Updates in Lung Cancer Screening: A Decade of Evidence

Keywords

lung cancer screening - review - randomized controlled trials

Lung cancer is the leading cause of cancer incidence and mortality worldwide, with an estimated 2.4 million individuals diagnosed with and 1.8 million dying from the disease in 2022.[1] Advances in lung cancer screening (LCS) over the last two decades have the potential to alter lung cancer outcomes among populations at high risk. However, there is no universal consensus on how best to define the high-risk population that will experience a net benefit from LCS. As such, variability in clinical guidelines and recommendations for whom to screen for lung cancer exists. In addition, implementation of LCS faces numerous challenges related to the identification of eligible individuals, the use of shared decision making, and the incorporation of smoking cessation into practice. In this review, we summarize the evidence from approximately the last 5 years across the LCS care continuum, focusing on updated results from randomized controlled trials (RCTs), clinical guidelines and recommendations, implementation of LCS, and the potential for personalized LCS and new technologies.

Lung Cancer Screening Trials

Initial results of the U.S. National Lung Screening Trial (NLST) published in 2011 reported a 20% mortality reduction in lung cancer mortality with 6.5 years of follow-up after three rounds of annual screening with low-dose CT (LDCT) compared with chest radiography among individuals ages 55 to 74 years who currently or formerly (quit within the last 15 years) smoked at least 30 pack-years (P-Y).[2] In 2020, the European NELSON trial reported a 24% reduction in lung cancer mortality with 10 years of follow-up after four rounds of LDCT among individuals ages 50 to 74 years who currently or formerly (quit within 10 years) smoked >15 cigarettes a day for >25 years or >10 cigarettes a day for >30 years.[3] The NLST and NELSON trials differed in terms of the recruited study population and nodule measurements. Compared to NLST participants, those in the NELSON trial were younger, had lower smoking intensity, and were predominantly male. In addition, NELSON assessed nodules based on volume growth, whereas NLST used nodule diameter. Despite these differences, the observed mortality reductions were similar. The NLST cohort was followed for an extended time of 11.3 years for incidence and 12.3 years for mortality outcomes, with results showing that the number needed to screen to prevent one lung cancer death was 303 and thus similar to the original analyses.[4] Beyond the NLST and the NELSON trials,[2] [3] several other RCTs examining LDCT for LCS have been conducted across Europe, including the DANTE,[5] DLCST,[6] LSS,[7] LUSI,[8] MILD,[9] ITALUNG,[10] and UKLS.[11] A meta-analysis of these seven RCTs and the NLST and NELSON trials reported a 16% relative reduction in lung cancer mortality for LDCT LCS versus non-LDCT controls (relative risk [RR] = 0.84; 95% confidence interval [CI]: 076–0.92).[11]

Although not powered to detect differences by subgroups, several analyses of LCS RCT results stratified by demographics suggest differences in mortality outcomes with regard to participant sex. In the NLST, the overall mortality RR was lower in women versus men (0.73 vs. 0.92, p-value for interaction 0.08), which appeared to be driven by differences in small cell and squamous cell carcinoma rather than nonsquamous NSCLC.[12] The NELSON trial, which included a small subsample of women, found that LDCT LCS was more favorable among women compared to men. In the German LUSI trial, women screened for lung cancer had a 69% mortality reduction (hazards ratio = 0.31; 95% CI: 0.10–0.96).[8] Pooled estimates from the NLST, NELSON, LUSI, and UKLS trial reported a 29% (RR = 0.71; 95% CI: 0.59–0.86) reduction for women and 15% for men (RR = 0.85; 95% CI: 0.76–0.95; the estimate for men also includes the DANTE trial, which excluded women).[13]

Clinical Guidelines and Recommendations

In the United States, several organizations recommend LCS based on individuals' age, smoking P-Y, years since quit (YSQ), smoking, and other factors, including comorbid conditions, functional status, or the ability to tolerate curative intent therapy ([Table 1]).[14] [15] [16] [17] [18] [19] [20] The recommendations for LCS have expanded over time. For example, the U.S. Preventive Services Task Force (USPSTF) updated their 2014 recommendations in 2021 to reduce the age at which to initiate LCS from 55 to 50 years and lowered the P-Y minimum from 30 to 20.[20] [21] [22] [23] [24] [25] This revision is expected to increase the eligible population by 6.4 million (81%).[21]

Table 1 Lung cancer screening recommendations, United States

Organization	Year updated	Age, y	Pack-years	Time since quit, y	Other
American Academy of Family Physicians[14]	2021	50–80	≥20	15	NA
American Association of Thoracic Surgery[15]	2012	55–79	≥30	15	NA
		50–79	≥20	NA	Cumulative risk >5% over the next 5 y
		NA	NA	NA	lung cancer survivors with no incidence of disease for ≥4 y
American Cancer Society[16]	2023	50–80	≥20	NA	NA
American College of Chest Physicians[17]	2021	50–80	≥20	15	NA
Center for Medicare and Medicaid Services[18]	2022	50–77	≥20	15	NA
National Comprehensive Cancer Network[19]	2025	50+	≥20	NA	No stopping age; screening not recommended for those with functional status or comorbidity that would not allow curative intent therapy
U.S. Preventive Services Task Force[20]	2021	50–80	≥20	15	NA

There is debate around the eligibility criteria for YSQ smoking. A systematic review conducted by the American Cancer Society (ACS) Cancer Related Evidence Synthesis Team found that the risk of developing lung cancer persisted beyond 15 YSQ and that risk remained significantly elevated even after quitting.[26] Based on this review and several other studies,[27] [28] the ACS removed the YSQ from their LCS recommendation.[16] This change is expected to result in nearly 5 million more U.S. adults being eligible for annual LCS.

Implementation

Global Landscape

LCS implementation is expanding worldwide ([Table 2]). To date, national screening programs have been established in North America (the United States), Asia (Japan, China, South Korea, and Taiwan), and Europe (Croatia, the Czech Republic, and Poland). Japan is the only country where screening has primarily occurred by chest radiology and sputum cytology until now.[29] Programs have varied in their effectiveness to reach the target population, due in part to differences in delivery, resource allocation and capacity, and policy decisions.[30] In the United States, LCS uptake remains below 20%,[31] whereas in Croatia, it has exceeded 80%.[32]

Table 2 Global implementation of lung cancer screening

Country	Implementation stage	Key milestones	Eligible population
United States	Implemented nationally	2013: U.S. Preventive Services Task Force first recommended annual LDCT screening for high-risk adults, based on National Lung Screening Trial (NLST) results 2015: Centers for Medicare and Medicaid Services established coverage for LDCT screening	Asymptomatic adults aged 50–80 with a >20 pack-year smoking history and still smoking or quit within the last 15 y
Canada	Implemented in some, but not all provinces	2021: Ontario launched an LCDT screening program at 4 sites after an implementation pilot 2022: British Columbia launched a province-wide LDCT screening program 2024: Nova Scotia launched a province-wide LDCT screening program 2025: New Brunswick will launch a province-wide LDCT screening program Quebec, Alberta, Newfoundland, and Labrador have implemented pilot programs	Ontario: Adults aged 55–80 who have a >20 pack-year smoking history and have smoked within the last 15 y British Columbia: Adults aged 55–74 who have a >20 year smoking history and are still smoking or have quit Nova Scotia: Adults aged 50–74 who have smoked daily for 20 or more years
Japan	Implemented nationally, with opportunistic LDCT screening	1987: Japan began offering chest X-ray screening to people aged 40 and older, with opportunistic LDCT screening 2010: Ministry of Health, Labor, and Welfare initiated a nationwide randomized controlled trial comparing two LDCT scans vs. a single chest X-ray scan over a 10-y period in over 20,000 adults aged 50–70 who currently or formerly smoked. Follow-up expected to end in 2035	Adults aged 40 and older
South Korea	Implemented nationally	2015: A multi-society collective developed guidelines recommending annual LDCT screening for high-risk individuals 2017–2018: Korean Lung Cancer Screening Project (K-LUCAS) conducted to assess the feasibility of implementing a national screening program, with promising results 2019: Formal launch of national program of biannual LDCT screening, including smoking cessation counseling 2023: Over 600,000 eligible individuals screened (∼53% uptake)	Adults aged 54–74 with a smoking history of >30 pack-years
Taiwan	Implemented nationally	2014: Taiwan Lung Cancer Screening for Never-Smoker Trial (TALENT) started 2022: The Ministry of Health and Welfare launched a national program of biannual LDCT screening for high-risk populations	Two high-risk populations: 1. Adults aged 50–74 with a smoking history of >30 pack-years 2. Men aged 50–74 and women aged 45–74 with a family history of lung cancer
China	Implemented primarily in urban areas	2012: China launched the Cancer Screening Program in Urban Areas, which includes biannual LDCT screening of high-risk individuals across 75 cities in 30 provinces	Adults meeting one of the following criteria: ≥30 pack-years of smoking and <15 y since quitting; exposed to passive smoking at home or in the workplace for ≥20 y; a diagnosis of chronic obstructive pulmonary disease; >1 y of regular occupational exposure to asbestos, radon, beryllium, chromium, cadmium, nickel, silica, coal smoke, or soot; first degree family history of lung cancer
Singapore	Not implemented	No national screening program, but the Ministry of Health recommends LDCT screening for high-risk populations
Croatia	Implemented nationally	2020: Croatia became the first European country to launch a national LDCT screening program 2023: More than 26,000 people screened, representing over 80% of the target population	Adults aged 50–75 with a smoking history of >30 pack-years and still smoking or quit in the last 15 y
The United Kingdom	Implemented nationally	2011: UK began pilot trials and studies to assess feasibility and aspects of LDCT screening, including UK Lung Cancer Screening (UKLS) pilot trial, Liverpool Health Lung Programme, Manchester Lung Health Check pilot, Lung Screen Uptake Trial (LSUT), West London lung screening pilot trial, Yorkshire Lung Screening Trial (YLST), and SUMMIT study 2019: NHS England launched the Targeted Lung Health Check program, which demonstrated efficacy to support the national LDCT screening program 2023: The UK began a national rollout of the LDCT screening program with targeted outreach, with complete rollout expected by 2030	Adults aged 55–74 who are registered with a GP practice and currently or formerly smoke are invited—eligibility for LDCT screening is then determined by model-based risk assessment
The Czech Republic	Implemented nationally via pilot	2022: The Czech Republic launched a 5-y population pilot screening program	Adults aged 55–74 with a smoking history of >20 pack-years
Poland	Implemented nationally via pilot	2018: Consensus for national LDCT screening, based on pilot data 2020–2024: Poland conducted a national pilot LDCT screening program across six macro-regions	Adults aged 55–74 with a smoking history of >20 pack-years or specific occupational exposures or related health conditions
Hungary	Assessing feasibility	2013–2020: First pilot conducted to demonstrate the feasibility of LDCT screening 2019–2022: Second pilot conducted to build a patient pathway 2023: Third pilot started to determine the approach for identifying high-risk individuals and inviting them to be screened. Data expected in 2025
Italy	Assessing feasibility	2021: Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation of Preventive Lung Health (PEOPLHE) trial launched to evaluate the feasibility and implementation of LDCT screening in the national healthcare system
Spain	Assessing feasibility	2022: Spain launched the CASSANDRA Project to explore the feasibility of implementing LDCT screening
France	Assessing feasibility	2022: The French Health Authority acknowledged the efficacy of LDCT screening and recommended a national pilot program 2025: Launch of pilot program to assess the feasibility of national LDCT screening implementation
Germany	Assessing feasibility	2024: Federal Ministry for the Environment, Nature Conservation, Nuclear Safety, and Consumer Protection issued the Lung Cancer Early Detection Ordinance, allowing providers to offer LDCT screening
Belgium	Assessing feasibility	2025: Flanders will launch an implementation study on LDCT screening in Zuid-Oost Rand Antwerpen (ZORALCS)
The Netherlands	Assessing feasibility	Research and implementation trials, including the 4-IN-THE-LUNG-RUN (4ITLR) trial, are ongoing to examine aspects of LDCT screening implementation
Australia	Pending implementation	2025: Australia will launch a national program of biannual LDCT screening in July	Asymptomatic adults aged 50–70 with >30 pack-year smoking history and still smoking or quit within the last 10 y

