules and lung cancer risk. A total of 21,949 participants from the National Lung Screening Trial (NLST) who underwent low-dose computed tomography (LDCT) examination were included. Participants were categorized into four groups (NENN group (non-emphysema and non-nodules), E group (emphysema without nodules), N group (nodules without emphysema), and E + N group (nodules with emphysema)) according to whether there were lung nodules and emphysema. Multivariable Cox regression and stratified analyses were performed to estimate the association between the four groups and lung cancer risk. Among the 21,949 participants, there were 9,040 (41.2%), 5,819 (26.5%), 4,737 (21.6%), and 2,353 (10.7%) participants in the NENN group, E group, N group, and E + N group. The risk of lung cancer incidence increased in turn in NENN group, E group, N group and E + N group. Compared with NENN group, the age-adjusted hazard ratios (HRs) (95% confidence intervals (CIs)) of lung cancer incidence were 2.07 (1.69 - 2.54) for E group, 4.13 (3.47 - 5.05) for N group, and 6.26 (5.14 - 7.62) for E + N group. The association was robust to adjustment for potential confounders (1.83 (1.47 - 2.27) for E group, 3.97 (3.24 - 4.86) for N group, and 5.23 (4.28 - 6.48) for E + N group). Comparable results as the lung cancer incidence were observed for lung cancer mortality, whether in age-adjusted model (E group: 1.85 (1.39 - 2.46), N group: 2.49 (1.89 - 3.29), E + N group: 4.27 (3.21 - 5.68)) or fully adjusted model (E group: 1.56 (1.15 - 2.11), N group: 2.43 (1.81 - 3.26), E + N group: 3.39 (2.50 - 4.61)). However, the trend of all-cause mortality risk among the four groups was somewhat different from that of lung cancer risk, whether in age-adjusted model (1.37 (1.21 - 1.54) for E group, 1.06 (0.92 - 1.21) for N group, and 1.75 (1.51 - 2.02) for E + N group) or fully adjusted model (1.26 (1.10 - 1.44) for E group, 1.09 (0.94 - 1.27) for N group, and 1.52 (1.30 - 1.79) for E + N group). Based on a large-scale lung cancer screening trial in the United States, this study demonstrated that either emphysema or lung nodules can increase lung cancer risk, and lung nodules combined with emphysema can further increase the lung cancer risk and all-cause mortality. The significance of these findings for lung cancer screening should be evaluated.

Smoking, Lung Cancer Stage, and Prognostic Factors-Findings from the National Lung Screening Trial.

Low-dose computed tomography (LDCT) increases the early detection of lung cancer. Identifying modifiable behaviors that may affect tumor progression in LDCT-detected patients increases the likelihood of long-term survival and a good quality of life. We examined cigarette smoking behaviors on lung cancer stage, progression, and survival in 299 ever-smoking patients with low-dose CT-detected tumors from the National Lung Screening Trial. Univariate and multivariate Cox models were used to estimate the hazard ratio (HR) for smoking variables on survival time. Current vs. former smokers and early morning smokers (≤5 min after waking, i.e., time to first cigarette (TTFC) ≤ 5 min) had more advanced-stage lung cancer. The adjusted HR for current vs. former smokers was 1.3 (95% confidence interval [CI] 0.911-1.98, p = 0.136) for overall survival (OS) and 1.3 (0.893-1.87, p = 0.1736) for progression-free survival (PFS). The univariate hazard ratios for TTFC ≤ 5 min vs. >5 min were 1.56 (1.1-2.2, p = 0.013) for OS and 1.53 (1.1-2.12, p = 0.01) for PFS. Among current smokers, the corresponding HRs for early TTFC were 1.78 (1.16-2.74, p = 0.0088) and 1.95 (1.29-2.95, p = 0.0016) for OS and PFS, respectively. In causal mediation analysis, the TTFC effect on survival time was mediated entirely through lung cancer stage. The current findings indicate smoking behaviors at diagnosis may affect lung cancer stage and prognosis.

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.

