Circulating tumor DNA as prognostic markers of non-small cell lung cancer (NSCLC): a systematic review and meta-analysis

Introduction

Lung cancer is the leading cause of cancer-related deaths worldwide (1). Non-small cell lung cancer (NSCLC) is the most common histological subtype, accounting for 85% of all lung cancers (2,3). Patients with early-stage and locally advanced NSCLC typically receive curative first-line treatments such as tumor resection, radiotherapy, and/or chemoradiotherapy, along with adjuvant systemic therapies such as chemotherapy or immune checkpoint inhibitors (4,5). Despite these approaches, many individuals with stage I–III NSCLC still develop disease progression during treatment, leading to poor prognosis and low 5-year survival rates (6,7).

Currently, imaging examinations like computed tomography (CT) and positron emission tomography (PET) are used to assess treatment response and monitor postoperative recurrence in NSCLC patients. However, imaging results may not always correlate with pathological responses obtained from surgical resection or biopsy specimens (8-10), which are invasive procedures and not always feasible (11). Therefore, there is an urgent need for accurate, robust, and non-invasive biomarkers to predict prognostic outcomes in NSCLC, guide timely treatment, and reduce the risk of recurrence.

Minimal residual disease (MRD) refers to the minimal residual lesions remaining after cancer treatment; MRD positivity indicates that residual lesions are still detectable (a small number of tumor cells unresponsive or resistant to treatment remain in the body) (12). The number of residual tumor cells may be small and might not cause any signs or symptoms, but they have the potential to lead to cancer recurrence. Circulating tumor DNA (ctDNA) consists of DNA fragments derived from the tumor genome present in peripheral blood, carrying specific tumor characteristics such as single nucleotide variants (SNVs), insertions/deletions, rearrangements, and copy number variations (CNVs) (13). The main sources of ctDNA are apoptotic or necrotic tumor cells, circulating tumor cells, and exosomes secreted by tumor cells (13). In recent years, ctDNA sequencing has emerged as a non-invasive method for MRD detection, applicable for early diagnosis, prognostic stratification, disease monitoring, and treatment response evaluation in various types of cancer (14,15). Compared with traditional biomarkers, ctDNA detection offers advantages such as higher sensitivity, ease of repetition, and cost-effectiveness (16).

Multiple studies have shown that ctDNA can identify NSCLC patients at higher risk of recurrence after surgery and/or chemoradiotherapy and other treatment options (17,18). Some reviews and meta-analyses have indicated that baseline ctDNA negativity is associated with improved progression-free survival (PFS) and overall survival (OS) in NSCLC patients undergoing targeted therapies. Patients with negative ctDNA at baseline tend to have better clinical outcomes compared to those with positive ctDNA levels (19,20). Other research also found that postoperative ctDNA detection could predict relapse but had limited effects in guiding adjuvant therapy for resectable stage I NSCLC patients (21). However, at present, these reviews and meta-analyses on the early detection and prognostic utility of ctDNA in NSCLC have largely concentrated on specific disease stages or single outcome endpoints. To assess the broader role of ctDNA assays for predicting the risk of NSCLC recurrence and treatment efficacy, this study conducted a comprehensive systematic search of the global research on ctDNA detection in NSCLC. Through meta-analysis, these studies were effectively synthesized to explore the relationship between ctDNA detection at different time points and various prognostic outcomes. We present this article in accordance with the PRISMA reporting checklist (22) (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-900/rc).

Methods

Protocol and registration

We prospectively registered the study protocol on PROSPERO (CRD 42023474517).

Search strategy

We searched Web of Science, PubMed, the Cochrane Library, Embase, Scopus, the ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform (WHO-ICTRP) database for studies dated from January 2016 to May 2022, with updates (manual replenishment) monitored until June 2024. Detailed search strategy is available in Appendix 1 (literature search strategies).

Study selection

Two reviewers independently carried out study selection in two stages. After automatic and manual removal of duplicate records, titles and abstracts were screened to identify potentially relevant studies. Full texts of these records were then assessed against predefined inclusion and exclusion criteria.

The inclusion criteria were as follows: (I) original observational studies (e.g., cohort or case-control) or randomized controlled trials; (II) study populations consisting of patients diagnosed with NSCLC; (III) reported collection and analysis of ctDNA, with results presented as a binary variable (categorical variables, expressed as positive or negative), without restrictions on assay technique or sampling timepoint; (IV) availability of at least one prognostic endpoint, such as relapse/disease/event-free survival, OS, recurrence, lead time, PFS, major pathologic response, or pathologic complete response; (V) full-length articles and relevant conference abstracts published within the last five years; and (VI) publications written in English.

Studies were excluded if they met any of the following criteria: (I) non-original articles or reports without primary data (such as reviews, editorials, comments, or case reports); (II) ctDNA not reported as categorical variables; (III) study populations involving diseases other than NSCLC; (IV) outcome was not related to prognosis; and (V) conference abstracts have been published in full-length.

Outcomes

The primary outcomes were relapse/disease/event/metastatic-free survival (referred to as RFS) and OS. RFS was defined as the time from inclusion or treatment to radiographic relapse, disease progression, or death (23). OS was defined as the time from inclusion or treatment to death from any cause (23,24). Secondary outcomes included disease recurrence and lead time (defined as the time from detection of preclinical cancer by screening to detection of clinical (symptomatic) cancer in the absence of screening) (25).