Other countries are at varying stages of implementation. In Canada, several provinces have implemented LCS programs, while other provinces are running pilot programs.[30] Implementation in Europe has occurred slowly, despite strong evidence from NELSON and other European screening trials and implementation studies. In September 2022, the European Commission endorsed stepwise implementation of LCS across the European Union (EU), and shortly thereafter, the EU Council of Ministers for Health approved funding for LCS at the national and EU levels. The Strengthening the Screening of Lung Cancer in Europe (SOLACE) project, which launched in April 2023, has become a key initiative within Europe's Beating Cancer Plan. SOLACE is uniting a network of European respiratory and radiology experts from 15 countries to enhance LCS efforts in underserved and high-risk populations across the EU.[33] Also in September 2022, the United Kingdom National Screening Committee recommended LCS, with a policy review outlining key priorities and requirements for implementation.[34] The UK began rolling out its nationwide targeted LCS program in 2023, with the goal of reaching 40% of the eligible population by 2025 and full implementation by 2030.[35] Australia is launching its national screening program in July 2025.[36]

Initiatives dedicated to advancing high-quality LCS implementation include the Lung Cancer Policy Network,[32] the G7 Cancer Alliance,[37] and the ACS National Lung Cancer Roundtable (NLCRT).[38]

Selection Criteria and Approach

No universal consensus exists on defining the population at “high risk” who is most likely to benefit from LCS. Accordingly, the population eligible for LCS differs between countries ([Table 2]). Even within the screening-eligible population, individual risk for developing lung cancer varies substantially, and the benefit conferred by LCS varies according to risk.[22] The selection criteria largely center on age and smoking history, given that lung cancer risk strongly increases with greater age and tobacco exposure, with some variation in the age range and thresholds for P-Y smoked and time since quit applied across countries. Selecting individuals on fixed age and smoking history criteria alone; however, results in including some at low risk who are unlikely to benefit from LCS and excluding others at high risk who are likely to benefit from LCS.[39] [40] Some countries consider screening based on other criteria. In Japan, eligibility is based on age alone.[29] Taiwan is the first to include screening of individuals with a family history of lung cancer who have never smoked,[41] while China is the first to include screening of individuals with a history of chronic obstructive pulmonary disease (COPD), with specific occupational exposures, or with a family history of lung cancer.[42] Poland has also included screening individuals with specific occupational exposures or related health conditions.[43]

Risk-based screening is an alternative approach with the potential to improve the balance of benefits to harms by screening fewer individuals, producing fewer false-positive results, and detecting more early-stage lung tumors.[44] Individuals are selected based on whether their personal risk for lung cancer—calculated using risk prediction models that incorporate age, smoking history, and various clinical and nonclinical factors—exceeds a specified risk threshold. To date, a myriad of risk prediction models have been proposed to estimate individual risk of developing or dying from lung cancer within a given time window; yet, only a handful have exhibited good performance in external validation studies[45]: the Bach model,[46] PLCO_M2012 model,[47] Liverpool Lung Project (LLP) model,[48] [49] Lung Cancer Risk Assessment Tool,[50] and Lung Cancer Death Risk Assessment Tool.[50]

Retrospective analyses consistently show that applying accurate risk prediction models to select ever-smoking adults for screening is more effective in preventing lung cancer deaths than applying fixed criteria on age and smoking history.[23] [47] [50] [51] Studies also suggest that risk-based screening using the PLCO_M2012 model can reduce lung cancer disparities, with a higher sensitivity for detecting lung cancer in racial and ethnic minority populations and women than USPSTF criteria-based screening.[52] [53] [54] However, risk-based screening can lead to comparatively modest increases in the number of life years and quality-adjusted life years (QALYs) gained and greater overdiagnosis, because it preferentially selects adults at the highest risk; these adults are generally older, have more comorbidities, and thereby may benefit less due to their shorter life expectancy and higher mortality from other causes.[51] [55] [56] Ideally, selecting individuals for LCS would consider both individual estimates of risk and life expectancy to prevent the most deaths and maximize the expected life-years gained in the population.[39] [55] [57] Several novel frameworks and strategies that incorporate individual risk, preferences, and life expectancy have been developed to inform risk-based screening decisions, although the minimum gain in life expectancy to recommend remains unclear.[55] [58] [59] Also under specific modeling assumptions, risk-based screening strategies appear more cost-effective than screening based on the 2021 USPSTF criteria.[60]

Outside the United States, promising results have emerged from multiple prospective trials and studies evaluating the feasibility and effectiveness of implementing risk-based LCS. The SUMMIT study demonstrated the feasibility of effectively delivering large-scale LCS to 12,773 enrolled the UK participants aged 55 to 77 who either met the 2013 USPSTF criteria or had a PLCO_M2012 risk threshold of ≥1.3%.[61] [62] The Manchester Lung Health Check pilot was likewise successful in delivering targeted LCS to high-risk individuals with a PLCO_M2012 risk threshold of ≥1.51% living in sociodemographically disadvantaged areas.[63] [64] In this pilot, follow-up of the screened and unscreened groups, classified based on the PLCO_M2012 risk threshold, confirmed that only a few lung cancer cases arose in the low-risk, unscreened group.[65] The Yorkshire Lung Screening Trial (YLST) compared the performance of selecting high-risk participants with the PLCO_M2012 risk threshold of ≥1.51%, LLP_V2 risk threshold of ≥5%, and 2013 USPSTF criteria.[66] The PLCO_M2012 model identified the most people eligible for screening and the most screen-detected lung cancers, and both risk models were more efficient at selecting individuals than the USPSTF criteria.[67] While telephone risk assessment was effective in inviting individuals for LCS in the YLST, current smoking status and socioeconomic deprivation corresponded with lower participation.[68] Interim analyses of the International Lung Screening Trial (ILST) further suggest that selecting ever-smoking adults aged 55 to 80 using the PLCO_M2012 risk threshold of ≥1.51% is more effective than the 2013 USPSTF criteria.[69] [70] Cost-effectiveness analyses using ILST data also show that risk-based screening results in greater cost savings, increased QALYs, and reduced inequities in screening access.[71] Currently, the 4-IN-THE-RUN trial is recruiting 900,000 individuals with a smoking history of ≥35 P-Y who have smoked in the last 10 years or with a PLCO_M2012 risk of ≥2.6% from the Netherlands, Germany, England, France, Italy, and Spain to determine the optimal personalized LCS approach that incorporates comorbidity-reducing strategies.[72]

Program Structure

In the United States, LCS programs are generally structured using a centralized, decentralized, or hybrid approach.[17] LCS guidelines do not recommend a specific approach, but instead the approach that aligns best with existing resources and needs of the population served. In centralized programs, primary care providers and other clinicians refer individuals to an LCS program, which has dedicated personnel responsible for critical program components, including eligibility assessment, shared decision-making (SDM), tobacco cessation management, LDCT ordering, communication of LDCT results, and management of follow-up screening and care. In decentralized programs, ordering providers hold overall responsibility for the entire LCS process, including specialty referral for follow-up care. At present, the majority of LCS occurs in decentralized settings. Many programs are also hybrid, where some but not all elements are centralized along the LCS continuum.

Centralized programs appear to be more effective, particularly in terms of adherence to annual LCS and follow-up care, than decentralized programs.[73] [74] [75] Centralized programs also more commonly implement practices to support LCS before screening is initiated.[76] Yet, the specific structures and practices of centralized programs that enhance LCS quality and effectiveness remain unclear.[77] [78] Limited evidence suggests that patient navigation practices, including support with scheduling, transportation, and accessing resources to reduce barriers to care, can improve LCS uptake and adherence, particularly in vulnerable populations.[79] [80] [81] Given that centralized programs require considerably more investment and resources, further research is needed to understand which components of centralized programs are the most beneficial and whether they could be extended into noncentralized programs.[77] [78]

Shared Decision-Making

SDM is recommended to help patients in making informed decisions about LCS with clinicians, considering the best available evidence on the benefits and harms of LCS and their own values and preferences.[20] The American Thoracic Society and Veterans Affairs Health Services Research and Development jointly advocate that SDM embrace several core principles, specifically to “empower the patient to participate in SDM to the extent they desire,” “include the information about LCS that the patient needs and wants to make an informed, value-based decision,” and “avoid exacerbating population-level disparities or worsening stigma related to smoking.”[82] For reimbursement coverage, the U.S. Centers for Medicare and Medicaid Services mandates a documented SDM visit with a qualified healthcare provider (physician, physician assistant, or nurse practitioner) before the beneficiary's first LDCT scan.[18] Specific visit requirements entail eligibility determination, SDM that incorporates the use of at least one decision aid, as well as counseling on the importance of screening adherence and of smoking cessation and abstinence, the impact of comorbidities, and the willingness to undergo follow-up care and treatment if abnormal findings arise.

These standards have been challenging to meet in practice. Although clinicians recognize the value of SDM, many cite major barriers to facilitating SDM, including time constraints, competing clinical demands and priorities, limited awareness about LCS guidelines, lack of skilled training in SDM, and insufficient resources to implement SDM.[83] [84] [85] Concerns have been raised about the intent and quality of SDM for LCS, given evidence that SDM conversations are often brief, prioritize exchanging information more than eliciting patient preferences, focus on benefits over harms, and lack the use of decision aids.[85] [86] Nonetheless, various decision aids, tools, and processes for SDM have been reported to improve patient knowledge, reduce decisional conflict, show acceptability to patients and providers, and increase LCS uptake and adherence.[87] [88] [89] [90] [91] [92] Especially those effectively tailored to populations at higher risk for lung cancer, such as people with HIV, may further promote health equity.[92] [93]

The role of SDM has become even more important since the USPSTF expanded the screening-eligible population to include individuals at lower absolute risk of lung cancer. Yet, the optimal approach to achieving high-quality SDM in LCS remains elusive. To overcome clinician barriers, a practical approach would have well-trained nonclinicians conduct SDM.[94] Several effectiveness-implementation studies are ongoing to identify effective and scalable approaches for SDM in LCS, including the TELESCOPE study that is evaluating a telehealth decision coaching and navigation intervention delivered by patient navigators in primary care clinics.[95] [96] [97] Clinicians are also open to employing more novel approaches, such as prediction-augmented SDM tools, which provide tailored LCS recommendations according to the level of predicted benefit.[98] Priorities outlined by the NLCRT and others to advance SDM research and implementation in LCS include developing adaptable SDM training programs for health care personnel, understanding how alternative screening delivery models affect SDM quality, developing and evaluating novel SDM tools across different settings and populations, establishing key SDM process quality measures, and investigating the utility of prediction-driven SDM.[82] [99]

Smoking Cessation

Smoking cessation is a necessary, but challenging component of high-quality LCS.[100] With at least 50% of all screen-eligible individuals actively smoking,[101] LCS offers “teachable moments” to reinforce smoking abstinence and to motivate and support those who smoke to quit.[102] [103] Of the participants in the UK Lung Health Check program who currently smoked, 44% indicated that screening made them consider quitting, 29% indicated that it made them attempt to quit, and 25% indicated that it made them smoke less, although only 10% indicated that it made them seek help to quit.[104]

Among NLST participants from the American College of Radiology (ACR) Imaging Network arm, over a third were highly addicted to nicotine and reported smoking within 5 minutes of waking up.[105] Yet, of those participants who smoked at enrollment and underwent LCS, 73.4% received no pharmacologic tobacco treatment, and those who were female, African American, unmarried, and less educated were less likely to attempt quitting.[106] Other studies have similarly noted that lower nicotine dependence and lower educational attainment correspond to less engagement in smoking cessation interventions.[107] [108] [109]

