Equivalence of neoadjuvant and perioperative therapies in patients with stage III non-small cell lung cancer: a systematic review and meta-analysis

Introduction

Lung cancer remains a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 80–85% of all lung cancer cases (1,2). Surgical resection is the cornerstone of curative treatment for patients with early-stage to locally advanced disease (3,4); however, traditional perioperative chemotherapy offers limited efficacy, with the 5-year overall survival (OS) rates being only 40–55% among patients with locally advanced disease (5,6). The advent of immune checkpoint inhibitors (ICIs) has revolutionized this therapeutic landscape, establishing both neoadjuvant and perioperative immunotherapy as standard-of-care strategies for individuals with resectable NSCLC. However, determining the optimal modality between these two approaches for stage III disease remains a critical but unmet need (7,8).

For patients with resectable stage III NSCLC, the efficacy of neoadjuvant immunotherapy has been confirmed in multiple phase III randomized controlled trials (RCTs) (9-11). The latest 5-year analysis from the CheckMate 816 trial demonstrated a significant event-free survival (EFS) benefit [5-year hazard ratio (HR) =0.68] (10). Multiple large-scale phase III RCTs have further solidified the clinical standing of perioperative immunotherapy (12-19). More recently, the CheckMate 77T trial demonstrated that perioperative nivolumab yielded a significant EFS improvement compared with neoadjuvant chemotherapy plus placebo followed by adjuvant placebo [HR =0.61; 95% confidence interval (CI): 0.41–0.91] (13). Studies on this topic have consistently demonstrated that compared to chemotherapy alone, both neoadjuvant and perioperative immunotherapies significantly improve pathological response rates and survival outcomes. However, despite these remarkable advances, direct head-to-head comparisons between these approaches are lacking. It therefore remains unclear whether extending immunotherapy into the adjuvant phase confers superior survival benefits over the neoadjuvant-only approach, and an international consensus on the optimal therapeutic strategy for this population has yet to be established.

A meta-analysis conducted by Meng et al. (20) suggested that compared to other modalities, perioperative immunotherapy may possess a potential survival advantage. However, due to reliance on aggregate data, traditional meta-analyses are limited in their ability to fully account for time-dependent survival outcomes and patient-level heterogeneity. To overcome these limitations and achieve a direct “head-to-head” comparison, we conducted a quantitative analysis using individual patient data (IPD) reconstructed from Kaplan-Meier (KM) curves of pivotal RCTs (21,22).

To ensure the robustness of this comparison and evaluate the treatment strategies across different statistical dimensions, we employed a comprehensive statistical framework. This framework integrated standard survival metrics, including KM estimation (21,23,24), log-rank tests (25,26), and Cox proportional hazards regression (27,28), with advanced estimands such as restricted mean survival time (RMST) (29,30) and parametric Weibull distribution models (31,32), which could capture long-term survival dynamics. To the best of our knowledge, this is the first study to employ a unified IPD framework to directly compare the efficacy of neoadjuvant and perioperative immunotherapy in treating patients with stage III NSCLC. The overall aim of this work is to provide robust evidence that can support future clinical trial design and individualized treatment decision-making. We present this article in accordance with the PRISMA-DTA reporting checklist (33) (Figure S1) (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0143/rc).

Methods

Study design and protocol registration

The study protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD420251115901.

Search strategy and selection criteria

We performed a comprehensive, systematic search of the PubMed, Embase, Cochrane Library, and Web of Science databases from their inception through June 21, 2025. The search strategy included a combination of keywords related to the disease (e.g., “non-small cell lung cancer”), intervention strategy (e.g., “neoadjuvant” and “perioperative”), and certain ICIs (Table S1). We also manually screened the reference lists of eligible articles to identify additional relevant studies.