We further classified ctDNA sampling into four measurement timepoints: baseline (before any treatment, all NSCLCs), perioperative period (during surgery and after surgery, only for resectable NSCLCs), after full-course therapy recommended by the guidelines (after completing corresponding treatments, including surgery and chemotherapy for resectable NSCLCs, and chemoradiotherapy/radiotherapy/chemotherapy/systemic therapy for unresectable NSCLCs), and during long-term post-treatment surveillance (all NSCLCs).

Data extraction

In this study, the following variables were extracted: (I) general information, including article title, first author, year of publication, and country of study; (II) population and disease characteristics: sample size, resectable or unresectable, and cancer stage; (III) ctDNA measurements information: definition of ctDNA-positive, timepoints of collecting ctDNA; (IV) outcome-related data: the effect estimates and corresponding 95% confidence intervals (CIs) for RFS and OS, the number of recurrence events in ctDNA-positive and ctDNA-negative groups, and the interval between ctDNA detection and radiologic confirmation used to derive lead time.

Assessment of risk of bias

For observational studies (cohort study and case-control study), the risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS) (26).

Data synthesis and assessment of publication bias

This meta-analysis was performed separately for each predefined ctDNA sampling measurement timepoints. Given the anticipated clinical and methodological differences across studies, we applied random-effects models. For time-to-event outcomes (RFS and OS), we pooled hazard ratios (HRs) with 95% CIs, whereas for recurrence we synthesized risk ratios (RRs). The overall statistical significance of pooled results was assessed using Z-test. Between-study heterogeneity was quantified with I² statistics (I²>50% interpreted as significant heterogeneity). Potential publication bias was explored using funnel plots and tested with Egger’s test. In addition, the Duval trim-and-fill method was used to further evaluate publication bias.

Statistical analyses

All analyses were conducted with R software version 4.0.0. Two-sided P values <0.05 were considered statistically significant. We performed sensitivity analyses using leave-one-out methods or by excluding conference abstracts, in order to examine the robustness of the pooled results when data available.

Results

Literature search and characteristics of included studies

A total of 52 studies were included, among which 50 studies were original research and 1 study was conference abstract and 1 study was letter. The screening process is shown in Figure 1. Table 1 presented the baseline characteristics of the included studies. The studies conducted between 2016 and 2024 across various countries, primarily in China and the US, were analyzed. The pathological stages of participants ranged from I to IV, with most studies focusing on stages II and III. Total sample sizes varied widely, ranging from 12 to 330 participants. CtDNA was collected at multiple time points, including baseline, post-surgery, after treatment, and during surveillance. As for the definition of ctDNA-positive, 5 studies distinguished ctDNA-positive and ctDNA-negative based on quantitative indicators such as tumor fraction; 20 studies considered any single detected mutation as positive; 5 studies required the presence of two or more variants or mutations; 4 studies determined positivity based on the number of positive droplets or other approaches; and 18 studies did not clearly report their definition (Table S1).

Figure 1 Flow diagram of study inclusion and exclusion. ctDNA, circulating tumor DNA; WHO-ICTRP, World Health Organization International Clinical Trials Registry Platform.

Quality assessment

The risk of bias assessment for the included studies is summarized in Table 2. Total risk scores ranged from 4 to 9, with 90.4% of studies scoring between 6 and 9 points, indicating a low to moderate risk of bias.

ctDNA and RFS

CtDNA measured at baseline was correlated with an unfavorable RFS in the overall NSCLC population, with a pooled HR of 2.23 (95% CI: 1.82–2.75; I²=49%, Figure 2A) in all NSCLCs. For resectable NSCLCs, 12 studies reported the association between ctDNA measured just after surgery (HR =5.64, 95% CI: 3.88–8.19; I²=36%, Figure 2B). Further, after the full-course treatment, HR was 5.82 (95% CI: 3.12–10.87; I²=53%, Figure 2C) and 2.72 (95% CI: 1.99–3.72; I²=39%, Figure 2C) for resectable and unresectable NSCLCs, respectively. The significant risk of poorer RFS persisted throughout the long-term post-treatment surveillance period (Figure 2D).

Figure 2 Forest plot of the association between different timepoint ctDNA detection with RFS. (A) Baseline (all NSCLCs); (B) perioperative period (only for resectable NSCLCs); (C) after full-course treatment (after completing corresponding treatments, all NSCLCs); (D) after long-term post-treatment surveillance (all NSCLCs). CI, confidence interval; ctDNA, circulating tumor DNA; HR, hazard ratio; NSCLC, non-small cell lung cancer; RFS, relapse-free survival.

ctDNA and OS

Association between ctDNA detection and poor OS persisted throughout the whole treatment cycle (Figure 3). Baseline ctDNA positivity significantly predicted poorer OS, particularly evident in both resectable (HR =4.15, 95% CI: 2.45–7.02) and unresectable (HR =1.74, 95% CI: 1.49–2.03) NSCLC patients (Figure 3A). In resectable NSCLC patients, HR was 4.17 (95% CI: 2.22–7.84; I²=12%, Figure 3B) after surgery. HR was 3.38 (95% CI: 1.97–5.80; I²=57%, Figure 3C) for those unresectable NSCLCs after the full-cycle treatment. Four studies reported the statistically significant association between ctDNA measured and OS during the long-term post-treatment surveillance period (Figure 3D).

Figure 3 Forest plot of the association between different timepoint ctDNA detection with OS. (A) Baseline (all NSCLCs); (B) perioperative period (only for resectable NSCLCs); (C) after full-course treatment (after completing corresponding treatments, all NSCLCs); (D) after long-term post-treatment surveillance (all NSCLCs). CI, confidence interval; ctDNA, circulating tumor DNA; HR, hazard ratio; NSCLC, non-small cell lung cancer; OS, overall survival.

ctDNA and recurrence

Studies reported that patients with positive ctDNA faced a higher risk of recurrence (Figure S1). RRs were found to be increased from baseline (RR =1.67, 95% CI: 1.27–2.20; I²=64%), post-full-course treatment (RR =3.13, 95% CI: 2.09–4.67; I²=52%) to long-term post-treatment surveillance (RR =5.42, 95% CI: 3.20–9.18; I²=81%) in all NSCLCs.