Model-based analyses indicate that smoking cessation confers added population health benefit beyond LCS alone. Even with modest quit rates (e.g., 10% quit), a single smoking cessation intervention at the initial screen can lead to fewer lung cancer deaths and notable gains in life expectancy, since smoking cessation reduces the risk of developing lung cancer and other tobacco-related conditions.[110] Additionally, integrating smoking cessation interventions, such as telephone-based counseling with nicotine replacement therapy (NRT), for individuals engaged in LCS has been estimated to be more cost-effective than LCS alone.[111] [112] [113]

As aforementioned, counseling on smoking cessation and interventions is recommended as part of the SDM visit. Beyond this, no specific guidance is provided on how to optimally deliver cessation interventions within the LCS process. The Society for Research on Nicotine and Tobacco and the Association for the Treatment of Tobacco Use and Dependence mutually recommend that individuals who smoke should be encouraged to quit at every screening visit and be offered evidence-based smoking cessation interventions, irrespective of their scan results or motivation to quit. These interventions should also be offered by SDM or other qualified providers; otherwise, individuals should be directed to tobacco cessation services.[114] However, many barriers hinder effective implementation of these services, including a lack of organizational support and limited time and reimbursement allotted to providers for treatment.[115] Most SDM providers further lack specialized training needed to provide comprehensive smoking cessation support.[116] Given these constraints, the most effective cessation intervention to implement in any given LCS program is likely to depend on its population, setting, and institutional resources and screening workflows.[117]

There is limited but growing evidence on best practices to optimize the delivery of smoking cessation interventions in LCS programs. In 2016, the U.S. National Cancer Institute established the Smoking Cessation at Lung Examination (SCALE) Collaboration, an initiative comprised of eight clinical trials evaluating the efficacy of smoking cessation interventions in the context of LCS. These trials tested various approaches ranging from individual therapies to health system interventions, and they collectively recruited 5,752 participants at 76 clinics.[118] Compared to screen-eligible individuals who smoke in the general U.S. population, participants enrolled in seven SCALE trials smoked slightly less, but were demographically similar at baseline.[119] Results from six SCALE trials have been published ([Table 3]).[120] [121] [122] [123] [124] [125] [126]

Table 3 Primary findings of U.S. scale trials

Trial	Primary findings
Georgetown Lung Screening, Tobacco, and Health (LSTH) trial[120]	In this randomized trial of 818 participants, those who received the intensive (8 telephone counseling sessions with 8 wk of nicotine patch) vs. minimal (3 telephone counseling sessions with 2 wk of nicotine patch) intervention had a higher self-reported quit rate at 3 mo (14.3 vs. 7.9%), but not at 6 or 12 mo
Optimizing Lung Screening Intervention (OaSIS) trial[121]	This effectiveness-implementation cluster randomized trial of 26 radiology facilities across 20 U.S. states found that average tobacco cessation increased from 0% at baseline to 13% at 6 mo but did not differ by trial group (intervention vs. usual care) at any measured timepoint (14 d, 3 mo, and 6 mo)
Program for Lung Cancer Screening and Tobacco Cessation (PLUTO) trial[122]	This adaptive sequential, multiple assignment randomized trial enrolling 636 screen-eligible adults who smoked daily from three large health systems found that adding a prescription medication therapy management referral to a tobacco longitudinal care (TLC) program (involving intensive telephone coaching and combination NRT for 1 y with at least monthly contact) did not improve smoking abstinence at 18 mo among those who did not respond to early treatment. In addition, the deintensification of TLC to quarterly contact among those who responded to early treatment had an unfavorable effect on smoking abstinence
Personalized Intervention Program: Tobacco Treatment for Patients at Risk for Lung Cancer (PIP) trial[123]	This two-phase, sequential, randomized controlled trial enrolling 188 patients found no difference in smoking abstinence at 8 wk, comparing standard of care (5 in-person counseling sessions and 8 wk of nicotine patch) plus gain-framed messaging vs. standard of care alone. Additionally, participants randomized to receive vs. not receive feedback on smoking-related biomarkers reported a similar daily number of cigarettes smoked at 6 mo
Screen ASSIST (aiding screening support in stopping tobacco) trial[124] [125]	In this randomized 2 × 2 × 2 factorial trial, 642 English- or Spanish-speaking adults scheduled for lung cancer screening were assigned to 8 groups receiving a multicomponent intervention with 3 treatment factors: duration of telehealth counseling (4 sessions over 4 wk vs. 8 sessions over 12 wk), duration of free NRT (2 wk vs. 8 wk), and screening for social determinants of health and referral to community-based resources (yes vs. no). At 6 mo, 7-d smoking abstinence was higher for those who received a longer duration of counseling only; no differences were noted for the other factors. This trial also found that proactive outreach at multiple points in the screening process is beneficial for engaging individuals in receiving tobacco treatment
Project LUNA[126]	This trial randomized 630 screening-eligible adults who currently smoke into three treatment groups: quitline referral for counseling and 12-wk NRT; quitline referral for counseling plus 12-wk NRT or pharmacotherapy prescribed by the screening clinician; and integrated care of 12-wk NRT or pharmacotherapy and intensive counseling provided by dedicated tobacco treatment specialists. Participants who received integrated care had the highest 7-d smoking abstinence at 3 mo (37.1%), followed by those who received quitline counseling with medication (27.1%) or without medication (25.2%); however, the difference in abstinence between those who received integrated care vs. quitline counseling with medication decreased at 6 mo (32.4 vs. 27.6%)

While not all SCALE trials identified group differences for their primary outcomes, they demonstrated the feasibility of integrating various smoking cessation interventions into LCS in different settings, as well as the importance of providing longitudinal behavioral and medication support for cessation. In the UK QuILT trial that randomized 412 adults aged 55 to 75 attending a targeted lung health check, those who received immediate support from a trained smoking cessation counselor with pharmacotherapy versus usual care (brief advice for quitting and signposting to cessation services) had higher 3-month quit rates (29.2% vs. 11%).[127] A meta-analysis of 10 RCTs of smoking cessation interventions delivered with LCS (including LSTH and QuILT) suggests that more intensive interventions are the most effective relative to usual care.[128] Unequivocally, continued efforts are vital to reducing barriers to smoking cessation and implementing evidence-based strategies that support individuals to quit smoking.

Lung Cancer Screening Uptake

Despite strong evidence that annual LCS with LDCT reduces mortality and recommendations endorsing LCS from multiple organizations, LCS uptake remains low in the United States, with 16.4% (95% CI: 15.6–17.2) of the 13.5 million eligible individuals screened in 2022,[31] the last year for which national data are available. Across the United States, LCS rates have ranged from 8.6 to 28.7%, with Northeastern and mid-Atlantic states tending to have higher rates. Currently, national self-reported LCS rates are available from the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System (BRFSS) through an LCS module that was optional until 2022. In 2022, the BRFSS LCS question asked if the respondent had any CT scans of the chest to check or screen for lung cancer.[129] No studies have validated the BRFSS LCS survey question, and it is possible that responders answer yes to include both screening and diagnostic chest CTs or that they may not recall accurately, both of which would result in overestimation of LCS uptake. The National Health Interview Survey[130] and the Health Information Trends Survey[131] also have cancer control supplemental modules and, in some years, asked about LCS.

While administrative claims data are also used to derive estimates of LCS uptake, these data do not contain sufficient detail to ascertain P-Y or YSQ and are limited to individuals with health insurance.[132] Electronic health records have also been used to assess LCS uptake and tend to have more, but not necessarily sufficient details on smoking information, such as P-Y and YSQ.

Several factors are associated with increased LCS uptake. A meta-analysis examining predictors of LCS in the United States found that Black or Hispanic adults had lower rates of LCS than White adults.[133] It also found geographic differences with higher rates in the Northeast and differences by socioeconomic status. The presence of comorbid conditions, particularly COPD, was also associated with higher rates of LCS uptake.

Interventions to increase LCS uptake have focused on (1) improving the identification of eligible individuals; (2) education to the community and providers; and (3) navigation to services.[134] A recent review noted that most LCS interventions address SDM and initial LCS uptake rather than LCS eligibility assessment, annual adherence, or diagnostic follow-up of screen-detected findings.[135]

Lung CT Screening Reporting and Data System

To facilitate standardized reporting and management of findings from LDCT screening exams, the ACR first released the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 (v1.0), with an update to v1.1 in 2019 and an update to Lung-RADS v2022 in November 2022.[136] The most recent updates include new classification criteria of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and inflammatory or infectious findings. Additionally, the latest Lung-RADS version provides clarification around volumetrics, nodule growth, the “S” modifier, and stepped management of nodules that are stable or decreasing in size. Lung-RADS continues to evolve over time based on new data and clinical insights.

Annual Adherence

For LCS to be effective in reducing mortality, adherence to recommended screening intervals is needed. Among individuals with a negative or normal LCS exam, the Lung-RADS recommendation is to return for screening annually. Measuring annual adherence across studies is challenging due to differences in definitions around the timing of follow-up and a need for standardization.[137]

Numerous studies have evaluated annual adherence to LCS.[73] [74] [138] [139] [140] [141] [142] [143] [144] [145] A meta-analysis by Lopez-Olivo et al reported a pooled LCS adherence rate of 55% (95% CI: 44–66), with adherence rates across studies ranging from 12 to 91%.[143] The wide variability in rates is likely due to differences in definitions, study populations, and study settings. Results of the pooled analyses showed factors associated with higher rates of annual adherence include centralized versus decentralized screening programs, having formerly smoked versus currently smoking, White versus other races, and completing 4 years or more of college versus not. A recent multicenter study of 10,170 individuals screened for lung cancer found that adherence to annual LCS was associated with increased lung cancer detection, especially at earlier stages; however, adherence declined annually following baseline screening, further signifying the need to improve adherence.[140]

Screen-Detected Findings and Management

Effective follow-up and management of pulmonary nodules detected from LCS is paramount to realizing the reported net benefits of LCS observed in the RCTs. Lung-RADS recommends specific testing and time intervals for follow-up depending on pulmonary nodule size, attenuation, shape, margin, calcification, and growth rate. Retrospective application of Lung-RADS criteria to the NLST resulted in 13.7% of baseline exams and 5.8% of subsequent exams classified as positive and warranting additional follow-up.[146] In real-world settings, approximately 11.4 to 19.6% of LCS exams are classified as positive (Lung-RADS 3, 4A, 4B, or 4X).[147] [148] [149] [150] [151] [152] In a multi-center cohort study, absolute rates of downstream imaging and invasive procedures were 31.9 and 2.8%, respectively.[152]

Limited data indicate suboptimal adherence to recommended follow-up care following positive LCS findings. In a prospective U.S. cohort study, overall adherence to recommended follow-up care was 42.6%, with an increasing trend in adherence from Lung-RADS category 3 to 4B/4X (30–68%), and factors related to poorer adherence included Black race, male sex, and current smoking status.[153] Another study similarly noted that almost half of the patients had delayed follow-up care after positive findings (i.e., >30 days beyond recommended Lung-RADS intervals), and among those diagnosed with lung cancer, delayed follow-up care was associated with clinical upstaging.[154]

Studies have documented higher rates of malignancy with increasing Lung-RADS category. In one study, malignancy rates corresponding to Lung-RADS categories 3, 4A, 4B, and 4X were 3.9, 15.5, 36.6, and 76.8%, respectively.[155] Lung cancer detection rates by LDCT LCS have ranged from 0.56 to 4.5%.[156] [157] [158] [159]

Incidental Findings and Management

Incidental findings (IFs) detected on LDCT LCS are defined as imaging abnormalities unrelated to lung cancer and are both pulmonary and extrapulmonary in nature. While some IFs may be benign, other IFs may be clinically significant (e.g., significant incidental findings [SIF]) and require subsequent follow-up, including diagnostic work-up and management. In 2023, the ACR released a quick reference guide for IF detected on LCS, which gives an overview of the most common IFs.[160]

Among the LDCT screening arm of the NLST, 18% of exams had at least one SIF, with the most common types being pulmonary findings (43%), coronary artery calcification (CAC; 12%), and masses or suspicious lesions (7%).[161] SIF were reported for about 8% of participants in the NELSON and COSMOS trials and for about 10% of those in the Australian and Canadian ILST.[162] [163] [164] Similar to the NLST, the most frequent IFs were CAC and emphysema.

