Over- and underscreening for lung cancer

Introduction

Lung cancer remains the leading cause of cancer-related mortality globally (1,2). Since the landmark National Lung Screening Trial (NLST), low-dose computed tomography (LDCT) screening has significantly reduced lung cancer mortality in high-risk populations (3), prompting the global rollout of screening programs (4,5). Evidence indicates that a substantial proportion of lung cancers are curable if detected at an early stage (6,7). However, this curative potential is often not fully realized under current screening frameworks. The rising incidence of lung cancer among non-smokers and younger individuals, particularly in East Asia (8-14), suggests that conventional age- and smoking-based criteria may no longer be sufficient.

This narrative review evaluates the clinical discrepancies in current LDCT implementation and investigates the biological and epidemiological drivers behind these observed misalignments. By synthesizing evidence on tumor development patterns and screening outcomes, we aim to clarify how the limitations of conventional eligibility frameworks contribute to current clinical dilemmas within an evolving epidemiological landscape. Finally, we explore the potential for risk-adapted frameworks to optimize screening strategies and align clinical practice with tumor natural history. We present this article in accordance with the Narrative Review reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0629/rc).

Methods

A literature search was performed in PubMed and MEDLINE through May 2026. The analytical logic involved evaluating observed clinical screening discrepancies and subsequently investigating the natural history of diverse lung cancer phenotypes to identify the biological drivers of these misalignments. This synthesis provided the rationale for exploring adaptive screening frameworks (Table 1).

The shifting landscape of lung cancer screening

Global discrepancies in screening outcomes

LDCT screening outcomes exhibit significant disparities between Western and Eastern cohorts, reflecting fundamental differences in participant demographics and tumor characteristics. Western trials, such as the NLST and NELSON, predominantly include heavy smokers with a high frequency of solid nodules, reporting approximately 60% of cases as stage I (15,16). Conversely, East Asian initiatives identify a much higher proportion of early-stage disease, with stage 0–I cases ranging from 80% to over 95% (8-14). This trend is largely driven by the high prevalence of ground-glass nodules (GGNs) in young, non-smoking individuals, particularly women. The systematic exclusion of these emerging high-risk groups under conventional smoking-centric criteria highlights a critical mismatch between current eligibility and the evolving epidemiological reality.

Beyond these statistical disparities, a troubling clinical reality has emerged as a screening paradox. On one hand, a significant proportion of patients presenting with advanced-stage lung cancer remain “CT-naive”. On the other hand, “lung cancer anxiety”, particularly in East Asia, has driven intensive screening among low-risk populations. This misalignment manifests as a surge in early-stage diagnoses without a proportional decline in lung cancer mortality. Reconciling this paradox requires moving beyond conventional risk factors to examine the divergent natural histories and evolutionary trajectories of lung cancer phenotypes.

Evolutionary trajectories of lung cancer

Accumulating evidence suggests that lung cancer is not a monolithic entity but mainly encompasses three distinct evolutionary trajectories, each with a different relationship to screening efficacy. The first trajectory involves occult primary tumors characterized by early systemic metastasis, rendering LDCT screening fundamentally ineffective as the disease is already systemic upon detection (e.g., most small cell lung cancers). The second consists of de novo solid nodules that exhibit rapid progression. These malignancies follow an accelerated evolutionary pace, often exhausting their narrow curative time windows between standard screening rounds, which accounts for poor prognosis.

In contrast, a significant proportion of screen-detected cases, particularly in East Asian cohorts, follow a third trajectory: GGN-associated progression. GGN-featured lung cancer is now recognized as a distinct clinical subtype and a specific stage in the natural evolution of adenocarcinoma, characterized by unique biological features and a favorable prognosis (14,17). This subtype is characterized by a protracted indolent phase that might even span 10 to 20 years before transitioning into a lethal malignancy (Figure 1) (14,17). Pathologically, this evolution follows a well-defined sequence from atypical adenomatous hyperplasia to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and finally invasive adenocarcinoma (from stages I to IV) (18). Radiologically, this is mirrored by the transition from pure GGNs to solid-dominant nodules (Figures 1C,2). Genomic mapping of over 1,000 lung adenocarcinoma samples confirms that these advanced malignancies are rarely de novo events but represent the culmination of accumulated mutational burdens originating from these indolent precursors (18).