Lead time

Eighteen studies reported lead time, typically defined as the interval between ctDNA detection and radiographic recurrence or clinical recurrence, which in almost all studies reflected post-treatment surveillance (postsurgical or post-systemic/locoregional therapy). Among studies with ≥10 patients contributing to the estimate, the median of study-specific median lead time was 2.93 months, with a range of 1.70–12.60 months (Table S2).

Sensitivity analysis and publication bias

Across all ctDNA measurement timepoints, the pooled estimates for RFS and OS were robust, with sensitivity analyses showing no significant changes in the results (Figures S2,S3). Visual inspection of the funnel plots and the Eggers’ test results suggested that there was publication bias for OS when ctDNA was detected at baseline or after full-course treatment (baseline: P_{Egger test}=0.001; after full-course treatment: P_{Egger test}=0.012), and there was also publication bias for RFS when ctDNA were detected after full-course treatment (P_{Egger test}=0.006) (Figures S4,S5). After the missing studies were imputed, the adjusted pooled HRs for OS when ctDNA were detected at baseline and after full-course treatment were 1.73 (95% CI: 1.50–2.00) and 2.89 (95% CI: 1.84–4.53), respectively. The adjusted pooled HR for OS when ctDNA were detected after full-course treatment was 2.57 (95% CI: 1.66–3.97). These results were similar in magnitude to the unadjusted estimates, indicating that the significant association still existed despite potential publication bias.

Discussion

In this meta-analysis, we systematically evaluated the prognostic value of ctDNA in patients with NSCLC across different treatment timepoints. Our findings demonstrated that ctDNA positivity was consistently associated with worse RFS and OS, and was also predictive of a higher recurrence risk. Furthermore, ctDNA detection may offer a distinct ‘lead time’ before radiographic confirmation of recurrence, underscoring its potential role as an early warning biomarker in NSCLC management.

ctDNA as a prognostic biomarker throughout the treatment cycle

NSCLC remains the most common histological subtype of lung cancer and the leading cause of cancer-related deaths worldwide (78). Despite curative-intent treatments (e.g., surgery, radiotherapy, or chemoradiotherapy) for early-stage and locally advanced disease, a significant proportion of NSCLC patients eventually experience recurrence or progression, resulting in poor prognosis and low 5-year survival rates (79,80).

Our analysis showed that ctDNA positivity at baseline was a significant predictor of inferior RFS, corroborating earlier studies indicating that tumor-specific genetic alterations in the bloodstream prior to definitive therapy may reflect a larger tumor burden or more aggressive disease biology, eventually contributing to early recurrence and poor survival (46,47,81). This association can be attributed to the fact that ctDNA reflects not only tumor load but also the molecular characteristics of the cancer, such as mutations in driver genes (e.g., EGFR, KRAS), which are often linked to more aggressive disease phenotypes (82).

The prognostic impact of ctDNA extends throughout the perioperative period and beyond the completion of the full treatment course. In resectable NSCLC, ctDNA positivity immediately after surgery or post-chemotherapy exhibited a notably stronger association with worse RFS and OS compared to those who were ctDNA-negative. This finding underscores the concept of MRD in which viable tumor cells remain in circulation post-treatment and can lead to subsequent relapse if not eradicated (46,47,83,84).

Similarly, among patients with unresectable NSCLC, ctDNA detection following completion of full-course treatment was associated with significantly worse RFS and OS. This suggests that ctDNA could serve as a real-time indicator of persistent or resistant disease that might not be fully evaluated by conventional imaging studies, such as CT or PET, which sometimes fail to distinguish between post-treatment inflammatory changes and residual tumors (85,86). Additionally, earlier reviews have found that patients achieving ctDNA negativity after therapy experience longer survival and lower recurrence rates, emphasizing the importance of ctDNA clearance as an indicator of effective disease control (50,87).

Risk of recurrence and lead time

Beyond survival outcomes, our results also highlighted a consistent association between ctDNA positivity and elevated recurrence risk at multiple treatment landmarks. Notably, the pooled RRs for recurrence increased from baseline to post-full-course treatment and finally to long-term post-treatment surveillance. These data suggest that the presence of ctDNA at later timepoints may reflect persistent MRD or emergent resistant tumor clones, both of which portend a markedly higher likelihood of eventual disease relapse.

Crucially, ctDNA often emerges in the bloodstream before radiographic recurrence can be confirmed, providing a ‘lead time’ for potential intervention (18,81,84). In clinical practice, this lead time could be capitalized upon for closer monitoring and earlier therapeutic adaptation.

Different role of ctDNA between resectable and unresectable NSCLC patients

The role of ctDNA appears to differ between resectable and unresectable NSCLC patients. In resectable patients, ctDNA is predominantly used as a prognostic marker for MRD and early recurrence detection. The association between postoperative ctDNA positivity and worse outcomes underscores the importance of ctDNA as a sensitive tool for detecting residual tumor burden following surgery, even in patients who initially appear to have undergone a successful resection (21,88,89). Conversely, in unresectable NSCLC, ctDNA detected after treatment often indicates persistent or resistant tumor clones, reflecting advanced disease and acquired resistance to therapies, particularly those targeting driver mutations like EGFR or ALK (90,91).