There is limited research on IF detected on LCS outside of RCTs.[165] In real-world U.S. settings, Lung-RADS specifies using an “S” modifier to indicate a clinically or potentially clinically significant IF that is unrelated to pulmonary nodules. However, prior studies have shown that the “S” modifier does not capture all IF for which clinical follow-up is recommended. Hence, studies estimating SIF prevalence have applied differing definitions and have reported wide variation ranging from 16 to 69%.[166] [167] [168] [169] [170] [171] A scoping review of 32 articles examining CAC detected from LCS found CAC prevalence of 14.8 to 98%.[172]

The net benefits of detecting SIF on LDCT LCS are unknown. While detecting clinically actionable findings has been shown to favorably impact outcomes, there is also the potential to detect nonsignificant IF that may cause unneeded anxiety, unnecessary imaging or invasive procedures, or financial distress. This poses challenges for patients, providers, systems, and payors. Additional research to evaluate the clinical benefits and harms of SIF detected on LCS is needed.

Disparities

Established drivers of LCS disparities include healthcare access and costs.[173] Accessibility to LCS intrinsically depends on travel distance to LDCT facilities, with distance being inversely associated with population density and urbanization.[174] [175] Geospatial analyses estimate that 5% of the eligible U.S. population has no access to any LDCT facility within 40 miles, and that lack of access is especially pronounced in rural and socioeconomically disadvantaged areas and populations.[175] [176] [177] [178] [179] [180]

LCS utilization is further influenced by health insurance access and coverage. Analyses using the BRFSS survey data have found a greater proportion of screened non-Hispanic Black respondents were of Medicare age, despite similar LCS rates between non-Hispanic White and Black respondents,[181] and that gaining access to Medicare coverage increased LCS in screening-eligible men.[182] Additionally, U.S. claims data suggest that individuals with higher total out-of-pocket medical costs are less likely to adhere to annual LCS.[183]

To date, the few interventions addressing social determinants of health in LCS have favorably resulted in increased screening rates, as shown for other screening-associated cancers.[184] Various proposed opportunities to mitigate disparities include increasing public awareness about LCS, addressing misconceptions and stigma associated with lung cancer, establishing mobile LCS clinics in underserved communities, and fostering partnerships for community engagement.[185] [186]

Personalized Screening and New Technologies: Emerging Data and Considerations

Applying personalized approaches holds great promise for enhancing the effectiveness, efficiency, and accessibility of LCS. A growing body of research focuses on integrating risk prediction modeling, biomarkers, and artificial intelligence (AI) into LCS. Although emerging data are encouraging, including that primary care clinicians are open to adopting personalized LCS,[187] most proposed innovations require more robust evaluation and consideration before widespread clinical implementation.

Risk Prediction Models

The application of lung cancer risk prediction models has been largely focused on tailoring the selection of high-risk adults who have smoked for LCS. A major consideration is the optimal risk threshold upon which to screen. This choice should strike the best balance between individual benefits and harms along with program efficiency, costs, and impact.[188] Although adopted, the PLCO_M2012 6-year risk threshold of ≥1.51% was not derived considering cost-effectiveness.[189] Optimal risk thresholds must be set for each model, as different models generate varying absolute risk estimates for the same individual.[190] Risk thresholds must also be reassessed and adjusted as population characteristics change over time.[57]

Evidence increasingly supports using the PLCO_M2012 and LLP_V2 models in targeted LCS programs.[191] However, the clinical feasibility and acceptability of newer models, especially ones designed to incorporate comorbidities, life expectancy, and/or individual preferences, remain largely untested.[59] Accurate collection of required input data is critical for effective implementation of model-based risk assessment. Given the time constraints clinicians face, embedding automated risk calculation within electronic health records may help to streamline workflows and promote wider adoption of personalized LCS. Additionally, developing best practices to prevent unnecessary screening of low-risk populations is needed.

Validated risk prediction models can perform differently when applied to different populations and settings. Models that perform well in predicting lung cancer risk of ever-smoking adults in Western populations have been found to perform suboptimally when applied to ever-smoking adults in Asian populations.[192] Also in Asia, approximately 30 to 40% of all lung cancers arise in adults who have never smoked, a higher proportion than observed in the United States and Europe, highlighting the need to consider factors beyond age and smoking history in assessing risk.[193] While extending models to identify high-risk adults who have never smoked may be beneficial in broadening the reach of LCS, more definitive evidence is required to support tailored screening of never-smoking adults across diverse populations.

Biomarkers

Biomarkers can facilitate early detection of lung cancer by indicating the presence of associated molecular alterations before clinical symptoms manifest. Various lung cancer biomarkers are under investigation for their use with LDCT to refine the selection of individuals for LCS or improve risk stratification after lung nodule detection, as well as their standalone use to screen for lung and other cancers concurrently.

Blood-based biomarkers are the furthest along in clinical development, although relatively few have moved beyond early evaluation.

Autoantibodies

In early studies, the EarlyCDT-Lung test, an assay profiling seven tumor-associated autoantibodies,[194] demonstrated high specificity (91%) and low sensitivity (34–37%), with better performance for early-stage lung cancer, and its ability to differentiate malignant from benign pulmonary nodules.[195] [196] [197] [198] The Early Diagnosis of Lung Cancer Scotland trial randomized over 12,000 high-risk participants to receive the EarlyCDT-Lung test or usual care, and test-positive participants received LDCT every 6 months up to 2 years. More early-stage lung cancers were diagnosed in the intervention than the control arm, with no difference in mortality after 2 years[199]; however, 5-year mortality was lower in those tested (vs. untested) for autoantibodies and diagnosed with lung cancer within 2 years.[200] As designed, the trial could not assess the added value of the EarlyCDT-Lung test to LDCT, only its benefit to select individuals for LDCT screening.

Proteins

Nodify XL2 is a plasma-based test to identify likely benign lung nodules, integrating measurement of two proteins (LG3BP and C163A) with five clinical factors (age, smoking status, nodule diameter, nodule edge characteristics, and nodule location). It was validated in the PANOPTIC trial for 8 to 30 mm lung nodules with a pretest probability of cancer of ≤50%, achieving 97% sensitivity, 44% specificity, and 98% negative predictive value.[201] Its clinical utility in managing new solid lung nodules of low to moderate cancer risk is presently under evaluation in a multisite randomized trial (NCT04171492). This test is commercially available and covered by Medicare for certain patients with lung nodules. Additionally, a four-marker protein panel (4MP; CEA, Cyfra21-1, CA125, and Pro-SFTBP) combined with smoking history has shown greater sensitivity (63%) compared to smoking history alone (43%) in selecting individuals for LCS.[202] The 4MP in combination with the PLCO_M2012 model has further demonstrated improved risk assessment for LCS, relative to the 2021 USPSTF LCS eligibility criteria.[203]

MicroRNAs

A plasma-based 24-miRNA signature classifier and a serum-based 13-miRNA signature test have demonstrated the potential to reduce LDCT false-positive rates with high sensitivity (78–87%) and specificity (75–81%).[204] [205] [206] In the BioMILD trial of over 4,000 heavy-smoking individuals, the 24-miRNA signature classifier was used conjunctively with baseline LDCT to personalize LCS intervals, showing that individuals classified as positive on both LDCT and miRNA signature had a higher 4-year lung cancer incidence and 5-year lung cancer mortality.[206] The COSMOS II study is prospectively examining the 13-miRNA signature test alongside LDCT in approximately 10,000 high-risk individuals.[207] Other miRNA panels have been examined in selecting individuals for LCS[208] and assessing the risk of solitary pulmonary nodules.[209]

Circulating DNA

Cell-free DNA (cfDNA) analysis is a major focus of investigation in multicancer and LCS. The commercially available Galleri test, which analyzes cfDNA methylation patterns, initially exhibited 55% sensitivity and 99% specificity for detecting >50 cancer types across all stages, with about 20% sensitivity for early-stage lung cancer.[210] This test is currently being validated for detecting multiple cancers at an early stage in over 13,000 UK SUMMIT participants at high-risk for lung cancer (NCT03934866). Its safety and performance are also under evaluation in PATHFINDER 2, a prospective interventional study comprising 35,000 U.S. adults eligible for cancer screening (NCT05155605). Based on analyses from the Copenhagen City Heart Study, DNA methylation could also be used with existing screening criteria to enhance the selection of individuals for LCS by excluding those at the lowest risk.[211] DELPHI (DNA evaluation of fragments for early interception) analyzes genomewide cfDNA fragmentation profiles to detect cancer.[212] The CASCADE-LUNG study is underway to prospectively validate DELPHI test performance in detecting lung cancer among nearly 12,000 screening-eligible adults (NCT04825834). The FIRSTLUNG cluster randomized trial is further assessing the clinical utility of DELFI on promoting LCS uptake in primary care settings (NCT0614570).

Beyond blood, other promising sources of biomarkers include airway epithelia, exhaled breath, sputum, and urine. Yet, some constraints hinder the utility and application of biomarkers in LCS.[213] [214] Blood and sputum tests have been generally limited by low sensitivity, especially for detecting small tumors. Assessing airway epithelia via bronchoscopic brushing is invasive. Methods for sampling and analyzing volatile organic compounds in exhaled breath lack standardization. Urine biomarkers can be influenced by diet, medication, and comorbidities. Most notably, many biomarkers have yet to undergo rigorous prospective validation for their intended use in LCS.

AI

With LCS implementation, radiologists face greater demands to interpret higher volumes of CT images promptly and accurately. AI models developed for pulmonary nodule detection and classification show promise in overcoming this challenge through automating image analysis and reducing inter-reader variation. Compared to radiologists, AI models for nodule detection have exhibited higher sensitivity (86–98% vs. 68–76%) but lower specificity (78–87% vs. 87–92%), and those for classification of nodule malignancy have generally exhibited better sensitivity (61–93% vs. 77–88%), specificity (64–96% vs. 62–84%), and accuracy (65–92% vs. 73–86%).[215] Evaluating LDCT interpretation times and outcomes across simulated AI use case scenarios, using AI as a prescreener (i.e., radiologists only interpret exams with a positive AI result) was the only scenario that led to a lower recall rate (20.8% vs. 22.1%), lower mean interpretation time (143 seconds vs. 164 seconds), and higher per-exam specificity (90.3% vs. 88.8%), relative to radiologist interpretation without AI.[216] Other scenarios included using AI as an assistant (i.e., radiologists interpret all exams with AI assistance) and using AI as a backup (i.e., radiologists reinterpret exams when AI indicates a missed finding). Data from the 4-IN-THE-LUNG-RUN and UK LCS trials further suggest that using commercial AI software as a first read to independently rule out negative LDCT scans at baseline can outperform radiologists and reduce their workload, without considerably missing diagnostic referrals or lung cancers.[217] [218]

AI models that predict future lung cancer risk have primarily emerged since 2020, exhibiting better performance than traditional regression-based models.[219] While most traditional risk prediction models consider epidemiologic and clinical factors, several AI models consider imaging data only. Deep learning models incorporating imaging data from LDCT scans, such as the Ardila et al[220] and Sybil[221] models, have especially shown strong predictive performance (pooled area under the curve: 0.85; 95% CI: 0.82–0.88), offering the potential to tailor screening frequency to an individual's predicted lung cancer risk from baseline or subsequent LDCT scans.[219] Notably, Sybil predicts risk from a single LDCT scan without additional clinical data or radiologist annotations, has been externally validated in several populations, and is publicly accessible.[221] AI can further support opportunistic CT screening for other chronic conditions, including cardiovascular disease, emphysema, and osteopenia, as an added benefit toward improving population health.[222]

To advance integration of AI-based tools in LCS, large-scale prospective validation of their performance and clinical utility is especially needed in diverse populations. That encompasses examining how imaging parameters and reconstruction techniques influence their performance, given variability in the imaging scanners and protocols used across clinical settings, as well as evaluating how AI-based tools impact the performance of radiologists. Investigating the interpretability of results from deep learning models through correlation with input data is also important to better understand how predictions are made.[223] Although relatively in its infancy, AI shows immense potential to transform LCS, particularly in facilitating clinical decision-making.