Studies were selected based on the PICOS (Population/Patient, Intervention, Comparison/Control, Outcome, and Study) framework. The inclusion criteria were as follows: (I) RCTs published in English; (II) patients with pathologically confirmed resectable stage IIIA, IIIB, or combined IIIA–IIIB NSCLC; (III) a comparison between neoadjuvant and perioperative immunotherapy arms (or inclusion of both arms for indirect comparison); and (IV) a provision of KM survival curves for OS, EFS, or progression-free survival (PFS) with sufficient resolution to permit IPD reconstruction. Studies were excluded if they were (I) retrospective studies or conference abstracts with insufficient data; (II) lacking extractable KM curves specific to the stage III population; or (III) duplicate publications (only the most recent/comprehensive dataset was included).

Data extraction and quality assessment

Data extraction was performed independently by two reviewers (J.L. and X.Z.) using a standardized electronic form. Key extracted variables included the National Clinical Trial (NCT) identifier, lead author, publication year, study region, sample size, tumor stage distribution, treatment regimens, and median follow-up duration. Any discrepancies were resolved through discussion or consultation with a third investigator (F.S.).

IPD reconstruction

Reconstruction of time-to-event data constituted the core methodological component of this study. KM curves for EFS, PFS, and OS, alongside the reported numbers at risk, were digitized from the original publications. Two authors independently performed the reconstruction using the IPD from the KM method (21,23), which implements the Guyot algorithm to derive IPD from digitized coordinates. The accuracy of the reconstructed data was validated through the visual superimposition of the derived survival curves onto the original published curves. In instances of significant deviation, the digitization process was repeated to ensure data fidelity.

Risk-of-bias assessment

The risk of bias for the included RCTs was evaluated via the Cochrane risk-of-bias tool 2.0 (RoB 2) (34). Assessment domains included the randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result (visual summaries of the risk of bias assessment are presented in the “Results” section and Figure S2).

End points and population definitions

The primary efficacy end points were OS and EFS/PFS. OS was defined as the time from randomization to death from any cause. To address heterogeneity in trial reporting, EFS and PFS were harmonized as a single surrogate end point reflecting the time to disease progression, recurrence, or death (20,35); EFS was prioritized for extraction when both were available. The analysis focused on resectable stage III NSCLC, with prespecified subgroup analyses performed for stage IIIA, stage IIIB, and the combined IIIA–IIIB cohorts.

Statistical analysis

Quantitative analyses were performed on the pooled reconstructed IPD, survival distributions were estimated via the KM method, and between-group differences were evaluated with the stratified log-rank test (25). Treatment effects were quantified as HRs with 95% CIs via Cox proportional hazards models. Models were stratified by trial to account for interstudy heterogeneity and to preserve within-trial randomization characteristics (27). Given the potential non-proportional hazards frequently observed in immunotherapy trials (e.g., delayed treatment effects or crossing survival curves), which render the standard HR insufficient for capturing the full treatment effect, the Cox proportional hazards model was used primarily for descriptive purposes. RMST analysis (29) was conducted to quantify the absolute survival time benefit, as this method provides a robust measure independent of the proportional hazards assumption. Furthermore, long-term survival outcomes were extrapolated via the parametric models based on the Weibull distribution (31). All statistical analyses were executed through the use of R software version 4.3.1 (The R Foundation for Statistical Computing), specifically, the survival and flexsurv packages. All tests were two-sided, with statistical significance defined as P<0.05.

Results

Study selection

A total of seven studies involving 1,436 patients were included in the final quantitative analysis (Table S2). The cohort comprised six RCTs and one single-arm phase II trial (19). AEGEAN and KEYNOTE-671 were excluded as they reported only overall intention-to-treat (ITT) results, lacking the stage III-specific KM curves required for IPD reconstruction. Two studies evaluated neoadjuvant immunotherapy (10,11), while five assessed perioperative regimens (12,16-19). Regarding tumor staging, three trials specifically enrolled patients with stage IIIA disease (10,16,19), whereas four included the broader stage IIIA–IIIB population (11,12,17,18). In terms of geography, the analysis included four studies conducted in China, two conducted in multiple centers around the world, and one conducted in Europe, with median follow-up durations ranging from 14.1 to 72 months.