The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT-based assessment as kernel differences can lead to substantial shifts in measurements unrelated to underlying anatomical structures. In this study, we investigate kernel harmonization in a multi-vendor low-dose CT lung cancer screening cohort and evaluate our approach's validity in quantitative CT-based assessments. Using the National Lung Screening Trial, we identified CT scan pairs of the same sessions with one reconstructed from a soft tissue kernel and one from a hard kernel. In total, 1000 pairs of five different paired kernel types (200 each) were identified. We adopt the pix2pix architecture to train models for kernel conversion. Each model was trained on 100 pairs and evaluated on 100 withheld pairs. A total of 10 models were implemented. We evaluated the efficacy of kernel conversion based on image similarity metrics including root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) as well as the capability of the models to reduce measurement shifts in quantitative emphysema and body composition measurements. Additionally, we study the reproducibility of standard radiomic features for all kernel pairs before and after harmonization. Our approach effectively converts CT images from one kernel to another in all paired kernel types, as indicated by the reduction in RMSE (p<0.05) and an increase in the PSNR (p<0.05) and SSIM (p<0.05) for both directions of conversion for all pair types. In addition, there is an increase in the agreement for percent emphysema, skeletal muscle area, and subcutaneous adipose tissue (SAT) area for both directions of conversion. Furthermore, radiomic features were reproducible when compared with the ground truth features. Kernel conversion using deep learning reduces measurement variation in percent emphysema, muscle area, and SAT area.

Deep Reinforcement Learning for Early Diagnosis of Lung Cancer

Lung cancer remains the leading cause of cancer-related death worldwide, and early diagnosis of lung cancer is critical for improving the survival rate of patients. Performing annual low-dose computed tomography (LDCT) screening among high-risk populations is the primary approach for early diagnosis. However, after each screening, whether to continue monitoring (with follow-up screenings) or to order a biopsy for diagnosis remains a challenging decision to make. Continuing with follow-up screenings may lead to delayed diagnosis but ordering a biopsy without sufficient evidence incurs unnecessary risk and cost. In this paper, we tackle the problem by an optimal stopping approach. Our proposed algorithm, called EarlyStop-RL, utilizes the structure of the Snell envelope for optimal stopping, and model-free deep reinforcement learning for making diagnosis decisions. Through evaluating our algorithm on a commonly used clinical trial dataset (the National Lung Screening Trial), we demonstrate that EarlyStop-RL has the potential to greatly enhance risk assessment and early diagnosis of lung cancer, surpassing the performance of two widely adopted clinical models, namely the Lung-RADS and the Brock model.

Projecting the impact of multi-cancer early detection on late-stage incidence using multi-state disease modeling.

Downstaging- reduction in late-stage incidence-has been proposed as an endpoint in randomized trials of multi-cancer early detection (MCED) tests. How downstaging depends on test performance and follow-up has been studied for some cancers but is understudied for cancers without existing screening and for MCED tests that include these cancer types. We develop a model for cancer natural history that can be fit to registry incidence patterns under minimal inputs and can be estimated for solid cancers without existing screening. Fitted models are combined to project downstaging in MCED trials given sensitivity for early- and late-stage cancers. We fit models for 12 cancers using incidence data from the Surveillance, Epidemiology, and End Results program and project downstaging in a simulated trial under variable preclinical latencies and test sensitivities. A proof-of-principle lung cancer model approximated downstaging in the National Lung Screening Trial. Given published stage-specific sensitivities for 12 cancers, we projected downstaging ranging 21%-43% across plausible preclinical latencies in a hypothetical 3-screen MCED trial. Late-stage incidence reductions manifest soon after screening begins. Downstaging increases with longer early-stage latency or higher early-stage test sensitivity. Even short-term MCED trials could produce substantial downstaging given adequate early-stage test sensitivity. Modeling the natural histories of cancers without existing screening facilitates analysis of novel MCED products and trial designs. The framework informs expectations of MCED impact on disease stage at diagnosis and could serve as a building block for designing trials with late-stage incidence as the primary endpoint.

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers

BackgroundHeavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium.MethodsWe investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9–12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke).ResultsHere we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score.ConclusionsOur findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.