CtDNA detection methods and binary classification

Variations in ctDNA detection technology and methodologies among studies are a major source of heterogeneity in reported outcomes. Different assays such as digital polymerase chain reaction (PCR), targeted next-generation sequencing (NGS) panels, or tumor-informed ultra-deep sequencing have different limits of detection and specificity. For example, a cancer personalized sequencing approach (CAPP-seq) can detect mutant allele frequencies down to ~0.02%, identifying ctDNA in about half of stage I NSCLC patients and ~100% of stage II–IV patients at that threshold (with ~96% specificity). Newer personalized MRD assays can achieve even higher sensitivity, detecting ctDNA at allele frequencies as low as 0.003–0.008% with near-perfect specificity. Such technical differences mean that some studies detect very low levels of ctDNA that others might miss, affecting who is classified as ‘ctDNA-positive’ (92,93). This also makes meaningful cross-comparison across the included studies more challenging. Therefore, caution is warranted when interpreting the results of our study.

Given this lack of standardization in ctDNA quantification across studies, we adopted a positive/negative (binary) classification for ctDNA status in our analysis, rather than using absolute ctDNA levels. This approach aligns with the reporting in most primary studies (ctDNA either detected or not detected) and thus allowed us to pool HRs consistently. While we acknowledge that quantitative ctDNA levels or dynamics (e.g., rising or clearance trends) can carry additional prognostic information, a dichotomous categorization is a practical and clinically interpretable choice for a meta-analysis spanning multiple platforms (94). Each included study’s defined threshold for ctDNA positivity was utilized, which provides a uniform way to identify high-risk patients (ctDNA-positive) versus low-risk (ctDNA-negative) across diverse cohorts. This strategy enabled us to integrate data despite assay variability, though we note that future standardization of ctDNA measurement would further enhance comparative analyses.

Strengths and limitations

This review possesses several notable strengths. Firstly, it comprehensively evaluated the associations between ctDNA and prognostic outcomes at multiple treatment timepoints rather than focusing on a single timepoint. By capturing the dynamic nature of ctDNA before treatment, immediately after curative interventions, and during long-term follow-up, the analysis provides a more robust assessment of ctDNA prognostic utility throughout the entire disease course. Secondly, sensitivity analyses were performed to validate the stability of the results, which is relatively uncommon in other similar studies.

Several limitations must be considered. First, there was notable heterogeneity across the included studies: no clinical gold standard or practical consensus for ctDNA detection currently exists, and the studies varied in design, assay methodologies, and treatment regimens. Outcome follow-up intervals and definitions also differed, with some relying on surrogate or alternative endpoints that may exclude non-disease-related deaths, potentially introducing confounders. Second, we only included studies reporting qualitative (positive vs. negative) ctDNA results. Third, the impact of quantitative changes in ctDNA levels on outcomes remains unclear. Fourth, publication bias was observed, particularly in analyses examining the association between ctDNA and OS/RFS. Nevertheless, after using the trim-and-fill method, the findings remained robust. Finally, many of the included studies had relatively small sample sizes and were retrospective, limiting the robustness of the conclusions.

Conclusions

This meta-analysis reinforces the prognostic significance of ctDNA across all major treatment timepoints in NSCLC. Positive ctDNA status consistently predicts worse RFS and OS, while also conferring a higher risk of recurrence. Moreover, ctDNA detection can precede radiographic evidence of disease progression, offering clinicians valuable time to initiate earlier therapeutic interventions. These findings underscore the potential of ctDNA-based liquid biopsy to refine risk stratification, guide individualized treatment decisions, and ultimately improve clinical outcomes in NSCLC. Prospective trials with standardized methodologies are warranted to further substantiate these clinical benefits.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-900/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-900/prf

Funding: This work was supported by the National Natural Science Foundation of China (grant Nos. 72474008 and 72074011 to F.S.), the Capital’s Funds for Health Improvement and Research (grant No. 2024-1G-4023 to F.S.), and the Special Project for Director, China Center for Evidence Based Traditional Chinese Medicine (grant No. 2020YJSZX-2 to F.S.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-900/coif). F.S. reports receiving research funding from the National Natural Science Foundation of China (grant Nos. 72474008 and 72074011), the Capital’s Funds for Health Improvement and Research (grant No. 2024-1G-4023), and the Special Project for Director, China Center for Evidence Based Traditional Chinese Medicine (grant No. 2020YJSZX-2). 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/.