Quantitative analysis of EFS

Study selection and data harmonization

We synthesized EFS outcomes from seven clinical trials comparing neoadjuvant (9,11) or perioperative immunotherapy (12,16-19) with chemotherapy. Regarding study populations, three studies (9,16,19) exclusively enrolled patients with stage IIIA disease, whereas four trials (11,12,17,18) included mixed-stage IIIA and IIIB NSCLC cohorts.

To address the substantial heterogeneity in follow-up duration, which ranged from 14.4 to 41 months in six studies compared to a median of 72 months in the SAKK 16/14 trial (19), a uniform truncation time of 30 months was applied to the reconstructed IPD. This methodological adjustment was implemented to mitigate potential bias arising from the instability of survival estimates at the tail of the curves and to ensure the comparability of risk sets across the pooled analysis.

Comparative efficacy analysis

Overall population analysis

Direct comparison in the overall stage III population demonstrated equivalent efficacy in terms of EFS between the neoadjuvant and perioperative strategies, supported by consistent results from both the log-rank test (P=0.94) and Cox regression (HR =0.99; 95% CI: 0.72–1.36) (Figure 1A). This equivalence was further visually corroborated by the overlapping trajectories of the fitted Weibull curves (Figure 1B). Both immunotherapy strategies conferred significant survival advantages over chemotherapy alone (P<0.01), exhibiting a virtually identical magnitude of effect.

Figure 1 EFS and Weibull survival analysis in the intent-to-treat population. (A) KM curve of EFS for the overall stage III cohort. (B) Predicted 5-year survival rate for stage III disease based on the Weibull distribution. (C) EFS for the stage IIIA subpopulation. (D) Predicted 5-year survival rate for the stage IIIA–IIIB subpopulation. Chem, chemotherapy; CI, confidence interval; EFS, event-free survival; KM, Kaplan-Meier; Neo, neoadjuvant; Periop, perioperative.

Subgroup analysis by disease stage

Stratification revealed stage-dependent heterogeneity. Although the outcomes were comparable in the stage IIIA cohort (HR =1.04; P=0.87) (Figure 1C), the results tended to favor neoadjuvant therapy for the mixed-stage IIIA–IIIB cohort (HR =0.52; 95% CI: 0.27–1.03; P=0.05) (Figure 1D).

RMST analysis and long-term survival projections

RMST analysis indicated comparable efficacy in the overall population, with negligible differences at the 12-month (perioperative: 10.82; neoadjuvant: 10.72 months) and 24-month benchmarks (perioperative: 19.29; neoadjuvant: 19.37 months) (Table 1, Figure S3). However, subgroup patterns diverged: while stage IIIA outcomes were highly concordant, in the mixed-stage IIIA–IIIB cohort, neoadjuvant therapy had a numerical advantage at both 1 year (11.42 vs. 10.92 months) and 2 years (21.28 vs. 19.49 months) (Table 1, Figures S4,S5). The long-term Weibull projections mirrored these trends (Table 1, Figures S6-S11). In the overall population, neoadjuvant therapy showed a marginal 5-year absolute gain of 1.89% over perioperative therapy (Table 1, Figures S9,S10). Consistent with RMST findings, projections slightly favored perioperative therapy in the stage IIIA subgroup, while neoadjuvant therapy appeared more beneficial for the mixed-stage IIIA–IIIB cohort (Table 1, Figures S6,S7).

Quantitative analysis of OS

Rationale for cohort selection

A marked disparity in data maturity was observed across the included trials. Although the studies involving mixed-stage IIIA–IIIB populations [i.e., NADIM II (17) and NEOTORCH (18)] report a limited median follow-up (18.3–26.1 months), those targeting stage IIIA disease [i.e., CheckMate 816 (13) and SAKK 16/14 (11)] provide mature data exceeding 5 years (range, 68.4–72.0 months). To ensure the robustness of long-term estimates, OS analysis was strictly confined to the stage IIIA subpopulation.