Conclusion

Over the last 5 years, the landscape around LCS has changed. Updated data from LCS RCTs solidify the mortality benefit of LDCT for LCS among high-risk individuals, especially with data from the NELSON study supporting LCS among younger individuals with lower smoking intensity. In the United States, updated LCS guidelines have reduced the age at which to initiate LCS to 50 years and the minimum smoking intensity to 20 P-Y, with some differences in guidelines around the time since quit criteria and the incorporation of other risk factors. While retrospective analyses consistently show that selecting ever-smoking adults for LCS based on risk prediction models is more effective than applying fixed criteria based on age and smoking history, implementation of risk prediction models into practice is challenging but possible. The SCALE and other trials demonstrated the feasibility of integrating various smoking cessation interventions into LCS across different settings and reinforced the importance of providing longitudinal behavioral and medication support for smoking cessation. In real-world settings, LCS uptake and adherence remain suboptimal, and lung cancer detection rates with LDCT LCS range from 0.56 to 4.5%. There is limited data on the net benefits of IFs detected on LCS. While the use of personalized approaches to LCS holds great promise for enhancing the effectiveness, efficiency, and accessibility of LCS, most proposed innovations require additional evaluation and consideration before widespread clinical adoption.

• References

• 1 Bray F, Laversanne M, Sung H. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024; 74 (03) 229-263
• 2 Aberle DR, Adams AM, Berg CD. et al; National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011; 365 (05) 395-409
• 3 de Koning HJ, van der Aalst CM, de Jong PA. et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med 2020; 382 (06) 503-513
• 4 National Lung Screening Trial Research Team. Lung cancer incidence and mortality with extended follow-up in the national lung screening trial. J Thorac Oncol 2019; 14 (10) 1732-1742
• 5 Infante M, Cavuto S, Lutman FR. et al; DANTE Study Group. Long-term follow-up results of the DANTE Trial, a randomized study of lung cancer screening with spiral computed tomography. Am J Respir Crit Care Med 2015; 191 (10) 1166-1175
• 6 Wille MM, Dirksen A, Ashraf H. et al. Results of the randomized Danish lung cancer screening trial with focus on high-risk profiling. Am J Respir Crit Care Med 2016; 193 (05) 542-551
• 7 Doroudi M, Pinsky PF, Marcus PM. Lung cancer mortality in the lung screening study feasibility trial. JNCI Cancer Spectr 2018; 2 (03) pky042
• 8 Becker N, Motsch E, Trotter A. et al. Lung cancer mortality reduction by LDCT screening-Results from the randomized German LUSI trial. Int J Cancer 2020; 146 (06) 1503-1513
• 9 Pastorino U, Silva M, Sestini S. et al. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Ann Oncol 2019; 30 (07) 1162-1169
• 10 Paci E, Puliti D, Lopes Pegna A. et al; the ITALUNG Working Group. Mortality, survival and incidence rates in the ITALUNG randomised lung cancer screening trial. Thorax 2017; 72 (09) 825-831
• 11 Field JK, Vulkan D, Davies MPA. et al. Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis. Lancet Reg Health Eur 2021; 10: 100179
• 12 Pinsky PF, Church TR, Izmirlian G, Kramer BS. The national lung screening trial: results stratified by demographics, smoking history, and lung cancer histology. Cancer 2013; 119 (22) 3976-3983
• 13 Bonney A, Malouf R, Marchal C. et al. Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality. Cochrane Database Syst Rev 2022; 8 (08) CD013829
• 14 Kim J, Lee H, Huang BW. Lung cancer: diagnosis, treatment principles, and screening. Am Fam Physician 2022; 105 (05) 487-494
• 15 Jaklitsch MT, Jacobson FL, Austin JH. et al. The American Association for Thoracic Surgery guidelines for lung cancer screening using low-dose computed tomography scans for lung cancer survivors and other high-risk groups. J Thorac Cardiovasc Surg 2012; 144 (01) 33-38
• 16 Wolf AMD, Oeffinger KC, Shih TY. et al. Screening for lung cancer: 2023 guideline update from the American Cancer Society. CA Cancer J Clin 2024; 74 (01) 50-81
• 17 Mazzone PJ, Silvestri GA, Souter LH. et al. Screening for lung cancer: CHEST guideline and expert panel report. Chest 2021; 160 (05) e427-e494
• 18 Centers for Medicare and Medicaid Services. Screening for Lung Cancer with Low Dose Computed Tomography (LDCT) Decision Summary. Accessed September 18, 2025 at: https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304
• 19 Wood DE, Kazerooni EA, Aberle DR. et al. NCCN guidelines insights: lung cancer screening, version 1.2025. J Natl Compr Canc Netw 2025; 23 (01) e250002
• 20 Krist AH, Davidson KW, Mangione CM. et al; US Preventive Services Task Force. Screening for lung cancer: US preventive services task force recommendation statement. JAMA 2021; 325 (10) 962-970
• 21 Henderson LM, Rivera MP, Basch E. Broadened eligibility for lung cancer screening: challenges and uncertainty for implementation and equity. JAMA 2021; 325 (10) 939-941
• 22 Jonas DE, Reuland DS, Reddy SM. et al. Screening for lung cancer with low-dose computed tomography: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 2021; 325 (10) 971-987
• 23 Meza R, Jeon J, Toumazis I. et al. Evaluation of the benefits and harms of lung cancer screening with low-dose computed tomography: modeling study for the US Preventive Services Task Force. JAMA 2021; 325 (10) 988-997
• 24 Moyer VA. U.S. Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2014; 160 (05) 330-338
• 25 Toumazis I, de Nijs K, Cao P. et al. Cost-effectiveness evaluation of the 2021 US Preventive Services Task Force Recommendation for Lung Cancer Screening. JAMA Oncol 2021; 7 (12) 1833-1842
• 26 Kondo KK, Rahman B, Ayers CK, Relevo R, Griffin JC, Halpern MT. Lung cancer diagnosis and mortality beyond 15 years since quit in individuals with a 20+ pack-year history: a systematic review. CA Cancer J Clin 2024; 74 (01) 84-114
• 27 Landy R, Cheung LC, Young CD, Chaturvedi AK, Katki HA. 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. Cancer 2024; 130 (02) 201-215
• 28 Meza R, Cao P, de Nijs K. et al. Assessing the impact of increasing lung screening eligibility by relaxing the maximum years-since-quit threshold: a simulation modeling study. Cancer 2024; 130 (02) 244-255
• 29 Horinouchi H, Kusumoto M, Yatabe Y, Aokage K, Watanabe SI, Ishikura S. Lung cancer in Japan. J Thorac Oncol 2022; 17 (03) 353-361
• 30 Poon C, Wilsdon T, Sarwar I, Roediger A, Yuan M. Why is the screening rate in lung cancer still low? A seven-country analysis of the factors affecting adoption. Front Public Health 2023; 11: 1264342
• 31 Henderson LM, Su I-H, Rivera MP. et al. Prevalence of lung cancer screening in the US, 2022. JAMA Netw Open 2024; 7 (03) e243190
• 32 Lung Cancer Policy Network. Accessed September 18, 2025 at: https://www.lungcancerpolicynetwork.com/
• 33 Kauczor HU, von Stackelberg O, Nischwitz E. et al; and SOLACE Consortium. Strengthening lung cancer screening in Europe: fostering participation, improving outcomes, and addressing health inequalities through collaborative initiatives in the SOLACE consortium. Insights Imaging 2024; 15 (01) 252
• 34 O'Dowd EL, Lee RW, Akram AR. et al. Defining the road map to a UK national lung cancer screening programme. Lancet Oncol 2023; 24 (05) e207-e218
• 35 Cancer Research UK. Accessed September 18, 2025 at: https://www.cancerresearchuk.org/health-professional/cancer-screening/lung-cancer-screening
• 36 Australian Government Department of Health and Aged Care. Accessed September 18, 2025 at: https://www.health.gov.au/our-work/nlcsp
• 37 Senior K. G7 Cancer: the priorities and challenges ahead. Lancet Oncol 2023; 24 (06) e240
• 38 Kazerooni EA, Wood DE, Rosenthal LS, Smith RA. The American Cancer Society National Lung Cancer Roundtable strategic plan: introduction. Cancer 2024; 130 (23) 3948-3960
• 39 Katki HA, Cheung LC, Landy R. Basing eligibility for lung cancer screening on individualized risk calculators should save more lives, but life expectancy matters. J Natl Cancer Inst 2020; 112 (05) 429-430
• 40 Faselis C, Nations JA, Morgan CJ. et al. Assessment of lung cancer risk among smokers for whom annual screening is not recommended. JAMA Oncol 2022; 8 (10) 1428-1437
• 41 Yang P-C, Chen TH-H, Huang K-P, Lin L-J, Wu C-C. Taiwan national lung cancer early detection program for heavy smokers and non-smokers with family history of lung cancer. J Clin Oncol 2024; 42 (16, suppl): 8009
• 42 Xia C, Basu P, Kramer BS. et al. Cancer screening in China: a steep road from evidence to implementation. Lancet Public Health 2023; 8 (12) e996-e1005
• 43 Manxhuka B, Hofmarcher T. Cancer Dashboard for Poland - Lung Cancer. Accessed September 18, 2025 at: https://onkologia.org.pl/sites/default/files/publications/2024-09/ihe_-_lung_cancer_dashboard_poland.pdf
• 44 Toumazis I, Bastani M, Han SS, Plevritis SK. Risk-based lung cancer screening: a systematic review. Lung Cancer 2020; 147: 154-186
• 45 Susai CJ, Velotta JB, Sakoda LC. Clinical adjuncts to lung cancer screening: a narrative review. Thorac Surg Clin 2023; 33 (04) 421-432
• 46 Bach PB, Kattan MW, Thornquist MD. et al. Variations in lung cancer risk among smokers. J Natl Cancer Inst 2003; 95 (06) 470-478
• 47 Tammemägi MC, Katki HA, Hocking WG. et al. Selection criteria for lung-cancer screening. N Engl J Med 2013; 368 (08) 728-736
• 48 Cassidy A, Myles JP, van Tongeren M. et al. The LLP risk model: an individual risk prediction model for lung cancer. Br J Cancer 2008; 98 (02) 270-276
• 49 Field JK, Vulkan D, Davies MPA, Duffy SW, Gabe R. Liverpool lung project lung cancer risk stratification model: calibration and prospective validation. Thorax 2021; 76 (02) 161-168
• 50 Katki HA, Kovalchik SA, Berg CD, Cheung LC, Chaturvedi AK. Development and validation of risk models to select ever-smokers for CT lung cancer screening. JAMA 2016; 315 (21) 2300-2311
• 51 Ten Haaf K, Bastani M, Cao P. et al. A comparative modeling analysis of risk-based lung cancer screening strategies. J Natl Cancer Inst 2020; 112 (05) 466-479
• 52 Pasquinelli MM, Tammemägi MC, Kovitz KL. et al. Addressing sex disparities in lung cancer screening eligibility: USPSTF vs PLCOm2012 criteria. Chest 2022; 161 (01) 248-256
• 53 Pasquinelli MM, Tammemägi MC, Kovitz KL. et al. Brief report: risk prediction model versus United States Preventive Services Task Force 2020 draft lung cancer screening eligibility criteria-reducing race disparities. JTO Clin Res Rep 2020; 2 (03) 100137
• 54 Choi E, Ding VY, Luo SJ. et al. Risk model-based lung cancer screening and racial and ethnic disparities in the US. JAMA Oncol 2023; 9 (12) 1640-1648
• 55 Cheung LC, Berg CD, Castle PE, Katki HA, Chaturvedi AK. Life-gained-based versus risk-based selection of smokers for lung cancer screening. Ann Intern Med 2019; 171 (09) 623-632