Figure 1 Heterogeneous clinical trajectories of GGNs under LDCT surveillance. (A,B) Progression from pure GGN to mixed GGN. (A) Case 1: a pure GGN evolved into a mixed GGN over a 15-year period (2001–2016). The final pathology was acinar-predominant adenocarcinoma, pT1bN0M0. (B) Case 2: a pure GGN showing gradual increase in size and density over 11 years of surveillance (2011–2022). The final pathology was papillary-predominant adenocarcinoma, pT1bN0M0. (C) Progression from pure GGN to mixed GGN and finally solid nodule. Case 3: serial CT images over 13 years illustrate the steady evolution of a pure GGN (2010–2019; C1-C7) into a mixed GGN (2020–2021; C8,C9) and finally a solid nodule (2022–2023; C10,C11). The final pathology was acinar-predominant adenocarcinoma, pT1bN0M0. (D) Progression from mixed GGN to advanced disease. Case 4: this time series documents the aggressive progression of a GGN, marked by the rapid dominance of a solid component (2014–2018; D1-D7), ultimately resulting in advanced disease with malignant pleural effusion (2020; D8). Biopsy of the nodule confirmed lung adenocarcinoma. CT, computed tomography; GGN, ground-glass nodule; LDCT, low-dose computed tomography.

Figure 2 Classification and clinical trajectories of pulmonary nodules. This schematic illustrates the categorization and potential outcomes of screen-detected pulmonary nodules. The left column depicts the primary radiological classification. The central column outlines their possible clinical trajectories. The right column indicates the corresponding pathological suspicion. Approximately 95% of persistent, slowly growing GGNs are malignant. GGN, ground-glass nodule.

However, the tempo of this evolution is highly heterogeneous. While most GGN-featured adenocarcinomas follow a slow, measurable course, certain molecular subtypes, such as fusion-positive adenocarcinomas (e.g., ALK fusion) (19), follow a fast-track trajectory. Although these fusions are detectable at the GGN stage, their rapid progression suggests they quickly exhaust their curative window, necessitating earlier detection strategies. Conversely, for the majority of persistent GGNs, the challenge lies in distinguishing biological malignancy from clinical urgency. GGNs are heterogeneous (14,17,20); some resolve, others persist stably, and a subset progresses (Figure 2). While approximately 95% of persistent and slowly-growing GGNs are pathologically malignant (21), their indolent natural history means they do not always mandate immediate surgical intervention.

The clinical dilemma arises from the cost of deferred management. Deferring the interception of a progressing GGN until it manifests as a solid-dominant nodule necessitates treatment escalation, shifting from a simple wedge resection to a lobectomy with systematic lymph node dissection and potential adjuvant therapy, while accepting a tangible risk of recurrence (Figure 3). Data highlight a contrast in outcomes: whereas interception during the AIS or MIA phase achieves nearly 100% 10-year recurrence-free survival (RFS) (Figure 3) (6,7,22), invasive disease at stage I drops 5-year RFS to approximately 80% (17). This biological reality reinforces that GGNs represent a pre-lethal phase when timely intervention, synchronized with the lesion’s specific evolutionary pace, could secure a definitive cure with minimal intervention.