Pooled analysis of OS outcomes

The pooled KM analysis showed no significant difference in OS between the neoadjuvant and perioperative therapy groups (P=0.92), with both demonstrating an improving trend compared with chemotherapy alone (P=0.08 and P=0.16, respectively). Cox regression analysis for stage IIIA NSCLC further confirmed that both neoadjuvant (HR =0.68) and perioperative therapies (HR =0.70) provided OS benefits over chemotherapy, with no significant difference observed between the two experimental arms (HR =0.97) (Figure 2A).

Figure 2 OS and quantitative analysis via Weibull distribution and RMST for patients with stage IIIA NSCLC in the intent-to-treat population. (A) KM curve of OS in the stage IIIA cohort. (B-D) Predicted 5-year survival rates based on the Weibull distribution for the neoadjuvant, perioperative, and chemotherapy groups. (E) RMST analysis comparing the neoadjuvant, perioperative, and chemotherapy groups among the stage IIIA population. Chem, chemotherapy; CI, confidence interval; KM, Kaplan-Meier; Neo, neoadjuvant; NSCLC, non-small cell lung cancer; OS, overall survival; Periop, perioperative; RMST, restricted mean survival time.

Long-term survival projection

Weibull distribution modeling projected that stage IIIA patients receiving either neoadjuvant or perioperative therapy would exhibit favorable long-term outcomes, with estimated median survival times of approximately 100 months. Both groups showed comparable projected survival rates at 1, 3, and 5 years. In contrast, the projected median survival for the chemotherapy group was markedly lower at 65.68 months, with consistently inferior survival rates at all time points (Figure 2B-2D).

To quantify absolute survival gains, RMST was calculated at a 5-year horizon. Among stage IIIA patients, the 5-year RMSTs for neoadjuvant therapy, perioperative therapy, and chemotherapy were 48.51, 47.56, and 42.82 months, respectively. Both immunotherapy strategies demonstrated significant survival benefits over chemotherapy (RMST difference P=0.02 and P=0.03, respectively). However, in a direct head-to-head comparison, the survival benefit of neoadjuvant therapy over perioperative therapy did not reach statistical significance (RMST difference =0.95 months; 95% CI: −3.77 to 5.67; P=0.69) (Figure 2E).

Discussion

This study represents the first comprehensive quantitative analysis to use reconstructed IPD from seven clinical trials to compare neoadjuvant and perioperative immunotherapy for the treatment of stage III NSCLC. Our findings indicate that extending immunotherapy into the adjuvant setting suggests a limited incremental survival benefit over neoadjuvant therapy alone. These results challenge the ‘more is better’ treatment paradigm and provide a rationale to hypothesize that a de-escalated neoadjuvant-only strategy may be sufficient for a substantial proportion of patients with stage III NSCLC.

EFS has been established as a robust end point for evaluating therapeutic efficacy in patients with resectable NSCLC (36,37). In the overall population, our pooled analysis demonstrated comparable efficacy between the two strategies. The consistency across KM estimates, Cox regression, and RMST analysis results further reinforces the notion that the addition of adjuvant therapy yields negligible incremental benefit in preventing disease recurrence. The consistency across KM estimates, Cox regression, and RMST analysis results further reinforces the notion that the addition of adjuvant therapy may yield negligible incremental benefit in preventing disease recurrence. These findings provide a rationale for investigating treatment de-escalation strategies in future trials, potentially sparing patients from the toxicity and financial burden associated with prolonged postoperative maintenance therapy.