• 56 Kumar V, Cohen JT, van Klaveren D. et al. Risk-targeted lung cancer screening: a cost-effectiveness analysis. Ann Intern Med 2018; 168 (03) 161-169
• 57 Landy R, Cheung LC, Berg CD, Chaturvedi AK, Robbins HA, Katki HA. Contemporary implications of U.S. Preventive Services Task Force and risk-based guidelines for lung cancer screening eligibility in the United States. Ann Intern Med 2019; 171 (05) 384-386
• 58 Caverly TJ, Hayward RA, Meza R. Identifying patients for whom lung cancer screening is preference-sensitive. Ann Intern Med 2018; 169 (11) 823
• 59 Toumazis I, Alagoz O, Leung A, Plevritis SK. A risk-based framework for assessing real-time lung cancer screening eligibility that incorporates life expectancy and past screening findings. Cancer 2021; 127 (23) 4432-4446
• 60 Toumazis I, Cao P, de Nijs K. et al. Risk model-based lung cancer screening : a cost-effectiveness analysis. Ann Intern Med 2023; 176 (03) 320-332
• 61 Horst C, Dickson JL, Tisi S. et al. Delivering low-dose CT screening for lung cancer: a pragmatic approach. Thorax 2020; 75 (10) 831-832
• 62 Bhamani A, Creamer A, Verghese P. et al; SUMMIT consortium. Low-dose CT for lung cancer screening in a high-risk population (SUMMIT): a prospective, longitudinal cohort study. Lancet Oncol 2025; 26 (05) 609-619
• 63 Crosbie PA, Balata H, Evison M. et al. Second round results from the Manchester ‘lung health check’ community-based targeted lung cancer screening pilot. Thorax 2019; 74 (07) 700-704
• 64 Crosbie PA, Balata H, Evison M. et al. Implementing lung cancer screening: baseline results from a community-based ‘lung health check’ pilot in deprived areas of Manchester. Thorax 2019; 74 (04) 405-409
• 65 Goodley P, Balata H, Robbins HA, Booton R, Sperrin M, Crosbie PAJ. Six-year performance of risk-based selection for lung cancer screening in the Manchester lung health check cohort. BMJ Oncol 2024; 3 (01) e000560
• 66 Crosbie PA, Gabe R, Simmonds I. et al. Yorkshire lung screening trial (YLST): protocol for a randomised controlled trial to evaluate invitation to community-based low-dose CT screening for lung cancer versus usual care in a targeted population at risk. BMJ Open 2020; 10 (09) e037075
• 67 Gabe R, Crosbie PAJ, Vulkan D. et al. Prospective evaluation of lung cancer screening eligibility criteria and lung cancer detection in the Yorkshire lung screening trial. J Thorac Oncol 2025; 20 (04) 425-436
• 68 Crosbie PAJ, Gabe R, Simmonds I. et al. Participation in community-based lung cancer screening: the Yorkshire Lung Screening Trial. Eur Respir J 2022; 60 (05) 2200483
• 69 Lim KP, Marshall H, Tammemägi M. et al; ILST (International Lung Screening Trial) Investigator Consortium. Protocol and rationale for the international lung screening trial. Ann Am Thorac Soc 2020; 17 (04) 503-512
• 70 Tammemägi MC, Ruparel M, Tremblay A. et al. USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (international lung screening trial): interim analysis of a prospective cohort study. Lancet Oncol 2022; 23 (01) 138-148
• 71 Cressman S, Weber MF, Ngo PJ. et al. Economic impact of using risk models for eligibility selection to the International Lung Screening Trial. Lung Cancer 2023; 176: 38-45
• 72 van der Aalst C, Vonder M, Hubert J. et al. P1.14–04 European lung cancer screening implementation: 4-IN-THE-LUNG-RUN trial. J Thorac Oncol 2023; 18 (11) S217
• 73 Kim RY, Rendle KA, Mitra N. et al. Racial disparities in adherence to annual lung cancer screening and recommended follow-up care: a multicenter cohort study. Ann Am Thorac Soc 2022; 19 (09) 1561-1569
• 74 Sakoda LC, Rivera MP, Zhang J. et al. Patterns and factors associated with adherence to lung cancer screening in diverse practice settings. JAMA Netw Open 2021; 4 (04) e218559
• 75 Smith HB, Ward R, Frazier C, Angotti J, Tanner NT. Guideline-recommended lung cancer screening adherence is superior with a centralized approach. Chest 2022; 161 (03) 818-825
• 76 Núñez ER, Slatore CG, Tanner NT. et al. National survey of lung cancer screening practices in Veterans Health Administration facilities. Am J Prev Med 2023; 65 (05) 901-905
• 77 Núñez ER, Triplette M. Addressing lung cancer screening disparities: what does it mean to be centralized?. Ann Am Thorac Soc 2022; 19 (09) 1457-1458
• 78 Martin JF, Kane GC, Shusted CS, Barta JA. Implementation of high-quality lung cancer screening: impact of centralized vs. decentralized processes. Popul Health Manag 2024; 27 (04) 291-293
• 79 Bhalla S, Natchimuthu V, Lee JL. et al. Effect of patient navigation on completion of lung cancer screening in vulnerable populations. J Natl Compr Canc Netw 2024; 22 (03) 151-157
• 80 Percac-Lima S, Ashburner JM, Rigotti NA. et al. Patient navigation for lung cancer screening among current smokers in community health centers a randomized controlled trial. Cancer Med 2018; 7 (03) 894-902
• 81 Shusted CS, Barta JA, Lake M. et al. The case for patient navigation in lung cancer screening in vulnerable populations: a systematic review. Popul Health Manag 2019; 22 (04) 347-361
• 82 Wiener RS, Barker AM, Carter-Harris L. et al. Stakeholder research priorities to promote implementation of shared decision-making for lung cancer screening: an American Thoracic Society and Veterans Affairs Health Services Research and Development statement. Am J Respir Crit Care Med 2022; 205 (06) 619-630
• 83 Abubaker-Sharif M, Shusted C, Myers P, Myers R. Primary care physician perceptions of shared decision making in lung cancer screening. J Cancer Educ 2022; 37 (04) 1099-1107
• 84 Eberth JM, McDonnell KK, Sercy E. et al. A national survey of primary care physicians: perceptions and practices of low-dose CT lung cancer screening. Prev Med Rep 2018; 11: 93-99
• 85 Melzer AC, Golden SE, Ono SS, Datta S, Crothers K, Slatore CG. What exactly is shared decision-making? A qualitative study of shared decision-making in lung cancer screening. J Gen Intern Med 2020; 35 (02) 546-553
• 86 Brenner AT, Malo TL, Margolis M. et al. Evaluating shared decision making for lung cancer screening. JAMA Intern Med 2018; 178 (10) 1311-1316
• 87 Fukunaga MI, Halligan K, Kodela J. et al. Tools to promote shared decision-making in lung cancer screening using low-dose CT scanning: a systematic review. Chest 2020; 158 (06) 2646-2657
• 88 Tan NQP, Nishi SPE, Lowenstein LM. et al. Impact of the shared decision-making process on lung cancer screening decisions. Cancer Med 2022; 11 (03) 790-797
• 89 Studts JL, Hirsch EA, Silvestri GA. Shared decision-making during a lung cancer screening visit: is it a barrier or does it bring value?. Chest 2023; 163 (01) 251-254
• 90 Walsh JME, Karliner L, Smith A. et al. LungCare: encouraging shared decision-making in lung cancer screening-a randomized trial. J Gen Intern Med 2023; 38 (14) 3115-3122
• 91 Schapira MM, Hubbard RA, Whittle J. et al. Lung cancer screening decision aid designed for a primary care setting: a randomized clinical trial. JAMA Netw Open 2023; 6 (08) e2330452
• 92 Caverly TJ, Wiener RS, Kumbier K, Lowery J, Fagerlin A. Prediction-augmented shared decision-making and lung cancer screening uptake. JAMA Netw Open 2024; 7 (07) e2419624
• 93 Murphy NR, Crothers K, Snidarich M. et al. The use of a tailored decision aid to improve understanding of lung cancer screening in people with HIV. Chest 2025; 167 (01) 259-269
• 94 Hoffman RM, Reuland DS, Volk RJ. The Centers for Medicare & Medicaid Services Requirement for shared decision-making for lung cancer screening. JAMA 2021; 325 (10) 933-934
• 95 Tan NQP, Lowenstein LM, Douglas EE. et al. The telehealth shared decision-making coaching and navigation in primary care (TELESCOPE) intervention: a study protocol for delivering shared decision-making for lung cancer screening by patient navigators. BMC Prim Care 2024; 25 (01) 373
• 96 Lowenstein LM, Shih YT, Minnix J. et al. A protocol for a cluster randomized trial of care delivery models to improve the quality of smoking cessation and shared decision making for lung cancer screening. Contemp Clin Trials 2023; 128: 107141
• 97 Khanna A, Fix GM, McCullough MB. et al. Implementing shared decision-making for lung cancer screening across a Veterans Health Administration Hospital Network: a hybrid effectiveness-implementation study protocol. Ann Am Thorac Soc 2022; 19 (03) 476-483
• 98 Skurla SE, Leishman NJ, Fagerlin A, Wiener RS, Lowery J, Caverly TJ. Clinician perceptions on using decision tools to support prediction-based shared decision making for lung cancer screening. MDM Policy Pract 2024; 9 (01) 23 814683241252786
• 99 Volk RJ, Myers RE, Arenberg D. et al. The American Cancer Society National Lung Cancer Roundtable strategic plan: current challenges and future directions for shared decision making for lung cancer screening. Cancer 2024; 130 (23) 3996-4011
• 100 Mazzone P, Powell CA, Arenberg D. et al. Components necessary for high-quality lung cancer screening: American College of Chest Physicians and American Thoracic Society Policy Statement. Chest 2015; 147 (02) 295-303
• 101 Bandi P, Star J, Ashad-Bishop K, Kratzer T, Smith R, Jemal A. Lung cancer screening in the US, 2022. JAMA Intern Med 2024; 184 (08) 882-891
• 102 Taylor KL, Cox LS, Zincke N, Mehta L, McGuire C, Gelmann E. Lung cancer screening as a teachable moment for smoking cessation. Lung Cancer 2007; 56 (01) 125-134
• 103 Williams RM, Cordon M, Eyestone E. et al; Lung Screening, Tobacco, Health Trial. Improved motivation and readiness to quit shortly after lung cancer screening: evidence for a teachable moment. Cancer 2022; 128 (10) 1976-1986
• 104 Balata H, Traverse-Healy L, Blandin-Knight S. et al. Attending community-based lung cancer screening influences smoking behaviour in deprived populations. Lung Cancer 2020; 139: 41-46
• 105 Tanner NT, Thomas NA, Ward R. et al. Association of cigarette type and nicotine dependence in patients presenting for lung cancer Screening. Chest 2020; 158 (05) 2184-2191
• 106 Thomas NA, Ward R, Tanner NT. et al. Factors associated with smoking cessation attempts in lung cancer screening: a secondary analysis of the national lung screening trial. Chest 2023; 163 (02) 433-443
• 107 Williams RM, Eyestone E, Smith L. et al; On Behalf Of The Lung Screening Tobacco Health Trial. Engaging patients in smoking cessation treatment within the lung cancer screening setting: lessons learned from an NCI SCALE Trial. Curr Oncol 2022; 29 (04) 2211-2224
• 108 Bhamani A, Katsampouris E, Bojang F. et al; SUMMIT Consortium. Uptake and 4-week outcomes of an ‘opt-out’ smoking cessation referral strategy in a London-based lung cancer screening setting. BMJ Open Respir Res 2025; 12 (01) e002337
• 109 Shusted CS, Mukhtar S, Lee J. et al. Factors associated with receipt of tobacco treatment integrated with nurse navigation in a centralized lung cancer screening program at an urban academic medical center. Cancer Control 2024; 31: 10 732748241304966