Figure 3 Paradigm shift in intervention: from standard care for invasive cancer to securing cure within the curative time window. This schematic contrasts the diagnostic and treatment paradigms for lung cancer at two distinct stages. The left panel depicts the management of typical invasive lung cancer (stages I–IV), involving standard preoperative staging (e.g., bronchoscope, PET scan), radical resection with systematic lymph node dissection, potential adjuvant therapy, and intensive surveillance, with a variable 5-year survival rate (6–90%). The right panel illustrates the same patient’s imaging 15 years ago, showing a pure GGN representing a pre-invasive stage within the “curative time window”. Management at this stage requires simplified preoperative workup, sublobar resection without lymph node dissection, less frequent postoperative surveillance, and achieves a 10-year RFS of 100%. This contrast raises an important clinical question: is intervention during the curative time window an act of overdiagnosis and overtreatment, or a definitive opportunity for cure? GGN, ground-glass nodule; MRI, magnetic resonance imaging; PET, positron emission tomography; RFS, recurrence-free survival.

The rising concern of overscreening

Real-world clinical practice frequently outpaces established guidelines, as evidenced by the rapid expansion of LDCT screening to broader populations beyond traditional high-risk criteria. However, these evolving screening behaviors often manifest in patterns of overscreening, characterized by excessive frequency and intensity without robust empirical evidence to demonstrate a commensurate improvement in health outcomes (23). This phenomenon represents a critical facet of the screening paradox, where the drive for early detection leads to clinical actions that lack a solid foundation in evidence-based benefit.

Overscreening manifests primarily as excessive LDCT frequency in low-risk individuals or unnecessarily intensive follow-up for indolent or clinically insignificant lesions. In regions such as China, the low cost of LDCT (approximately $30) and its inclusion in routine health examination packages (24-26) have normalized annual screening for many low-risk individuals, including never-smokers with negative baseline scans. This trend is further exacerbated by the detection of subclinical lesions, such as GGNs, which frequently trigger short-interval follow-up recommendations of 3–6 months (20,27-29). Despite evidence that most small GGNs follow an indolent course, data indicate that 37.2% of patients receive unnecessary repeat computed tomography (CT) scans during nodule surveillance, with this rate rising to 57.5% for pure GGNs compared to 16.2% for solid nodules (30). It is therefore critical to distinguish a lesion’s biological malignancy from its clinical urgency, as long-term surveillance within a defined curative time window has proven to be a safe alternative to immediate intervention (31,32).

Evidence from major clinical trials suggests that even within guideline-eligible cohorts, rigid annual protocols may constitute overscreening for a subset of participants. The MILD trial (33) demonstrated that biennial screening for high-risk individuals with negative baseline scans achieved mortality reduction comparable to annual protocols. Similarly, risk-based modeling indicates that over half of NLST participants with negative baseline scans could safely extend their screening intervals (34). The consequences of failing to adopt more flexible intervals are significant, as overscreening imposes a cumulative radiation burden and contributes to high benign resection rates, which range from 6% to over 30% (35,36). Such unnecessary surgical interventions lead to physical morbidity and financial strain, ultimately undermining public trust in screening programs.

Recent studies (37,38) have reported increased lung cancer incidence without a concomitant decline in late-stage disease, a finding frequently interpreted as an “overdiagnosis signal”. However, this requires a more nuanced biological interpretation. While the rising detection of indolent GGNs is often labeled as overdiagnosis, the TALENT study (8) confirmed that 36% of pure GGNs already represent pathologically invasive adenocarcinomas. Consistent with the protracted natural history described in the evolutionary trajectories section, the current 10-year observation windows may be insufficient to fully capture the mortality benefits of early interception. The persistence of late-stage disease likely reflects the “screening paradox” in clinical practice: while over-screened low-risk individuals fuel the overdiagnosis signal, many patients diagnosed with advanced-stage disease remain “CT-naïve” at the time of diagnosis. A shift toward prolonged, trajectory-aligned surveillance of indolent GGNs could therefore mitigate the harms of overtreatment while facilitating the redirection of clinical resources toward individuals at true risk of aggressive disease.