We observed stage-dependent heterogeneity in the subgroup analyses. Although the outcomes were indistinguishable in the stage IIIA cohort, the mixed-cohort, including stage IIIB patients, tended to receive greater benefit from the neoadjuvant-only approach. From a biological perspective, neoadjuvant immunotherapy relies on the presence of a macroscopic tumor to serve as an antigen source for expanding tumor-specific T cells (38,39). In patients with high tumor burden (stage IIIB), this “in situ vaccination” effect induced during the induction phase may be sufficient to eradicate micrometastases. Consequently, continuing adjuvant therapy after tumor resection may offer limited incremental biological benefit. In the clinical setting, the surgical complexity associated with locally advanced disease often leads to prolonged recovery or complications, resulting in significant postoperative treatment attrition. Thus, the theoretical benefit of adjuvant consolidation is frequently compromised by reduced feasibility. Although certain retrospective research suggests benefits for perioperative therapy (40), our IPD analysis underscores the need for cautious interpretation and further head-to-head validation in this specific population (41).

Additionally, adjuvant therapy efficacy likely depends on pathological response. Emerging evidence suggests that patients achieving major pathological response (MPR) without pathological complete response (pCR) derive greater benefit from adjuvant consolidation compared to those achieving pCR (9,12,42). However, as the included trials lacked KM curves stratified by MPR or pCR specifically for the stage III population, we were unable to perform specific subgroup analyses. Consequently, while our pooled analysis suggests limited incremental benefit in the overall stage III population, we cannot preclude the possibility that the residual disease (non-pCR) subgroup may still derive significant benefit from adjuvant intervention. To move beyond a ‘one-size-fits-all’ approach, ctDNA dynamics (clearance vs. persistence) can effectively distinguish candidates for treatment omission from those requiring adjuvant consolidation (43,44). Therefore, future IPD meta-analyses should incorporate these dynamic variables to validate personalized de-escalation strategies.

OS remains the gold standard metric of long-term efficacy (45). To ensure data maturity and comparability, we confined our analysis to the stage IIIA population. In this cohort, neoadjuvant and perioperative strategies yielded indistinguishable survival outcomes, evidenced by overlapping HRs and consistent 5-year RMST estimates. These findings imply that for patients with stage IIIA NSCLC, the maximal survival benefit is likely achieved during the neoadjuvant induction phase.

Consequently, omitting the adjuvant component represents a potential de-escalation strategy worthy of further investigation, as it could minimize the treatment burden while maintaining comparable long-term survival outcomes, particularly for patients at high surgical risk or those preferring a shorter treatment duration (46,47).

Several limitations of this study should be acknowledged. First, the analysis of the Stage IIIA–IIIB subgroup was primarily driven by a single trial, resulting in a significant sample size imbalance between the neoadjuvant (n=88) and perioperative (n=406) arms; consequently, the borderline significance (P=0.05) should be interpreted with caution as an exploratory trend. Second, the inclusion of single-arm trials lacks randomization, and the use of reconstructed IPD precludes multivariable adjustments for baseline covariates, limiting causal inference and potentially introducing confounding bias. Third, inconsistent original reporting prevented pooled analyses of biomarkers (e.g., PD-L1), safety end points, or OS. Finally, due to significant heterogeneity in follow-up duration (14.1–72 months), a uniform 30-month truncation was applied to ensure statistical stability, as numbers at risk in the perioperative arm were insufficient beyond this point. While this limits the assessment of late-term events, data immaturity rendered sensitivity analyses at extended thresholds infeasible.

Conclusions

In conclusion, this meta-analysis suggests that, particularly in patients with stage IIIA NSCLC, neoadjuvant immunotherapy may yield OS and EFS benefits comparable to those of the more intensive perioperative approach. These findings provide a rationale for investigating treatment de-escalation strategies to optimize resource allocation and minimize toxicity. Future large-scale, head-to-head RCTs with extended follow-up are warranted to definitively guide precision treatment strategies for patients with stage IIIB disease.