• 110 Cao P, Jeon J, Levy DT. et al. Potential impact of cessation interventions at the point of lung cancer screening on lung cancer and overall mortality in the United States. J Thorac Oncol 2020; 15 (07) 1160-1169
• 111 Cao P, Smith L, Mandelblatt JS. et al. Cost-effectiveness of a telephone-based smoking cessation randomized trial in the lung cancer screening setting. JNCI Cancer Spectr 2022; 6 (04) pkac048
• 112 Cadham CJ, Cao P, Jayasekera J. et al; CISNET-SCALE Collaboration. Cost-effectiveness of smoking cessation interventions in the lung cancer screening setting: a simulation study. J Natl Cancer Inst 2021; 113 (08) 1065-1073
• 113 Evans WK, Gauvreau CL, Flanagan WM. et al. Clinical impact and cost-effectiveness of integrating smoking cessation into lung cancer screening: a microsimulation model. CMAJ Open 2020; 8 (03) E585-E592
• 114 Fucito LM, Czabafy S, Hendricks PS, Kotsen C, Richardson D, Toll BA. Association for the Treatment of Tobacco Use and Dependence/Society for Research on Nicotine and Tobacco Synergy Committee. Pairing smoking-cessation services with lung cancer screening: a clinical guideline from the Association for the Treatment of Tobacco Use and Dependence and the Society for Research on Nicotine and Tobacco. Cancer 2016; 122 (08) 1150-1159
• 115 Rojewski AM, Bailey SR, Bernstein SL. et al. Considering systemic barriers to treating tobacco use in clinical settings in the United States. Nicotine Tob Res 2019; 21 (11) 1453-1461
• 116 Roughgarden KL, Toll BA, Tanner NT, Frazier CC, Silvestri GA, Rojewski AM. Tobacco treatment specialist training for lung cancer screening providers. Am J Prev Med 2021; 61 (05) 765-768
• 117 Rendle KA, Burnett-Hartman AN, Neslund-Dudas C. et al. Evaluating lung cancer screening across diverse healthcare systems: a process model from the lung PROSPR consortium. Cancer Prev Res (Phila) 2020; 13 (02) 129-136
• 118 Lang AE. Update on the National Cancer Institute's smoking cessation at lung examination collaboration trials. Chest 2024; 165 (06) 1302-1306
• 119 Meza R, Jeon J, Jimenez-Mendoza E. et al. National cancer institute smoking cessation at lung examination trials brief report: baseline characteristics and comparison with the U.S. general population of lung cancer screening-eligible patients. JTO Clin Res Rep 2022; 3 (07) 100352
• 120 Taylor KL, Williams RM, Li T. et al; Georgetown Lung Screening, Tobacco, and Health Trial. A randomized trial of telephone-based smoking cessation treatment in the lung cancer screening setting. J Natl Cancer Inst 2022; 114 (10) 1410-1419
• 121 Foley KL, Dressler EV, Weaver KE. et al; Optimizing Lung Screening Trial Writing Team. The optimizing lung screening trial (WF-20817CD): multicenter randomized effectiveness implementation trial to increase tobacco use cessation for individuals undergoing lung screening. Chest 2023; 164 (02) 531-543
• 122 Fu SS, Rothman AJ, Vock DM. et al. Optimizing longitudinal tobacco cessation treatment in lung cancer screening: a sequential, multiple assignment, randomized trial. JAMA Netw Open 2023; 6 (08) e2329903
• 123 Cartmel B, Fucito LM, Bold KW. et al. Effect of a personalized tobacco treatment intervention on smoking abstinence in individuals eligible for lung cancer screening. J Thorac Oncol 2024; 19 (04) 643-649
• 124 Park ER, Haas JS, Rigotti NA. et al. Integrating tobacco treatment into lung cancer screening: the screen assist factorial randomized clinical trial. JAMA Intern Med 2025; 185 (05) 531-539
• 125 Park ER, Neil JM, Noonan E. et al. Leveraging the clinical timepoints in lung cancer screening to engage individuals in tobacco treatment. JNCI Cancer Spectr 2022; 6 (06) pkac073
• 126 Cinciripini PM, Minnix JA, Kypriotakis G. et al. Smoking cessation interventions in the lung cancer screening setting: a randomized clinical trial. JAMA Intern Med 2025; 185 (03) 284-291
• 127 Buttery SC, Williams P, Mweseli R. et al. Immediate smoking cessation support versus usual care in smokers attending a targeted lung health check: the QuLIT trial. BMJ Open Respir Res 2022; 9 (01) e001030
• 128 Williams PJ, Philip KE, Alghamdi SM. et al. Strategies to deliver smoking cessation interventions during targeted lung health screening - a systematic review and meta-analysis. Chron Respir Dis 2023; 20: 14 799731231183446
• 129 2022 BRFSS Questionnaire. Accessed September 18, 2025 at: https://www.cdc.gov/brfss/questionnaires/pdf-ques/2022-BRFSS-Questionnaire-508.pdf
• 130 National Health Interview Survey. Accessed September 18, 2025 at: https://www.cdc.gov/nchs/nhis/index.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fnchs%2Fnhis%2Findex.htm
• 131 Health Information National Trends Survey. Accessed September 18, 2025 at: https://hints.cancer.gov/Default.aspx
• 132 Rai A, Doria-Rose VP, Silvestri GA, Yabroff KR. Evaluating lung cancer screening uptake, outcomes, and costs in the United States: challenges with existing data and recommendations for improvement. J Natl Cancer Inst 2019; 111 (04) 342-349
• 133 Sarkar S, Choa E, Manzo LL. et al. Predictors and uptake of lung cancer screening in the US: an integrative literature review. Lung Cancer 2025; 203: 108529
• 134 Núñez ER, Ito Fukunaga M, Stevens GA. et al. Review of interventions that improve uptake of lung cancer screening: a cataloging of strategies that have been shown to work (or not). Chest 2024; 166 (03) 632-648
• 135 Elston Lafata J, Rendle KA, Wainwright JV. et al. Characterizing the design of and emerging evidence for health care organization-based lung cancer screening interventions: a systematic review. MDM Policy Pract 2025; 10 (01) 23 814683251328375
• 136 Christensen J, Prosper AE, Wu CC. et al. ACR lung-RADS v2022: assessment categories and management recommendations. J Am Coll Radiol 2024; 21 (03) 473-488
• 137 Sakoda LC, Henderson LM, Rivera MP. Adherence to lung cancer screening: what exactly are we talking about?. Ann Am Thorac Soc 2021; 18 (12) 1951-1952
• 138 Alshora S, McKee BJ, Regis SM. et al. Adherence to radiology recommendations in a clinical CT lung screening program. J Am Coll Radiol 2018; 15 (02) 282-286
• 139 Erkmen CP, Dako F, Moore R. et al. Adherence to annual lung cancer screening with low-dose CT scan in a diverse population. Cancer Causes Control 2021; 32 (03) 291-298
• 140 Kim RY, Rendle KA, Mitra N. et al. Adherence to annual lung cancer screening and rates of cancer diagnosis. JAMA Netw Open 2025; 8 (03) e250942
• 141 Lake M, Shusted CS, Juon HS. et al. Black patients referred to a lung cancer screening program experience lower rates of screening and longer time to follow-up. BMC Cancer 2020; 20 (01) 561
• 142 Lin Y, Fu M, Ding R. et al. Patient adherence to lung CT screening reporting & data system-recommended screening intervals in the United States: a systematic review and meta-analysis. J Thorac Oncol 2022; 17 (01) 38-55
• 143 Lopez-Olivo MA, Maki KG, Choi NJ. et al. Patient adherence to screening for lung cancer in the US: a systematic review and meta-analysis. JAMA Netw Open 2020; 3 (11) e2025102
• 144 Spalluto LB, Lewis JA, LaBaze S. et al. Association of a lung screening program coordinator with adherence to annual CT lung screening at a large academic institution. J Am Coll Radiol 2020; 17 (02) 208-215
• 145 Triplette M, Wenger DS, Shahrir S. et al. Patient identification of lung cancer screening follow-up recommendations and the association with adherence. Ann Am Thorac Soc 2022; 19 (05) 799-806
• 146 Pinsky PF, Gierada DS, Black W. et al. Performance of lung-RADS in the national lung screening trial: a retrospective assessment. Ann Intern Med 2015; 162 (07) 485-491
• 147 Tailor TD, Gutman R, An N. et al. Positive screens are more likely in a national lung cancer screening registry than the national lung screening trial. J Am Coll Radiol 2025; 22 (06) 644-652
• 148 Henderson LM, Bacchus L, Benefield T, Huamani Velasquez R, Rivera MP. Rates of positive lung cancer screening examinations in academic versus community practice. Transl Lung Cancer Res 2020; 9 (04) 1528-1532
• 149 Burnett-Hartman AN, Carroll NM, Honda SA. et al. Community-based lung cancer screening results in relation to patient and radiologist characteristics: the PROSPR consortium. Ann Am Thorac Soc 2022; 19 (03) 433-441
• 150 Jacobs CD, Jafari ME. Early results of lung cancer screening and radiation dose assessment by low-dose CT at a community hospital. Clin Lung Cancer 2017; 18 (05) e327-e331
• 151 McKee BJ, Regis SM, McKee AB, Flacke S, Wald C. Performance of ACR lung-RADS in a clinical CT lung screening program. J Am Coll Radiol 2015; 12 (03) 273-276
• 152 Rendle KA, Saia CA, Vachani A. et al. Rates of downstream procedures and complications associated with lung cancer screening in routine clinical practice : a retrospective cohort study. Ann Intern Med 2024; 177 (01) 18-28
• 153 Rivera MP, Durham DD, Long JM. et al. Receipt of recommended follow-up care after a positive lung cancer screening examination. JAMA Netw Open 2022; 5 (11) e2240403
• 154 Ahmed A, Hippe DS, Snidarich M, Crothers K, Triplette M. Delays in recommended follow-up after positive findings in lung cancer screening. Ann Am Thorac Soc 2023; 20 (08) 1175-1181
• 155 Mendoza DP, Petranovic M, Som A. et al. Lung-RADS category 3 and 4 nodules on lung cancer screening in clinical practice. AJR Am J Roentgenol 2022; 219 (01) 55-65
• 156 Silvestri GA, Goldman L, Tanner NT. et al. Outcomes from more than 1 million people screened for lung cancer with low-dose CT imaging. Chest 2023; 164 (01) 241-251
• 157 Zhang EW, Shepard JO, Kuo A. et al. Characteristics and outcomes of lung cancers detected on low-dose lung cancer screening CT. Cancer Epidemiol Biomarkers Prev 2021; 30 (08) 1472-1479
• 158 Lin Y, Tabatabaei SMH, Ding R. et al. Analyzing patient characteristics and lung cancer outcomes pre and post the 2021 USPSTF lung cancer screening guidelines: experience from a large academic institution. J Thorac Imaging 2025; (e-pub ahead of print)
• 159 Lafata KJ, Read C, Tong BC. et al. Lung cancer screening in clinical practice: a 5-year review of frequency and predictors of lung cancer in the screened population. J Am Coll Radiol 2024; 21 (05) 767-777
• 160 Dyer DS, White C, Conley Thomson C. et al. A quick reference guide for incidental findings on lung cancer screening CT examinations. J Am Coll Radiol 2023; 20 (02) 162-172
• 161 Gareen IF, Gutman R, Sicks J. et al. Significant incidental findings in the national lung screening trial. JAMA Intern Med 2023; 183 (07) 677-684
• 162 Bonney A, Pascoe DM, McCusker MW. et al. Incidental findings during lung low-dose computed tomography cancer screening in Australia and Canada, 2016-21: a prospective observational study. Med J Aust 2025; 222 (08) 403-411
• 163 Rampinelli C, Preda L, Maniglio M. et al. Extrapulmonary malignancies detected at lung cancer screening. Radiology 2011; 261 (01) 293-299