The challenges of underscreening

While overscreening consumes significant healthcare resources, the second facet of the clinical paradox is the persistent challenge of underscreening, which remains a primary driver of advanced-stage lung cancer presentations globally. Underscreening encompasses not only the failure of high-risk individuals to undergo recommended LDCT but also a systemic eligibility mismatch where current guidelines fail to capture individuals developing lung cancer outside traditional risk factors. Whether driven by restrictive criteria, socioeconomic barriers, or biological factors, the ultimate consequence of underscreening is the loss of the “curative time window”, allowing aggressive malignancies to progress to an incurable stage.

A profound eligibility mismatch is particularly evident in East Asia, where an increasing proportion of lung cancers occur in never-smokers (8,13,39). Current age- and smoking-based guidelines cover only approximately 50% of the Chinese lung cancer population (40), yet the TALENT trial demonstrated a 2.6% detection rate in never-smokers with other risk factors, a rate exceeding that of the NLST (8,41). This trend is not exclusive to the East; the incidence among non-smokers is rising in Western cohorts as well (42,43), with data from the Mississippi Delta showing that half of diagnosed lung cancers failed to meet USPSTF eligibility criteria (44). These gaps point up an urgent need for more inclusive, risk-based models that transcend smoking history alone to address the shifting epidemiological landscape.

Beyond eligibility, underscreening is fueled by socioeconomic disparities and systemic barriers to access. In China, rural medical insurance holders are significantly more likely to receive a late-stage diagnosis than their urban counterparts, reflecting disparities in both screening awareness and facility availability (45). Similarly, in the United States and Europe, restrictive insurance policies, complex prior authorization processes, and coverage denials remain formidable obstacles to widespread LDCT implementation (46,47). These barriers leave a significant portion of the at-risk population “CT-naïve” until they present with symptomatic, advanced-stage disease, at which point the opportunity for curative intervention has often passed.

Another clinically challenging aspect of underscreening involves diagnostic misinterpretation and the occurrence of interval cancers (48,49). Aggressive nodules may be overlooked or misclassified due to variability in radiological expertise, allowing early-stage lesions to progress beyond their curative time window. Furthermore, interval cancers, those diagnosed between screening rounds, often represent aggressive histologic subtypes with rapid doubling times. In the NLST, the lung cancer mortality rate for patients with interval cancers was 84%, compared to 35% for all screen-detected cases (50). Data from the NELSON trial further demonstrated that extending screening intervals to 2.5 years significantly increased the incidence of these aggressive interval cancers (51). This highlights the limitations of fixed-interval screening and provides a compelling argument for developing risk-based intervals specifically designed to capture different evolutionary trajectories (48,52).

Discussion and clinical translation

Causes of the screening misalignment

The multifaceted discrepancies in current screening paradigms, as detailed in the preceding sections, likely result from several intersecting factors, primarily the inconsistency between standardized protocols and the highly variable natural history of different lung cancer subtypes. Current guidelines, often designed around the progression rates of tobacco-related malignancies, may fail to capture the diversity of the disease spectrum, ranging from indolent GGNs with protracted clinical courses to aggressive phenotypes that progress rapidly between screening rounds (14,17,50). This biological mismatch is exacerbated by an eligibility gap, as traditional smoking-centric criteria do not fully reflect the shifting epidemiology of the disease, particularly the increasing burden among younger non-smokers (13). Furthermore, systemic drivers such as defensive medical practices and patient anxiety often lead to excessive surveillance of low-risk lesions, contributing to the high rates of unnecessary repeat scans and benign resections observed in clinical practice (30,35).

These diverse challenges suggest that the clinical effectiveness of screening is limited by a “one-size-fits-all” approach that does not sufficiently account for the varying urgency of different tumor phenotypes. The evidence indicates that the success of early detection is not merely a matter of identifying a lesion, but of intercepting it during its specific “curative time window” before it advances to an incurable stage (22). Reconciling these systemic misalignments points toward a shift in perspective, favoring more flexible, risk-adapted frameworks that better synchronize screening intensity with both individual risk profiles and the biological potential of detected nodules. Such an approach may help balance the dual challenge of over- and underscreening by prioritizing the interception of potentially lethal disease while reducing the unnecessary physical and financial burdens associated with overtreatment.