Acknowledgments

Declaration of generative AI and AI-assisted technologies in the writing process: during the preparation of this work, the authors used ChatGPT exclusively in order to improve the readability and language quality of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Footnote

Reporting Checklist: The authors have completed the PRISMA-DTA reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0143/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0143/prf

Funding: This study was funded by the Beijing Science and Technology Innovation Medical Development Foundation (No. KC2021-JX-0186-59).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0143/coif). W.G. reports that this study was funded by the Beijing Science and Technology Innovation Medical Development Foundation (No. KC2021-JX-0186-59). 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. Wagle NS, Nogueira L, Devasia TP, et al. Cancer treatment and survivorship statistics, 2025. CA Cancer J Clin 2025;75:308-40. [Crossref] [PubMed]
2. 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]
3. Rami-Porta R, Nishimura KK, Giroux DJ, et al. The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groups in the Forthcoming (Ninth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2024;19:1007-27. [Crossref] [PubMed]
4. Riely GJ, Wood DE, Ettinger DS, et al. Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2024;22:249-74. [Crossref] [PubMed]
5. Daly ME, Singh N, Ismaila N, et al. Management of Stage III Non-Small-Cell Lung Cancer: ASCO Guideline. J Clin Oncol 2022;40:1356-84. [Crossref] [PubMed]
6. Remon J, Soria JC, Peters S, et al. Early and locally advanced non-small-cell lung cancer: an update of the ESMO Clinical Practice Guidelines focusing on diagnosis, staging, systemic and local therapy. Ann Oncol 2021;32:1637-42. [Crossref] [PubMed]
7. Houda I, Dickhoff C, Uyl-de Groot CA, et al. Challenges and controversies in resectable non-small cell lung cancer: a clinician's perspective. Lancet Reg Health Eur 2024;38:100841. [Crossref] [PubMed]
8. Liang W, Cai K, Chen C, et al. Expert consensus on neoadjuvant immunotherapy for non-small cell lung cancer. Transl Lung Cancer Res 2020;9:2696-715. [Crossref] [PubMed]
9. Forde PM, Spicer J, Lu S, et al. Neoadjuvant Nivolumab plus Chemotherapy in Resectable Lung Cancer. N Engl J Med 2022;386:1973-85. [Crossref] [PubMed]
10. Forde PM, Spicer JD, Provencio M, et al. Overall Survival with Neoadjuvant Nivolumab plus Chemotherapy in Lung Cancer. N Engl J Med 2025;393:741-52. [Crossref] [PubMed]
11. Lei J, Zhao J, Gong L, et al. Neoadjuvant Camrelizumab Plus Platinum-Based Chemotherapy vs Chemotherapy Alone for Chinese Patients With Resectable Stage IIIA or IIIB (T3N2) Non-Small Cell Lung Cancer: The TD-FOREKNOW Randomized Clinical Trial. JAMA Oncol 2023;9:1348-55. [Crossref] [PubMed]
12. Cascone T, Awad MM, Spicer JD, et al. LBA1 CheckMate 77T: Phase III study comparing neoadjuvant nivolumab (NIVO) plus chemotherapy (chemo) vs neoadjuvant placebo plus chemo followed by surgery and adjuvant NIVO or placebo for previously untreated, resectable stage II–IIIb NSCLC. Ann Oncol 2023;34:S1295.
13. Cascone T, Awad MM, Spicer J, et al. Perioperative nivolumab (NIVO) vs placebo (PBO) in patients (pts) with resectable NSCLC: Updated survival and biomarker analyses from CheckMate 77T. J Clin Oncol 2025;43:LBA8010.
14. Heymach JV, Harpole D, Mitsudomi T, et al. OA13. 03 Perioperative durvalumab for resectable NSCLC (R-NSCLC): updated outcomes from the phase 3 AEGEAN trial. J Thorac Oncol 2024;19:S38-9.