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;74:229-63. [Crossref] [PubMed]
2. Wang H, Niu X, Jin Z, et al. Immunotherapy resistance in non-small cell lung cancer: from mechanisms to therapeutic opportunities. J Exp Clin Cancer Res 2025;44:250. [Crossref] [PubMed]
3. Ziółkowska-Suchanek I, Rozwadowska N. Advancements in Gene Therapy for Non-Small Cell Lung Cancer: Current Approaches and Future Prospects. Genes (Basel) 2025;16:569. [Crossref] [PubMed]
4. Tanaka F, Yoneda K. Adjuvant therapy following surgery in non-small cell lung cancer (NSCLC). Surg Today 2016;46:25-37. [Crossref] [PubMed]
5. Owen D, Chaft JE. Immunotherapy in surgically resectable non-small cell lung cancer. J Thorac Dis 2018;10:S404-11. [Crossref] [PubMed]
6. Chinese Thoracic Society. Chinese expert consensus on refractory lung cancer. Zhonghua Jie He He Hu Xi Za Zhi 2024;47:301-12. [Crossref] [PubMed]
7. Araghi M, Mannani R, Heidarnejad Maleki A, et al. Recent advances in non-small cell lung cancer targeted therapy; an update review. Cancer Cell Int 2023;23:162. [Crossref] [PubMed]
8. William WN Jr, Pataer A, Kalhor N, et al. Computed tomography RECIST assessment of histopathologic response and prediction of survival in patients with resectable non-small-cell lung cancer after neoadjuvant chemotherapy. J Thorac Oncol 2013;8:222-8. [Crossref] [PubMed]
9. Xu J, Yao J, Geng Y, et al. Neoadjuvant immunotherapy plus chemotherapy for resectable non-small cell lung cancer with driver mutations: a retrospective analysis. Front Immunol 2025;16:1637615. [Crossref] [PubMed]
10. Forde PM, Chaft JE, Smith KN, et al. Neoadjuvant PD-1 Blockade in Resectable Lung Cancer. N Engl J Med 2018;378:1976-86. [Crossref] [PubMed]
11. Chaubal R, Gardi N, Joshi S, et al. Surgical Tumor Resection Deregulates Hallmarks of Cancer in Resected Tissue and the Surrounding Microenvironment. Mol Cancer Res 2024;22:572-84. [Crossref] [PubMed]
12. Moding EJ, Nabet BY, Alizadeh AA, et al. Detecting Liquid Remnants of Solid Tumors: Circulating Tumor DNA Minimal Residual Disease. Cancer Discov 2021;11:2968-86. [Crossref] [PubMed]
13. The Oncology Expert Group on the Interpretation of Next Generation Sequencing Clinical Reports. Consensus on Interpretation of Clinical Reports for Next-Generation Sequencing of Tumors. Journal of Evidence-Based Medicine 2022;22:65-79.
14. Magbanua MJM, Swigart LB, Wu HT, et al. Circulating tumor DNA in neoadjuvant-treated breast cancer reflects response and survival. Ann Oncol 2021;32:229-39. [Crossref] [PubMed]
15. Campos-Carrillo A, Weitzel JN, Sahoo P, et al. Circulating tumor DNA as an early cancer detection tool. Pharmacol Ther 2020;207:107458. [Crossref] [PubMed]
16. Pessoa LS, Heringer M, Ferrer VP. ctDNA as a cancer biomarker: A broad overview. Crit Rev Oncol Hematol 2020;155:103109. [Crossref] [PubMed]
17. Waldeck S, Mitschke J, Wiesemann S, et al. Early assessment of circulating tumor DNA after curative-intent resection predicts tumor recurrence in early-stage and locally advanced non-small-cell lung cancer. Mol Oncol 2022;16:527-37. [Crossref] [PubMed]
18. Abbosh C, Birkbak NJ, Wilson GA, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 2017;545:446-51. [Crossref] [PubMed]
19. Zaman FY, Subramaniam A, Afroz A, et al. Circulating Tumour DNA (ctDNA) as a Predictor of Clinical Outcome in Non-Small Cell Lung Cancer Undergoing Targeted Therapies: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023;15:2425. [Crossref] [PubMed]
20. Wang H, Zhou F, Qiao M, et al. The Role of Circulating Tumor DNA in Advanced Non-Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis. Front Oncol 2021;11:671874. [Crossref] [PubMed]
21. Wang B, Zou B, Xu S, et al. Postoperative ctDNA detection predicts relapse but has limited effects in guiding adjuvant therapy in resectable stage I NSCLC. Front Oncol 2023;13:1083417. [Crossref] [PubMed]
22. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372: [Crossref] [PubMed]
23. Liu Y, Yang T, Wei YW. What is the difference between overall survival, recurrence-free survival and time-to-recurrence? Br J Surg 2020;107:e634. [Crossref] [PubMed]
24. Broström E, Isaksson J, Xanthoulis P, et al. Predictors of survival and recurrence patterns following definitive chemoradiotherapy in stage III non-small cell lung cancer-a retrospective cohort study. Transl Lung Cancer Res 2025;14:1972-85. [Crossref] [PubMed]
25. Baker SG, Prorok PC, Kramer BS. Lead time and overdiagnosis. J Natl Cancer Inst 2014;106:dju346. [Crossref] [PubMed]
26. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 2010;25:603-5. [Crossref] [PubMed]
27. Wang Y, Wang W, Zhang T, et al. Dynamic bTMB combined with residual ctDNA improves survival prediction in locally advanced NSCLC patients with chemoradiotherapy and consolidation immunotherapy. J Natl Cancer Cent 2024;4:177-87. [Crossref] [PubMed]