Conceptualizing the “curative time window”

The “curative time window” provides a conceptual basis for potentially reconciling these misalignments, defined as the temporal phase during which surgical intervention alone achieves a predictable cure without the requirement for adjuvant therapy (53). This window is inherently subtype-specific; in the context of adenocarcinoma, it pathologically encompasses AIS and MIA (22), typically manifesting radiologically as pure GGNs (7). By evaluating screening intensity through the lens of these windows, there is a biological basis for transitioning from a uniform detection model toward a strategy of precision interception.

The efficacy of current protocols is often limited by a lack of synchronization between screening frequency and the varying durations of these curative windows across different tumor trajectories. Aggressive malignancies frequently possess narrow windows that are easily missed by standard annual intervals, contributing to the poor prognosis of interval cancers. In contrast, GGN-featured cancers often exhibit protracted windows spanning years or even decades. For these indolent phenotypes, a “low-frequency” surveillance approach may be biologically appropriate, allowing for the strategic selection of intervention timing while minimizing the cumulative risks of over-screening. This approach necessitates a balanced consideration of a patient’s life expectancy relative to the tumor’s progression rate; for those with long life expectancy, interception within the curative time window is advocated to avoid the morbidity and recurrence risks of later, more extensive operations (Figure 3).

While the high survival rates associated with screen-detected GGNs might sometimes reflect lead-time bias, the observed long-term plateau in RFS suggests a biological reset toward a cure rather than a mere advancement of the diagnosis date (22,54). Longitudinal evidence indicates that a subset of these precursors eventually transitions into lethal invasive disease if the curative time window is missed, suggesting they are not all purely indolent (Figure 1). Ultimately, the curative time window theory reframes the objective of screening: it emphasizes the need to optimize the capture of this window for aggressive subtypes through higher sensitivity, while providing a rationale for extending intervals for lesions with protracted biological trajectories.

The “low-age, low-frequency” strategy

The biological insights into tumor evolutionary trajectories and the curative time window necessitate a shift toward dynamic, risk-adapted screening management. We propose a hypothesis-generating “low-age, low-frequency” strategy as a pragmatic framework to reconcile the current limitations of age- and smoking-based protocols. The rationale for “low-age” initiation is driven by the increasing incidence of lung cancer among younger populations (55) and the rapid progression of specific molecular subtypes (19,56,57). These aggressive malignancies often possess narrow curative windows, suggesting that earlier baseline screening may be required to capture these cases before they advance to an incurable stage.

Complementing this, the “low-frequency” component is supported by evidence indicating that the majority of screen-detectable cancers in low-risk cohorts are identified during the initial scan. A systematic review found that 95.4% of lung cancers in never-smokers were detected at the baseline LDCT, with a minimal yield during short-term follow-up (41). This high baseline detection rate suggests that for individuals with a negative initial scan, the risk of developing a rapid, new-onset malignancy is statistically low. Consequently, extending screening intervals to 5 to 10 years for baseline-negative, low-risk individuals could enhance the cost-effectiveness of screening programs without compromising patient safety (58). This strategy is not intended to replace established annual protocols for high-risk smokers but rather to expand the screening net to include currently overlooked demographics while mitigating the cumulative harms of overscreening.

The feasibility of broadening initial screening criteria is further supported by real-world evidence from the Mississippi Delta cohort (44). Their Lung Nodule Program, which enrolled individuals with incidental nodules regardless of traditional eligibility, successfully detected a significant proportion of early-stage lung cancers, with approximately half of the diagnosed cases failing to meet USPSTF criteria. This program demonstrated that a more inclusive initial screening net could effectively identify malignancies and benefit individuals typically considered too young or too old for conventional screening (59). While this provisional framework represents a significant step toward precision screening, its successful implementation will rely on identifying “rapid progressors” through emerging technologies to ensure that personalized intervals remain safe and effective.