15. Spicer JD, Garassino MC, Wakelee H, et al. Neoadjuvant pembrolizumab plus chemotherapy followed by adjuvant pembrolizumab compared with neoadjuvant chemotherapy alone in patients with early-stage non-small-cell lung cancer (KEYNOTE-671): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 2024;404:1240-52. [Crossref] [PubMed]
16. Yue D, Wang W, Liu H, et al. VP1-2024: RATIONALE-315: Event-free survival (EFS) and overall survival (OS) of neoadjuvant tislelizumab (TIS) plus chemotherapy (CT) with adjuvant TIS in resectable non-small cell lung cancer (NSCLC). Ann Oncol 2024;35:332-3.
17. Provencio M, Nadal E, González-Larriba JL, et al. Perioperative Nivolumab and Chemotherapy in Stage III Non-Small-Cell Lung Cancer. N Engl J Med 2023;389:504-13. [Crossref] [PubMed]
18. Lu S, Zhang W, Wu L, et al. Perioperative Toripalimab Plus Chemotherapy for Patients With Resectable Non-Small Cell Lung Cancer: The Neotorch Randomized Clinical Trial. JAMA 2024;331:201-11. [Crossref] [PubMed]
19. Rothschild SI, Zippelius A, Hayoz S, et al. 189MO: Perioperative durvalumab in addition to neoadjuvant chemotherapy in patients with stage IIIa (N2) non-small cell lung cancer: Final analysis of the trial SAKK 16/14. J Thorac Oncol 2025;20:S126-7.
20. Meng Y, Zhang Q, Wu R, et al. Efficacy and safety of perioperative, neoadjuvant, or adjuvant immunotherapy alone or in combination with chemotherapy in early-stage non-small cell lung cancer: a systematic review and meta-analysis of randomized clinical trials. Ther Adv Med Oncol 2024;16:17588359241284929.
21. Liu N, Zhou Y, Lee JJ. IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves. BMC Med Res Methodol 2021;21:111. [Crossref] [PubMed]
22. Parisi C, Abdayem P, Tagliamento M, et al. Neoadjuvant immunotherapy strategies for resectable non-small cell lung cancer (NSCLC): Current evidence among special populations and future perspectives. Cancer Treat Rev 2024;131:102845. [Crossref] [PubMed]
23. Guyot P, Ades AE, Ouwens MJ, et al. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol 2012;12:9. [Crossref] [PubMed]
24. Marinelli D, Nuccio A, Di Federico A, et al. Improved Event-Free Survival After Complete or Major Pathologic Response in Patients With Resectable NSCLC Treated With Neoadjuvant Chemoimmunotherapy Regardless of Adjuvant Treatment: A Systematic Review and Individual Patient Data Meta-Analysis. J Thorac Oncol 2025;20:285-95. [Crossref] [PubMed]
25. Mantel N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep 1966;50:163-70.
26. Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 2016;31:337-50. [Crossref] [PubMed]
27. Cox DR. Regression models and life-tables. J R Stat Soc Series B Stat Methodol 1972;34:187-202.
28. Brahmer JR, Lee JS, Ciuleanu TE, et al. Five-Year Survival Outcomes With Nivolumab Plus Ipilimumab Versus Chemotherapy as First-Line Treatment for Metastatic Non-Small-Cell Lung Cancer in CheckMate 227. J Clin Oncol 2023;41:1200-12. [Crossref] [PubMed]
29. Uno H, Claggett B, Tian L, et al. Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J Clin Oncol 2014;32:2380-5. [Crossref] [PubMed]
30. Liang F, Zhang S, Wang Q, et al. Treatment effects measured by restricted mean survival time in trials of immune checkpoint inhibitors for cancer. Ann Oncol 2018;29:1320-4. [Crossref] [PubMed]
31. Carroll KJ. On the use and utility of the Weibull model in the analysis of survival data. Control Clin Trials 2003;24:682-701. [Crossref] [PubMed]