Personal history of cancer as a risk factor for second primary lung cancer: Implications for lung cancer screening.

Personal history of cancer is an independent risk factor for lung cancer but is omitted from existing lung cancer screening eligibility criteria. In this study, we assess the lung cancer risk among cancer survivors and discuss potential implications for screening. This was a retrospective, secondary analysis of data from the Surveillance, Epidemiology and End Results (SEER) registry and the MD Anderson Cancer Center (MDACC). We estimated the standardized incidence ratios (SIRs) for lung cancer by site of first primary cancer using data from SEER. We assessed the lung cancer risk among head and neck cancer survivors from MDACC using cumulative incidence and compared the risk ratios (RR) by individuals' screening eligibility status. Other than first primary lung cancer (SIR: 5.10, 95% CI: 5.01-5.18), cancer survivors in SEER with personal history of head and neck cancer (SIR: 3.71, 95% CI: 3.63-3.80) had the highest risk of developing second primary lung cancer, followed by bladder (SIR: 1.86, 95% CI: 1.81-1.90) and esophageal cancers (SIR: 1.78, 95% CI: 1.61-1.96). Head and neck cancer survivors had higher risk to develop lung cancer compared to the National Lung Screening Trial's subjects, (781 vs. 572 per 100,000 person-years, respectively). Head and neck cancer survivors ineligible for lung cancer screening seen at MDACC had significantly higher lung cancer risk than head and neck cancer survivors from SEER (RR: 1.9, p < 0.001). Personal history of cancer, primarily head and neck cancer, is an independent risk factor for lung cancer and may be considered as an eligibility criterion in future lung cancer screening recommendations.

Penalized deep partially linear cox models with application to CT scans of lung cancer patients.

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (eg, image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of dimensionality. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the smoothly clipped absolute deviation (SCAD) penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes.

Long-term Survival After Lung Cancer Resection in the National Lung Screening Trial

BackgroundThis study sought to evaluate the long-term survival and causes of death after surgery among patients with pathologic stage IA non-small cell lung cancer (NSCLC) in the National Lung Screening Trial (NLST). MethodsPatients who underwent surgery and who had a diagnosis of pathologic stage IA NSCLC in the NLST were identified for analysis. The 5- and 10-year overall survival and lung cancer–specific survival, stratified by operation type, were evaluated. Among patients who underwent lobectomy, the causes of death and the cumulative incidence of lung cancer death were assessed. ResultsA total of 380 patients (n = 329, 86.6% lobectomy; n = 20, 5.3% segmentectomy; n = 31, 8.1% wedge resection) met inclusion criteria. Median follow-up time from the date of surgery was 7.8 years (interquartile range, 4.8-10.7 years). The 10-year overall survival rate was 58.3% (95% CI, 52.4%-63.8%) for lobectomy, 59.9% (95% CI, 33.2%-78.8%) for segmentectomy, and 45.2% (95% CI, 20.8%-66.9%) for wedge resection. The 10-year lung cancer–specific survival rate was 74.3% (95% CI, 68.6%-79.1%) for lobectomy, 81.3% (95% CI, 51.3%-93.8%) for segmentectomy, and 84.8% (95% CI, 64.0%-94.1%) for wedge resection. Lung cancer was the leading cause of death, accounting for 55.8% of deaths after lobectomy. The 10-year cumulative incidence of lung cancer death after lobectomy was 22.5% (95% CI, 18.3%-27.1%). ConclusionsThe 10-year overall survival rate after lobectomy among patients with pathologic stage IA NSCLC in the NLST was 58%. Lung cancer was the leading cause of death, accounting for more than 55% of deaths.

Enrichment of lung cancer computed tomography collections with AI-derived annotations

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections containing computed tomography images of the chest, NSCLC-Radiomics, and a subset of the National Lung Screening Trial. Using publicly available AI algorithms, we derived volumetric annotations of thoracic organs-at-risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR (Findable, Accessible, Interoperable, Reusable) data principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can aid in cancer imaging.

Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial

The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population. To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults. This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023. Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years. For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality. The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%). Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

Abstract C121: Follow-up of moderate and severe coronary artery calcifications identified on lung cancer screening CT scans: An opportunity to improve cardiac health outcomes in Black men and women

Abstract Background: The USPSTF currently recommends lung cancer screening (LCS) by low-dose CT (LDCT) in those 50 to 80 years of age with 20+ pack-years of smoking who are currently smoking or  quit smoking within the last 15 years. LCS LDCT often detects non-cancer related incidental findings, most commonly coronary artery calcifications (CAC). CAC is a measure of subclinical coronary atherosclerosis, is higher in those with tobacco use, and may require additional clinical assessment after LCS. In the National Lung Screening Trial, reductions in all-cause mortality were stronger among Black than White participants undergoing LDCT. It was hypothesized that this may have been due to follow-up and treatment of incidental findings such as CAC during the trial. Methods: In this on-going observational study, we are evaluating clinical follow-up of moderate or severe CAC within 12 months of LCS among individuals undergoing baseline LCS at two Michigan health systems, one urban and one rural. We report here our initial findings for individuals with a baseline LCS LDCT at Henry Ford Health in 2018. Pearson Chi Square and McNemar’s tests were used to determine differences between race and sex groups and in change in statin use and cardiology visits (yes/no) pre- to post-screening. Results: Of 1,630 individuals (18% Black) with a baseline LDCT in 2018, moderate or severe CAC was documented in 154 (9.4%). Moderate or severe CAC prevalence was significantly higher among Black versus Non-Black ( 17.7 % vs 7.1%; p=  &amp;lt;0.001) individuals and men versus women ( 11.7% vs 7.1%; p=0.002 ). Pre- to post-LCS increases in statin use (68.2% vs. 76.0%; p=0.02) and cardiology visits (25.9% vs. 50.7%; p&amp;lt;0.01) were observed, overall. Statin use did not differ by race, sex, or race-sex group in the pre- or post- periods. Cardiology visits were less common among Blacks (18.2% vs. 28.9%; p=0.14) and women ( 18.2% vs. 28.9%; p= 0.14) in the pre-LCS period with Black women (8.3%) being the least likely to have a cardiology visit before LCS. Post-LCS, the cardiology gap between Black and Non-Black individuals (43.6% vs. 55.6%: p=0.16) narrowed and was eliminated between women and men (53.6 % vs. 50%; p=0.67). This was due in part to a substantial uptick of cardiology visits post-LCS in Black females. However, Black men, were significantly less likely than Non-Black men (32.3 % vs 56.7% ; p=0.024 ) and Black women (32.2% vs. 62.5%; p=0.024) to have a cardiology visit post-LCS . Conclusions: Our study is on-going; however, preliminary results suggest that in addition to reducing lung cancer mortality, LCS provides an opportunity for CAC detection and its related management. LCS, if implemented equitably with comprehensive follow-up of incidental findings, may help to close gaps in cardiac care for  Black individuals and women. Further investigation is needed to assess whether appropriate follow-up of CAC decreases cardiovascular risks for those undergoing screening.   Citation Format: Christine Neslund-Dudas, Katie R. Zarins, Punith Shetty, Andrea E. Cassidy-Bushrow, Nada Al-Antary, Katie A. Latack, Vritti Gupta, Michael Simoff, Riley Draper, Samuel Wilcox, Kendra Worden, Kathy LaRaia, Kelly Hirko. Follow-up of moderate and severe coronary artery calcifications identified on lung cancer screening CT scans: An opportunity to improve cardiac health outcomes in Black men and women [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C121.

Why is the screening rate in lung cancer still low? A seven-country analysis of the factors affecting adoption.