28. Tan AC, Lai GGY, Saw SPL, et al. Detection of circulating tumor DNA with ultradeep sequencing of plasma cell-free DNA for monitoring minimal residual disease and early detection of recurrence in early-stage lung cancer. Cancer 2024;130:1758-65. [Crossref] [PubMed]
29. Bossé Y, Dasgupta A, Abadier M, et al. Prognostic implication of methylation-based circulating tumor DNA detection prior to surgery in stage I non-small cell lung cancer. Cancer Lett 2024;594:216984. [Crossref] [PubMed]
30. Oh Y, Yoon SM, Lee J, et al. Personalized, tumor-informed, circulating tumor DNA assay for detecting minimal residual disease in non-small cell lung cancer patients receiving curative treatments. Thorac Cancer 2024;15:1095-102. [Crossref] [PubMed]
31. Nielsen LR, Stensgaard S, Meldgaard P, et al. ctDNA-based minimal residual disease detection in lung cancer patients treated with curative intended chemoradiotherapy using a clinically transferable approach. Cancer Treat Res Commun 2024;39:100802. [Crossref] [PubMed]
32. Tian X, Liu X, Wang K, et al. Postoperative ctDNA in indicating the recurrence risk and monitoring the effect of adjuvant therapy in surgical non-small cell lung cancer. Thorac Cancer 2024;15:797-807. [Crossref] [PubMed]
33. Liu SY, Dong S, Yang XN, et al. Neoadjuvant nivolumab with or without platinum-doublet chemotherapy based on PD-L1 expression in resectable NSCLC (CTONG1804): a multicenter open-label phase II study. Signal Transduct Target Ther 2023;8:442. [Crossref] [PubMed]
34. Tran HT, Heeke S, Sujit S, et al. Circulating tumor DNA and radiological tumor volume identify patients at risk for relapse with resected, early-stage non-small-cell lung cancer. Ann Oncol 2024;35:183-9. [Crossref] [PubMed]
35. Eslami-S Z, Cortés-Hernández LE, Sinoquet L, et al. Circulating tumour cells and PD-L1-positive small extracellular vesicles: the liquid biopsy combination for prognostic information in patients with metastatic non-small cell lung cancer. Br J Cancer 2024;130:63-72. [Crossref] [PubMed]
36. Pan Y, Zhang JT, Gao X, et al. Dynamic circulating tumor DNA during chemoradiotherapy predicts clinical outcomes for locally advanced non-small cell lung cancer patients. Cancer Cell 2023;41:1763-1773.e4. [Crossref] [PubMed]
37. Pellini B, Madison RW, Childress MA, et al. Circulating Tumor DNA Monitoring on Chemo-immunotherapy for Risk Stratification in Advanced Non-Small Cell Lung Cancer. Clin Cancer Res 2023;29:4596-605. [Crossref] [PubMed]
38. Zhong H, Zhang X, Tian P, et al. Tislelizumab plus chemotherapy for patients with EGFR-mutated non-squamous non-small cell lung cancer who progressed on EGFR tyrosine kinase inhibitor therapy. J Immunother Cancer 2023;11:e006887. [Crossref] [PubMed]
39. Jung HA, Ku BM, Kim YJ, et al. Longitudinal Monitoring of Circulating Tumor DNA From Plasma in Patients With Curative Resected Stages I to IIIA EGFR-Mutant Non-Small Cell Lung Cancer. J Thorac Oncol 2023;18:1199-208. [Crossref] [PubMed]
40. Frank MS, Andersen CSA, Ahlborn LB, et al. Circulating Tumor DNA Monitoring Reveals Molecular Progression before Radiologic Progression in a Real-life Cohort of Patients with Advanced Non-small Cell Lung Cancer. Cancer Res Commun 2022;2:1174-87. [Crossref] [PubMed]
41. Han X, Tang X, Zhu H, et al. Short-term dynamics of circulating tumor DNA predicting efficacy of sintilimab plus docetaxel in second-line treatment of advanced NSCLC: biomarker analysis from a single-arm, phase 2 trial. J Immunother Cancer 2022;10:e004952. [Crossref] [PubMed]
42. Reichert ZR, Morgan TM, Li G, et al. Prognostic value of plasma circulating tumor DNA fraction across four common cancer types: a real-world outcomes study. Ann Oncol 2023;34:111-20. [Crossref] [PubMed]
43. Zheng J, Wang Y, Hu C, et al. Predictive value of early kinetics of ctDNA combined with cfDNA and serum CEA for EGFR-TKI treatment in advanced non-small cell lung cancer. Thorac Cancer 2022;13:3162-73. [Crossref] [PubMed]
44. Anagnostou V, Forde PM, White JR, et al. Dynamics of Tumor and Immune Responses during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer Res 2019;79:1214-25. [Crossref] [PubMed]
45. Angeles AK, Christopoulos P, Yuan Z, et al. Early identification of disease progression in ALK-rearranged lung cancer using circulating tumor DNA analysis. NPJ Precis Oncol 2021;5:100. [Crossref] [PubMed]
46. Chaudhuri AA, Chabon JJ, Lovejoy AF, et al. Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling. Cancer Discov 2017;7:1394-403. [Crossref] [PubMed]
47. Chen K, Zhao H, Shi Y, et al. Perioperative Dynamic Changes in Circulating Tumor DNA in Patients with Lung Cancer (DYNAMIC). Clin Cancer Res 2019;25:7058-67. [Crossref] [PubMed]
48. Chen Y, Li X, Liu G, et al. ctDNA Concentration, MIKI67 Mutations and Hyper-Progressive Disease Related Gene Mutations Are Prognostic Markers for Camrelizumab and Apatinib Combined Multiline Treatment in Advanced NSCLC. Front Oncol 2020;10:1706. [Crossref] [PubMed]