32. Plana D, Fell G, Alexander BM, et al. Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects. Nat Commun 2022;13:873. [Crossref] [PubMed]
33. Stewart LA, Clarke M, Rovers M, et al. Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement. JAMA 2015;313:1657-65. [Crossref] [PubMed]
34. Sterne JAC, Savović J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 2019;366:l4898. [Crossref] [PubMed]
35. Baik CS, Rubin EH, Forde PM, et al. Immuno-oncology Clinical Trial Design: Limitations, Challenges, and Opportunities. Clin Cancer Res 2017;23:4992-5002. [Crossref] [PubMed]
36. Groome PA, Bolejack V, Crowley JJ, et al. The IASLC Lung Cancer Staging Project: validation of the proposals for revision of the T, N, and M descriptors and consequent stage groupings in the forthcoming (seventh) edition of the TNM classification of malignant tumours. J Thorac Oncol 2007;2:694-705. [Crossref] [PubMed]
37. Schmid P, Cortes J, Pusztai L, et al. Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med 2020;382:810-21. [Crossref] [PubMed]
38. Liu J, Blake SJ, Yong MC, et al. Improved Efficacy of Neoadjuvant Compared to Adjuvant Immunotherapy to Eradicate Metastatic Disease. Cancer Discov 2016;6:1382-99. [Crossref] [PubMed]
39. Topalian SL, Taube JM, Pardoll DM. Neoadjuvant checkpoint blockade for cancer immunotherapy. Science 2020;367:eaax0182. [Crossref] [PubMed]
40. Yang Y, Li S, Zhang Q, et al. Comparative investigation of neoadjuvant chemoimmunotherapy versus perioperative (chemo)immunotherapy in resectable non-small cell lung cancer: a real-world retrospective cohort study. Transl Lung Cancer Res 2025;14:2954-68. [Crossref] [PubMed]
41. Marinelli D, Gallina FT, Pannunzio S, et al. Surgical and survival outcomes with perioperative or neoadjuvant immune-checkpoint inhibitors combined with platinum-based chemotherapy in resectable NSCLC: A systematic review and meta-analysis of randomised clinical trials. Crit Rev Oncol Hematol 2023;192:104190. [Crossref] [PubMed]
42. Zhao J, Huang B, Li M, et al. Efficacy of adjuvant therapy regimens administered during the perioperative period in resectable non-small cell lung cancer. Cancer Immunol Immunother 2025;74:296. [Crossref] [PubMed]
43. Bai L, Courcoubetis G, Mason J, et al. Longitudinal tracking of circulating rare events in the liquid biopsy of stage III-IV non-small cell lung cancer patients. Discov Oncol 2024;15:142. [Crossref] [PubMed]
44. Scimone C, Palumbo L, Borea R, et al. Innovation in next-generation sequencing in non-small cell lung cancer diagnostics. Expert Rev Anticancer Ther 2025;25:1333-51. [Crossref] [PubMed]
45. Saad ED, Buyse M. Statistical controversies in clinical research: end points other than overall survival are vital for regulatory approval of anticancer agents. Ann Oncol 2016;27:373-8. [Crossref] [PubMed]
46. Felip E, Rosell R, Maestre JA, et al. Preoperative chemotherapy plus surgery versus surgery plus adjuvant chemotherapy versus surgery alone in early-stage non-small-cell lung cancer. J Clin Oncol 2010;28:3138-45. [Crossref] [PubMed]
47. Strauss GM, Herndon JE 2nd, Maddaus MA, et al. Adjuvant paclitaxel plus carboplatin compared with observation in stage IB non-small-cell lung cancer: CALGB 9633 with the Cancer and Leukemia Group B, Radiation Therapy Oncology Group, and North Central Cancer Treatment Group Study Groups. J Clin Oncol 2008;26:5043-51. [Crossref] [PubMed]

(English Language Editor: J. Gray)