Implementation with precision tools

The successful implementation of a “low-age, low-frequency” strategy requires advanced technical tools to accurately stratify risk and monitor disease progression. Artificial intelligence (AI) and radiomics represent the primary technical pathways for translating biological insights into personalized clinical management. By leveraging deep learning algorithms, AI-powered radiomics may analyze subtle texture changes and volumetric growth patterns in GGNs that are often imperceptible to the human eye. These tools are increasingly capable of distinguishing indolent from aggressive phenotypes, thereby identifying “rapid progressors” who require more intensive surveillance or immediate intervention within their specific curative time window.

Furthermore, the integration of AI with multidimensional risk-prediction models (60-62) may offer a more comprehensive approach to identifying high-risk individuals who fall outside traditional smoking-centric criteria. By synthesizing data from genetic susceptibility, family history, and environmental exposures, these models can refine the selection of “low-risk” individuals for extended screening intervals, ensuring that the “low-frequency” component of the paradigm remains safe. Complementary to imaging, the emergence of molecular biomarkers, such as circulating tumor DNA (63), provides a liquid biopsy filter that can further validate the biological potential of detected lesions. Together, these AI-driven and molecular tools may serve as essential pre-screening filters and management guides, enabling a transition from population-based protocols to a truly individualized precision screening paradigm.

Future perspectives and evidence gaps

The need for high-quality evidence

While the proposed conceptual paradigm offers a biological framework for current screening misalignments, several critical evidence gaps must be bridged to support its clinical adoption. There is an urgent need for high-quality, large-scale randomized controlled trials to confirm the mortality benefits of LDCT screening in never-smoking populations, particularly within the unique epidemiological context of Asia. Beyond mortality data, future research must further elucidate the distinct etiology of lung cancer in non-smokers to refine risk stratification models. Additionally, refined health-economic analyses are required to evaluate the cost-effectiveness of various screening intervals across diverse healthcare systems, ensuring that personalized strategies remain sustainable. Finally, the prospective validation of multidimensional risk-prediction models is essential to guarantee their accuracy and equity in real-world clinical settings.

Ongoing trials

To address these evidence gaps and provide empirical support for the proposed paradigm,the first, ECTOP-1038 (NCT07626177), is a multi-center, prospective observational study designed to investigate the correlation between real-world screening behaviors and the evolving spectrum of lung cancer. By characterizing how varying screening practices influence clinical presentations at diagnosis, this study aims to identify the specific systemic gaps that lead to missed opportunities for curative interception. Building on this, we are currently in the preparation phase of a second prospective trial focused on evaluating diverse screening modalities. This large-scale collaborative effort will involve a prolonged enrollment period and longitudinal follow-up to provide the mature data necessary to validate the “low-age, low-frequency” hypothesis. Through these initiatives, we seek to generate the high-quality evidence required to transition lung cancer screening from a static, population-based model toward a risk-adapted framework.

Conclusions

The persistent challenges of over- and underscreening result from the limitation of rigid, smoking-centric guidelines to account for the diverse evolutionary trajectories of lung cancer in an evolving epidemiological landscape. We explore a hypothesis-generating “low-age, low-frequency” framework as a potential strategy to align screening intensity with the “curative time window”. By expanding baseline access for younger populations and extending intervals for low-risk individuals, this conceptual approach aims to optimize screening efficiency and outcomes. However, prospective trials are essential to validate its clinical impact and long-term benefits.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0629/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0629/prf

Funding: The study was supported by National Natural Science Foundation of China (No. 82430099), Medical Research Special Project for Shanghai Science and Technology Innovation Action Plan (No. 24Y12800400), and Clinical Cohort Shanghai (No. SHDC2025CCS017).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0629/coif). Haiquan Chen reports that this study was supported by National Natural Science Foundation of China (No. 82430099), Medical Research Special Project for Shanghai Science and Technology Innovation Action Plan (No. 24Y12800400), and Clinical Cohort Shanghai (No. SHDC2025CCS017). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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