49. Ding PN, Becker TM, Bray VJ, et al. The predictive and prognostic significance of liquid biopsy in advanced epidermal growth factor receptor-mutated non-small cell lung cancer: A prospective study. Lung Cancer 2019;134:187-93. [Crossref] [PubMed]
50. Gale D, Heider K, Ruiz-Valdepenas A, et al. Residual ctDNA after treatment predicts early relapse in patients with early-stage non-small cell lung cancer. Ann Oncol 2022;33:500-10. [Crossref] [PubMed]
51. Gassa A, Fassunke J, Schueten S, et al. Detection of circulating tumor DNA by digital droplet PCR in resectable lung cancer as a predictive tool for recurrence. Lung Cancer 2021;151:91-6. [Crossref] [PubMed]
52. Hellmann MD, Nabet BY, Rizvi H, et al. Circulating Tumor DNA Analysis to Assess Risk of Progression after Long-term Response to PD-(L)1 Blockade in NSCLC. Clin Cancer Res 2020;26:2849-58. [Crossref] [PubMed]
53. Isaksson S, George AM, Jönsson M, et al. Pre-operative plasma cell-free circulating tumor DNA and serum protein tumor markers as predictors of lung adenocarcinoma recurrence. Acta Oncol 2019;58:1079-86. [Crossref] [PubMed]
54. Jiang J, Adams HP, Lange M, et al. Plasma-based longitudinal mutation monitoring as a potential predictor of disease progression in subjects with adenocarcinoma in advanced non-small cell lung cancer. BMC Cancer 2020;20:885. [Crossref] [PubMed]
55. Kallergi G, Kontopodis E, Ntzifa A, et al. Effect of Osimertinib on CTCs and ctDNA in EGFR Mutant Non-Small Cell Lung Cancer Patients: The Prognostic Relevance of Liquid Biopsy. Cancers (Basel) 2022;14:1574. [Crossref] [PubMed]
56. Knapp B, Mezquita L, Devarakonda S, et al. Exploring the Feasibility of Utilizing Limited Gene Panel Circulating Tumor DNA Clearance as a Biomarker in Patients With Locally Advanced Non-Small Cell Lung Cancer. Front Oncol 2022;12:856132. [Crossref] [PubMed]
57. Li Y, Xu Z, Wang S, et al. Disease monitoring of epidermal growth factor receptor (EGFR)-mutated non-small-cell lung cancer patients treated with tyrosine kinase inhibitors via EGFR status in circulating tumor DNA. Thorac Cancer 2022;13:2201-9. [Crossref] [PubMed]
58. Kris MG, Grindheim JM, Chaft JE, et al. Dynamic circulating tumour DNA (ctDNA) response to neoadjuvant (NA) atezolizumab (atezo) and surgery (surg) and association with outcomes in patients (pts) with NSCLC. Ann Oncol 2021;32:S1373.
59. Kuang PP, Li N, Liu Z, et al. Circulating Tumor DNA Analyses as a Potential Marker of Recurrence and Effectiveness of Adjuvant Chemotherapy for Resected Non-Small-Cell Lung Cancer. Front Oncol 2020;10:595650. [Crossref] [PubMed]
60. Kwon M, Ku BM, Olsen S, et al. Longitudinal monitoring by next-generation sequencing of plasma cell-free DNA in ALK rearranged NSCLC patients treated with ALK tyrosine kinase inhibitors. Cancer Med 2022;11:2944-56. [Crossref] [PubMed]
61. Li N, Wang BX, Li J, et al. Perioperative circulating tumor DNA as a potential prognostic marker for operable stage I to IIIA non-small cell lung cancer. Cancer 2022;128:708-18. [Crossref] [PubMed]
62. Moding EJ, Liu Y, Nabet BY, et al. Circulating Tumor DNA Dynamics Predict Benefit from Consolidation Immunotherapy in Locally Advanced Non-Small Cell Lung Cancer. Nat Cancer 2020;1:176-83. [Crossref] [PubMed]
63. Ohara S, Suda K, Sakai K, et al. Prognostic implications of preoperative versus postoperative circulating tumor DNA in surgically resected lung cancer patients: a pilot study. Transl Lung Cancer Res 2020;9:1915-23. [Crossref] [PubMed]
64. Behel V, Chougule A, Noronha V, et al. Clinical Utility of Liquid Biopsy (Cell-free DNA) Based EGFR Mutation Detection Post treatment Initiation as a Disease Monitoring Tool in Patients With Advanced EGFR-mutant NSCLC. Clin Lung Cancer 2022;23:410-8. [Crossref] [PubMed]
65. Ortiz-Cuaran S, Mezquita L, Swalduz A, et al. Circulating Tumor DNA Genomics Reveal Potential Mechanisms of Resistance to BRAF-Targeted Therapies in Patients with BRAF-Mutant Metastatic Non-Small Cell Lung Cancer. Clin Cancer Res 2020;26:6242-53. [Crossref] [PubMed]
66. Pécuchet N, Zonta E, Didelot A, et al. Base-Position Error Rate Analysis of Next-Generation Sequencing Applied to Circulating Tumor DNA in Non-Small Cell Lung Cancer: A Prospective Study. PLoS Med 2016;13:e1002199. [Crossref] [PubMed]
67. Provencio M, Serna-Blasco R, Nadal E, et al. Overall Survival and Biomarker Analysis of Neoadjuvant Nivolumab Plus Chemotherapy in Operable Stage IIIA Non-Small-Cell Lung Cancer (NADIM phase II trial). J Clin Oncol 2022;40:2924-33. [Crossref] [PubMed]
68. Peng M, Huang Q, Yin W, et al. Circulating Tumor DNA as a Prognostic Biomarker in Localized Non-small Cell Lung Cancer. Front Oncol 2020;10:561598. [Crossref] [PubMed]
69. Qiu B, Guo W, Zhang F, et al. Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC. Nat Commun 2021;12:6770. [Crossref] [PubMed]
70. Xia L, Mei J, Kang R, et al. Perioperative ctDNA-Based Molecular Residual Disease Detection for Non-Small Cell Lung Cancer: A Prospective Multicenter Cohort Study (LUNGCA-1). Clin Cancer Res 2022;28:3308-17. [Crossref] [PubMed]