Strong evidence of lung cancer screening's effectiveness in mortality reduction, as demonstrated in the National Lung Screening Trial (NLST) in the US and the Dutch-Belgian Randomized Lung Cancer Screening Trial (NELSON), has prompted countries to implement formal lung cancer screening programs. However, adoption rates remain largely low. This study aims to understand how lung cancer screening programs are currently performing. It also identifies the barriers and enablers contributing to adoption of lung cancer screening across 10 case study countries: Canada, China, Croatia, Japan, Poland, South Korea and the United States. Adoption rates vary significantly across studied countries. We find five main factors impacting adoption: (1) political prioritization of lung cancer (2) financial incentives/cost sharing and hidden ancillary costs (3) infrastructure to support provision of screening services (4) awareness around lung cancer screening and risk factors and (5) cultural views and stigma around lung cancer. Although these factors have application across the countries, the weighting of each factor on driving or hindering adoption varies by country. The five areas set out by this research should be factored into policy making and implementation to maximize effectiveness and outreach of lung cancer screening programs.

Benefit-to-radiation-risk of low-dose computed tomography lung cancer screening.

The US National Lung Screening Trial (NLST) and Dutch-Belgian NELSON randomized controlled trials have shown significant mortality reductions from low-dose computed tomography (CT) lung cancer screening (LCS). NLST, ITALUNG, and COSMOS trials have provided detailed dosimetry data for LCS. LCS trial mortality benefit results, organ dose and effective dose data, and Biological Effects of Ionizing Radiation, Report VII (BEIR VII) organ dose-to-cancer-mortality risk data are used to estimate benefit-to-radiation-risk ratios of the NLST, ITALUNG, and COSMOS trials. Data from those trials also are used to estimate benefit-to-radiation-risk ratios for longer-term LCS corresponding to scenarios recommended by United States Preventive Services Task Force and the American Cancer Society. Including only screening doses, NLST benefit-to-radiation-risk ratios are 12:1 for males, 19:1 for females, and 16:1 overall. Including both screening and estimated follow-up doses, benefit-to-radiation-risk ratios for NLST are 9:1 for males, 13:1 for females, and 12:1 overall. For the ITALUNG trial, the benefit-to-radiation-risk ratio is 58-63:1. For the COSMOS trial, assuming sex-specific mortality benefits like those of the NELSON trial, the benefit-to-radiation-risk ratio is 23:1. Assuming a conservative 20% mortality benefit, annual screening in people 50-79 years old with a 20+ pack-year history of smoking has benefit-to-radiation-risk ratios of 23:1 (with follow-up doses adding 40% to screening doses) to 29:1 (with follow-up adding 10%) based on COSMOS dose data. Based on linear, no threshold BEIR VII dose-risk estimates, benefit-to-radiation-risk ratios for LCS are highly favorable. Results emphasize the importance of using modern CT technologies, maintaining low diagnostic follow-up rates, and minimizing both screening and diagnostic follow-up doses. The benefits of lung cancer screening significantly outweigh estimates of future harms associated with exposure to radiation during screening and diagnostic follow-up examinations. Our findings emphasize the importance of lung cancer screening practices using state-of-the-art computed tomography scanners and specialized low-dose lung screening and diagnostic follow-up techniques.

Absolute lung cancer risk increases among individuals with >15 quit-years: Analyses to inform the update of the American Cancer Society lung cancer screening guidelines.

This report quantifies counteracting effects of quit-years and concomitant aging on lung cancer risk, especially on exceeding 15 quit-years, when the US Preventive Services Task Force (USPSTF) recommends curtailing lung-cancer screening. Cox models were fitted to estimate absolute lung cancer risk among Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST) participants who ever smoked. Absolute lung cancer risk and gainable years of life from screening for individuals aged 50 to 80 in the US-representative National Health Interview Survey (NHIS) 2015-2018 who ever smoked were projected. Relaxing USPSTF recommendations to 20/25/30 quit-years versus augmenting USPSTF criteria with individuals whose estimated gain in life expectancy from screening exceeded 16.2days according to the Life Years From Screening-CT (LYFS-CT) prediction model was compared. Absolute lung cancer risk increased by 8.7%/year (95% CI, 7.7%-9.7%; p<.001) as individuals aged beyond 15 quit-years in the PLCO, with similar results in NHIS and NLST. For example, mean 5-year lung cancer risk for those aged 65 years with 15 quit-years=1.47% (95% CI, 1.35%-1.59%) versus 1.76% (95% CI, 1.62%-1.90%) for those aged 70 years with 20 quit-years in the PLCO. Removing the quit-year criterion would make 4.9 million more people eligible and increase the proportion of preventable lung cancer deaths prevented (sensitivity) from 63.7% to 74.2%. Alternatively, augmentation using LYFS-CT would make 1.7million more people eligible while increasing the lung cancer death sensitivity to 74.0%. Because of aging, absolute lung cancer risk increases beyond 15 quit-years, which does not support exemption from screening or curtailing screening once it has been initiated. Compared with relaxing the USPSTF quit-year criterion, augmentation using LYFS-CT could prevent most of the deaths at substantially superior efficiency, while also preventing deaths among individuals who currently smoke with low intensity or long duration.

Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population

PurposeThe aims of this study were to evaluate (1) frequency, type, and lung cancer stage in a clinical lung cancer screening (LCS) population and (2) the association between patient characteristics and Lung CT Screening Reporting & Data System (Lung-RADS®) with lung cancer diagnosis. MethodsThis retrospective study enrolled individuals undergoing LCS between January 1, 2015, and June 30, 2020. Individuals’ sociodemographic characteristics, Lung-RADS scores, pathology-proven lung cancers, and tumor characteristics were determined via electronic health record and the health system’s tumor registry. Associations between the outcome of lung cancer diagnosis within 1 year after LCS and covariates of sociodemographic characteristics and Lung-RADS score were determined using logistic regression. ResultsOf 3,326 individuals undergoing 5,150 LCS examinations, 102 (3.1%) were diagnosed with lung cancer within 1 year of LCS; most of these cancers were screen detected (97 of 102 [95.1%]). Over the study period, there were 118 total LCS-detected cancers in 113 individuals (3.4%). Most LCS-detected cancers were adenocarcinomas (62 of 118 [52%]), 55.9% (65 of 118) were stage I, and 16.1% (19 of 118) were stage IV. The sensitivity, specificity, positive predictive value, and negative predictive value of Lung-RADS in diagnosing lung cancer within 1 year of LCS were 93.1%, 83.8%, 10.6%, and 99.8%, respectively. On multivariable analysis controlling for sociodemographic characteristics, only Lung-RADS score was associated with lung cancer (odds ratio for a one-unit increase in Lung-RADS score, 4.68; 95% confidence interval, 3.87-5.78). ConclusionsThe frequency of LCS-detected lung cancer and stage IV cancers was higher than reported in the National Lung Screening Trial. Although Lung-RADS was a significant predictor of lung cancer, the positive predictive value of Lung-RADS is relatively low, implying opportunity for improved nodule classification.

Efforts to increase the application of low-dose computed tomography (LDCT): A resident-led strategy to improve lung cancer screening in an urban primary care clinic.

132 Background: Lung cancer carries the highest mortality rate of all malignancies, implicated in approximately 20% of cancer-related deaths in the United States. The 2011 National Lung Screening Trial (NLST) established a 20% reduction in lung cancer-associated mortality associated with annual LDCT screening in high-risk populations compared to screening chest radiography. In 2013 the United States Preventative Services Task Force (USPSTF) adopted this approach into their recommendations, with a subsequent update in 2021 establishing a recommended annual LDCT screening for those at high risk. There remains a concerning lack of access to PCP-initiated LDCT screening in medically underserved (e.g., urban, rural) areas, translating to relatively low rates of early lung cancer detection. Methods: Any patient meeting the 2021 USPSTF criteria (at least 20-pack-year history of tobacco use, who still smoke or have quit within the past 15 years) was deemed eligible. Excluded were those meeting these criteria and any patient who had received LDCT within the past 12 months. The pre-intervention stage of this study included a 15-week data collection of chart review to establish baseline rates. The intervention involved an educational flyer detailing the guidelines for lung cancer screening, placed in each clinic exam room to promote patient self-assessment of eligibility. Results: In the 15-week control group (i.e., pre-intervention), 177 patients were eligible for LDCT screening, with 65 successful LDCT orders and 112 not ordered (36.7% screened appropriately) and 27 (41.5%) LDCT orders completed. Through the 15 weeks of post-intervention data, 193 patients were eligible for LDCT screening, with 99 orders placed and 94 not (51.3% screened appropriately), and 42 (42.4%) of these LDCT orders were completed. Conclusions: The initial goal set for this project was a 50% lung cancer screening rate among eligible patients, with at least 50% of those LDCT orders performed by the patient. There was a remarkable improvement in LDCT ordering rates; however, the primary goal of these ordering improvement rates was to translate them into a higher percentage of patients completing these orders. Despite the increase in LDCT orders, the percentage completed stagnated (41.5% pre-intervention, 42.4% post-intervention). It was essential to our team that the interventions remain cost-effective. We did achieve this using printed flyers, allowing for patient-initiated involvement in the process. Using these interventions as a template, a clinic in any setting may benefit their LDCT ordering rates. However, these measures alone will not improve the percentage of patients completing the LDCT orders. As our project continues, we plan to modify the intervention to reflect constructive feedback from the resident physicians and patients involved.