• 164 van de Wiel JC, Wang Y, Xu DM. et al; NELSON study group. Neglectable benefit of searching for incidental findings in the Dutch-Belgian lung cancer screening trial (NELSON) using low-dose multidetector CT. Eur Radiol 2007; 17 (06) 1474-1482
• 165 Henderson LM, Kim RY, Tanner NT. et al. Lung cancer screening and incidental findings: a research agenda: an official American Thoracic Society research statement. Am J Respir Crit Care Med 2025; 211 (03) 436-451
• 166 Kinsinger LS, Anderson C, Kim J. et al. Implementation of lung cancer screening in the Veterans Health Administration. JAMA Intern Med 2017; 177 (03) 399-406
• 167 Morgan L, Choi H, Reid M, Khawaja A, Mazzone PJ. Frequency of incidental findings and subsequent evaluation in low-dose computed tomographic scans for lung cancer screening. Ann Am Thorac Soc 2017; 14 (09) 1450-1456
• 168 Reiter MJ, Nemesure A, Madu E, Reagan L, Plank A. Frequency and distribution of incidental findings deemed appropriate for S modifier designation on low-dose CT in a lung cancer screening program. Lung Cancer 2018; 120: 1-6
• 169 Janssen K, Schertz K, Rubin N, Begnaud A. Incidental findings in a decentralized lung cancer screening program. Ann Am Thorac Soc 2019; 16 (09) 1198-1201
• 170 Henderson LM, Chiles C, Perera P. et al. Variability in reporting of incidental findings detected on lung cancer screening. Ann Am Thorac Soc 2023; 20 (04) 617-620
• 171 Melzer AC, Atoma B, Fabbrini AE, Campbell M, Clothier BA, Fu SS. Variation in reporting of incidental findings on initial lung cancer screening and associations with clinician assessment. J Am Coll Radiol 2024; 21 (01) 118-127
• 172 Al-Antary N, Hirko KA, Cassidy-Bushrow AE. et al. Coronary artery calcification identified on lung cancer screening CT Scans: a scoping review. Chest 2025; 168 (03) 719-736
• 173 Rivera MP, Katki HA, Tanner NT. et al. Addressing disparities in lung cancer screening eligibility and healthcare access. An official American Thoracic Society statement. Am J Respir Crit Care Med 2020; 202 (07) e95-e112
• 174 Tailor TD, Choudhury KR, Tong BC, Christensen JD, Sosa JA, Rubin GD. Geographic access to CT for lung cancer screening: a census tract-level analysis of cigarette smoking in the United States and driving distance to a CT facility. J Am Coll Radiol 2019; 16 (01) 15-23
• 175 Simkin J, Khoo E, Darvishian M. et al. Addressing inequity in spatial access to lung cancer screening. Curr Oncol 2023; 30 (09) 8078-8091
• 176 Sahar L, Douangchai Wills VL, Liu KK, Kazerooni EA, Dyer DS, Smith RA. Using geospatial analysis to evaluate access to lung cancer screening in the United States. Chest 2021; 159 (02) 833-844
• 177 Sahar L, Douangchai Wills VL, Liu KKA. et al. Geographic access to lung cancer screening among eligible adults living in rural and urban environments in the United States. Cancer 2022; 128 (08) 1584-1594
• 178 Welch AC, Gorden JA, Mooney SJ, Wilshire CL, Zeliadt SB. Understanding Washington State's low uptake of lung cancer screening in two steps: a geospatial analysis of patient travel time and health care availability of imaging sites. Chest 2024; 166 (03) 622-631
• 179 Tailor TD, Tong BC, Gao J, Choudhury KR, Rubin GD. A geospatial analysis of factors affecting access to CT facilities: implications for lung cancer screening. J Am Coll Radiol 2019; 16 (12) 1663-1668
• 180 Peña MA, Sudarshan A, Muns CM. et al. Analysis of geographic accessibility of breast, lung, and colorectal cancer screening centers among American Indian and Alaskan Native tribes. J Am Coll Radiol 2023; 20 (07) 642-651
• 181 Lozier JW, Fedewa SA, Smith RA, Silvestri GA. Lung cancer screening eligibility and screening patterns among Black and White adults in the United States. JAMA Netw Open 2021; 4 (10) e2130350
• 182 Sun J, Perraillon MC, Myerson R. The impact of medicare health insurance coverage on lung cancer screening. Med Care 2022; 60 (01) 29-36
• 183 Tailor TD, Bell S, Doo FX, Carlos RC. Repeat annual lung cancer screening after baseline screening among screen-negative individuals: no-cost coverage is not enough. J Am Coll Radiol 2023; 20 (01) 29-36
• 184 Korn AR, Walsh-Bailey C, Correa-Mendez M. et al. Social determinants of health and US cancer screening interventions: a systematic review. CA Cancer J Clin 2023; 73 (05) 461-479
• 185 Studts JL, Carter-Bawa L, Feldman J. et al. Embracing compassion for people facing lung cancer-the American Cancer Society National Lung Cancer Roundtable. Ann Intern Med 2025; 178 (07) 1027-1028
• 186 Wilder FG, Cangut B, Jindani R, Abioye O, Florez N. Lung cancer screening among minority groups: identifying gaps in screening and opportunities for intervention. JTCVS Open 2024; 21: 341-348
• 187 Resong PJ, Niu J, Duhon GF. et al. Acceptability of personalized lung cancer screening program among primary care providers. Cancer Prev Res (Phila) 2024; 17 (02) 51-57
• 188 Ten Haaf K, van der Aalst CM, de Koning HJ, Kaaks R, Tammemägi MC. Personalising lung cancer screening: an overview of risk-stratification opportunities and challenges. Int J Cancer 2021; 149 (02) 250-263
• 189 Tammemägi MC, Church TR, Hocking WG. et al. Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts. PLoS Med 2014; 11 (12) e1001764
• 190 Ten Haaf K, Jeon J, Tammemägi MC. et al. Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med 2017; 14 (04) e1002277
• 191 Kim YW, Lee CT. Advancing the implementation of risk model-based lung cancer screening. J Thorac Oncol 2025; 20 (04) 419-421
• 192 Yang JJ, Wen W, Zahed H. et al. Lung cancer risk prediction models for Asian ever-smokers. J Thorac Oncol 2024; 19 (03) 451-464
• 193 Lam DC, Liam CK, Andarini S. et al. Lung cancer screening in Asia: an expert consensus report. J Thorac Oncol 2023; 18 (10) 1303-1322
• 194 Boyle P, Chapman CJ, Holdenrieder S. et al. Clinical validation of an autoantibody test for lung cancer. Ann Oncol 2011; 22 (02) 383-389
• 195 Chapman CJ, Healey GF, Murray A. et al. EarlyCDT-lung test: improved clinical utility through additional autoantibody assays. Tumour Biol 2012; 33 (05) 1319-1326
• 196 Duarte A, Corbett M, Melton H. et al. EarlyCDT lung blood test for risk classification of solid pulmonary nodules: systematic review and economic evaluation. Health Technol Assess 2022; 26 (49) 1-184
• 197 Macdonald IK, Murray A, Healey GF. et al. Application of a high throughput method of biomarker discovery to improvement of the EarlyCDT-lung test. PLoS One 2012; 7 (12) e51002
• 198 Massion PP, Healey GF, Peek LJ. et al. Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer. J Thorac Oncol 2017; 12 (03) 578-584
• 199 Sullivan FM, Mair FS, Anderson W. et al; Early Diagnosis of Lung Cancer Scotland (ECLS) Team. Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging. Eur Respir J 2021; 57 (01) 2000670
• 200 Sullivan FM, Mair FS, Anderson W. et al. Five year mortality in an RCT of a lung cancer biomarker to select people for low dose CT screening. PLoS One 2025; 20 (01) e0306163
• 201 Silvestri GA, Tanner NT, Kearney P. et al; PANOPTIC Trial Team. Assessment of plasma proteomics biomarker's ability to distinguish benign from malignant lung nodules: results of the PANOPTIC (pulmonary nodule plasma proteomic classifier) trial. Chest 2018; 154 (03) 491-500
• 202 Guida F, Sun N, Bantis LE. et al; Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer. Assessment of lung cancer risk on the basis of a biomarker panel of circulating proteins. JAMA Oncol 2018; 4 (10) e182078
• 203 Fahrmann JF, Marsh T, Irajizad E. et al. Blood-based biomarker panel for personalized lung cancer risk assessment. J Clin Oncol 2022; 40 (08) 876-883
• 204 Sozzi G, Boeri M, Rossi M. et al. Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study. J Clin Oncol 2014; 32 (08) 768-773
• 205 Montani F, Marzi MJ, Dezi F. et al. miR-Test: a blood test for lung cancer early detection. J Natl Cancer Inst 2015; 107 (06) djv063
• 206 Pastorino U, Boeri M, Sestini S. et al. Baseline computed tomography screening and blood microRNA predict lung cancer risk and define adequate intervals in the BioMILD trial. Ann Oncol 2022; 33 (04) 395-405
• 207 Lampignano R, Kloten V, Krahn T, Schlange T. Integrating circulating miRNA analysis in the clinical management of lung cancer: present or future?. Mol Aspects Med 2020; 72: 100844
• 208 Yu H, Raut JR, Bhardwaj M. et al. A serum microRNA signature for enhanced selection of people for lung cancer screening. Cancer Commun (Lond) 2022; 42 (11) 1222-1225
• 209 Tao R, Wang D, Pei W. et al. Highly sensitive and specific panels of plasma exosomal microRNAs for identification of malignant pulmonary nodules. Clin Respir J 2024; 18 (11) e70034
• 210 Liu MC, Oxnard GR, Klein EA, Swanton C, Seiden MV. CCGA Consortium. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol 2020; 31 (06) 745-759
• 211 Jacobsen KK, Schnohr P, Jensen GB, Bojesen SE. AHRR (cg05575921) methylation safely improves specificity of lung cancer screening eligibility criteria: a cohort study. Cancer Epidemiol Biomarkers Prev 2022; 31 (04) 758-765
• 212 Cristiano S, Leal A, Phallen J. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 2019; 570 (7761): 385-389
• 213 Bhalla S, Yi S, Gerber DE. Emerging strategies in lung cancer screening: blood and beyond. Clin Chem 2024; 70 (01) 60-67
• 214 van den Broek D, Groen HJM. Screening approaches for lung cancer by blood-based biomarkers: challenges and opportunities. Tumour Biol 2024; 46 (s1): S65-S80
• 215 Cheo HM, Ong CYG, Ting Y. A systematic review of AI performance in lung cancer detection on CT thorax. Healthcare (Basel) 2025; 13 (13) 1510
• 216 Lee M, Hwang EJ, Lee JH. et al. Artificial intelligence for low-dose CT lung cancer screening: comparison of utilization scenarios. AJR Am J Roentgenol 2025; 225 (01) e2532829
• 217 Walstra ANH, Lancaster HL, Heuvelmans MA. et al. Feasibility of AI as first reader in the 4-IN-THE-LUNG-RUN lung cancer screening trial: impact on negative-misclassifications and clinical referral rate. Eur J Cancer 2025; 216: 115214
• 218 Lancaster HL, Jiang B, Davies MPA. et al. Histological proven AI performance in the UKLS CT lung cancer screening study: potential for workload reduction. Eur J Cancer 2025; 220: 115324
• 219 Leonard S, Patel MA, Zhou Z, Le H, Mondal P, Adams SJ. Comparing artificial intelligence and traditional regression models in lung cancer risk prediction using a systematic review and meta-analysis. J Am Coll Radiol 2025; 22 (06) 675-690
• 220 Ardila D, Kiraly AP, Bharadwaj S. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019; 25 (06) 954-961
• 221 Mikhael PG, Wohlwend J, Yala A. et al. Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J Clin Oncol 2023; 41 (12) 2191-2200
• 222 Adams SJ, Mikhael P, Wohlwend J, Barzilay R, Sequist LV, Fintelmann FJ. Artificial intelligence and machine learning in lung cancer screening. Thorac Surg Clin 2023; 33 (04) 401-409
• 223 Quanyang W, Yao H, Sicong W. et al. Artificial intelligence in lung cancer screening: detection, classification, prediction, and prognosis. Cancer Med 2024; 13 (07) e7140