71. Yang W, You N, Jia M, et al. Undetectable circulating tumor DNA levels correlate with low risk of recurrence/metastasis in postoperative pathologic stage I lung adenocarcinoma patients. Lung Cancer 2020;146:327-34. [Crossref] [PubMed]
72. Yin JX, Hu WW, Gu H, et al. Combined assay of Circulating Tumor DNA and Protein Biomarkers for early noninvasive detection and prognosis of Non-Small Cell Lung Cancer. J Cancer 2021;12:1258-69. [Crossref] [PubMed]
73. Wang S, Li M, Zhang J, et al. Circulating tumor DNA integrating tissue clonality detects minimal residual disease in resectable non-small-cell lung cancer. J Hematol Oncol 2022;15:137. [Crossref] [PubMed]
74. Yue D, Liu W, Chen C, et al. Circulating tumor DNA predicts neoadjuvant immunotherapy efficacy and recurrence-free survival in surgical non-small cell lung cancer patients. Transl Lung Cancer Res 2022;11:263-76. [Crossref] [PubMed]
75. Zhang JT, Liu SY, Gao W, et al. Longitudinal Undetectable Molecular Residual Disease Defines Potentially Cured Population in Localized Non-Small Cell Lung Cancer. Cancer Discov 2022;12:1690-701. [Crossref] [PubMed]
76. Zugazagoitia J, Ramos I, Trigo JM, et al. Clinical utility of plasma-based digital next-generation sequencing in patients with advance-stage lung adenocarcinomas with insufficient tumor samples for tissue genotyping. Ann Oncol 2019;30:290-6. [Crossref] [PubMed]
77. Ku BM, Kim YJ, Park D, et al. Role of Circulating Tumor DNA Profiling in Patients with Non-Small Cell Lung Cancer Treated with EGFR Inhibitor. Oncology 2022;100:228-37. [Crossref] [PubMed]
78. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
79. Postmus PE, Kerr KM, Oudkerk M, et al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2017;28:iv1-iv21. [Crossref] [PubMed]
80. Park K, Vansteenkiste J, Lee KH, et al. Pan-Asian adapted ESMO Clinical Practice Guidelines for the management of patients with locally-advanced unresectable non-small-cell lung cancer: a KSMO-ESMO initiative endorsed by CSCO, ISMPO, JSMO, MOS, SSO and TOS. Ann Oncol 2020;31:191-201. [Crossref] [PubMed]
81. Schuurbiers MMF, Smith CG, Hartemink KJ, et al. Recurrence prediction using circulating tumor DNA in patients with early-stage non-small cell lung cancer after treatment with curative intent: A retrospective validation study. PLoS Med 2025;22:e1004574. [Crossref] [PubMed]
82. Stadler JC, Belloum Y, Deitert B, et al. Current and Future Clinical Applications of ctDNA in Immuno-Oncology. Cancer Res 2022;82:349-58. [Crossref] [PubMed]
83. Pantel K, Alix-Panabières C. Liquid biopsy and minimal residual disease - latest advances and implications for cure. Nat Rev Clin Oncol 2019;16:409-24. [Crossref] [PubMed]
84. Alix-Panabières C, Pantel K. Liquid Biopsy: From Discovery to Clinical Application. Cancer Discov 2021;11:858-73. [Crossref] [PubMed]
85. Chi A, Fang W, Sun Y, et al. Comparison of Long-term Survival of Patients With Early-Stage Non-Small Cell Lung Cancer After Surgery vs Stereotactic Body Radiotherapy. JAMA Netw Open 2019;2:e1915724. [Crossref] [PubMed]
86. Liu B, Liu L, Hu M, et al. Percutaneous radiofrequency ablation for medically inoperable patients with clinical stage I non-small cell lung cancer. Thorac Cancer 2015;6:327-33. [Crossref] [PubMed]
87. Wang B, Pei J, Wang S, et al. Prognostic potential of circulating tumor DNA detection at different time periods in resectable non-small cell lung cancer: Evidence from a meta-analysis. Crit Rev Oncol Hematol 2022;177:103771. [Crossref] [PubMed]
88. Xia L, Pu Q, Kang R, et al. Dynamic ctDNA to inform the precise management of resected NSCLC: LUNGCA-2 study. J Clin Oncol 2023;41:8528.
89. Khan S, Feng J, Waddell T, et al. ctDNA-Lung-DETECT: ctDNA outcomes for resected early stage non-small cell lung cancers at 12 months. J Clin Oncol 2024;42:8018.
90. Bi N, Yang Y, Wang J, et al. ctDNA for clonal evolution surveillance and therapeutic efficacy prediction in unresectable locally advanced non-small cell lung cancer under radiotherapy. J Clin Oncol 2023;41:e20555.
91. Thompson JC, Yee SS, Troxel AB, et al. Detection of Therapeutically Targetable Driver and Resistance Mutations in Lung Cancer Patients by Next-Generation Sequencing of Cell-Free Circulating Tumor DNA. Clin Cancer Res 2016;22:5772-82. [Crossref] [PubMed]
92. Ulrich B, Pradines A, Mazières J, et al. Detection of Tumor Recurrence via Circulating Tumor DNA Profiling in Patients with Localized Lung Cancer: Clinical Considerations and Challenges. Cancers (Basel) 2021;13:3759. [Crossref] [PubMed]
93. Boukouris AE, Michaelidou K, Joosse SA, et al. A comprehensive overview of minimal residual disease in the management of early-stage and locally advanced non-small cell lung cancer. NPJ Precis Oncol 2025;9:178. [Crossref] [PubMed]
94. Zheng J, Qin C, Wang Q, et al. Circulating tumour DNA-Based molecular residual disease detection in resectable cancers: a systematic review and meta-analysis. EBioMedicine 2024;103:105109. [Crossref] [PubMed]