Assessing the clinical utility of biomarkers using the intervention probability curve (IPC).

Assessing the clinical utility of biomarkers is a critical step before clinical implementation. The reclassification of patients across clinically relevant subgroups is considered one of the best methods to estimate clinical utility. However, there are important limitations with this methodology. We recently proposed the intervention probability curve (IPC) which models the likelihood that a provider will choose an intervention as a continuous function of the probability, or risk, of disease. To assess the potential impact of a new biomarker for lung cancer using the IPC. The IPC derived from the National Lung Screening Trial was used to assess the potential clinical utility of a biomarker for suspected lung cancer. The summary statistics of the change in likelihood of intervention over the population can be interpreted as the expected clinical impact of the added biomarker. The IPC analysis of the novel biomarker estimated that 8% of the benign nodules could avoid an invasive procedure while the cancer nodules would largely remain unchanged (0.1%). We showed the benefits of this approach compared to traditional reclassification methods based on thresholds. The IPC methodology can be a valuable tool for assessing biomarkers prior to clinical implementation.

Lung-Reporting and Data System 2.0: Impact of the Updated Approach to Juxtapleural Nodules During Lung Cancer Screening Using the National Lung Cancer Screening Trial Data Set.

To determine the frequency of malignancy of nonperifissural juxtapleural nodules (JPNs) measuring 6 to < 10 mm in a subset of low-dose chest computed tomographies from the National Lung Cancer Screening Trial and the rate of down-classification of such nodules in Lung-Reporting and Data System (RADS) 2.0 compared with Lung-RADS 1.1. A secondary analysis of a subset of the National Lung Screening Trial was performed. An exemption was granted by the Institutional Review Board. The dominant noncalcified nodule measuring 6 to <10 mm was identified on all available prevalence computed tomographies. Nodules were categorized as pleural or nonpleural. Benign or malignant morphology was recorded. Initial and updated categories based on Lung-RADS 1.1 and Lung-RADS 2.0 were assigned, respectively. The impact of the down-classification of JPN was assessed. Both classification schemes were compared using the McNemar test (P < 0.01). A total of 2813 patients (62 ± 5 y, 1717 men) with 4408 noncalcified nodules were studied. One thousand seventy-three dominant nodules measuring 6 to <10 mm were identified. Three hundred forty-eight (32.4%) were JPN. The updated scheme allowed down-classification of 310 JPN from categories 3 (n = 198) and 4A (n = 112) to category 2. We, therefore, estimate a 4.8% rate of down-classification to category 2 in the entire National Lung Screening Trial screening group. Two/348 (0.57%) JPN were malignant, both nonbenign in morphology. The false-positive rate decreased in the updated classification (P < 0.01). This study demonstrates the low malignant potential of benign morphology JPN measuring 6 mm to <10 mm. The Lung-RADS 2.0 approach to JPN is estimated to reduce short-term follow-ups and false-positive results.