Time to Benefit of Perioperative Immunotherapy Among Patients With Resectable Non–Small-Cell Lung Cancer

Time to Benefit of Perioperative Immunotherapy Among Patients With Resectable Non–Small-Cell Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Time to Benefit of Perioperative Immunotherapy Among Patients With Resectable Non–Small-Cell Lung Cancer Yongyan Lu, Wenming Bian, Xiaohui Jia, Jingyi Li, Mengjie Liu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8788627/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background The optimal duration of perioperative immunotherapy for resectable non–small-cell lung cancer (NSCLC) remains undefined, and conventional efficacy measures provide limited insight into the temporal pattern of benefit emergence, for which time-to-benefit (TTB) serves as a complementary metric that quantifies how therapeutic effects accumulate over time and bridges the gap between treatment duration and clinical benefit. This study aimed to quantitatively assess the TTB of perioperative immunotherapy and determine the treatment duration required to achieve predefined absolute risk reduction (ARR) thresholds. Methods This comparative effectiveness study reconstructed individual patient data from eight randomized controlled trials (RCTs) using digitized Kaplan-Meier curves. Pooled survival analyses and Weibull survival modeling with Monte Carlo simulation were applied to estimate TTB at ARR thresholds. Subgroup analyses were conducted by histology, stage, and PD-L1 expression. Results Among 5,123 participants, the entire perioperative immunotherapy significantly improved event-free survival (EFS) and overall survival (OS). TTB analysis showed a near-linear accumulation of benefit: 1.02 months of therapy were required for a 1% EFS-related risk reduction and 9.01 months for a 10% reduction. For OS, a 1% mortality risk reduction required 10.67 months. Subgroup analyses demonstrated that, for the same ARR thresholds, patients with squamous histology, stage III disease, or PD-L1 expression ≥ 1% achieved shorter TTB. Conclusions This study quantitatively evaluated TTB in perioperative immunotherapy for resectable NSCLC, revealing a near-linear accumulation of benefit with meaningful efficacy by 9–10 months, and establishing TTB as a complementary endpoint to guide evidence-based, individualized treatment duration optimization. Non–small cell lung cancer Perioperative therapy Immune checkpoint inhibitors Time to benefit Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lung cancer represents the most common cause of cancer-related deaths, with non-small-cell lung cancer (NSCLC) accounting for approximately 85% of cases [ 1 , 2 ]. While surgical resection remains the standard treatment for early-stage NSCLC management, 30–55% of patients will relapse and develop metastases post-surgery [ 3 , 4 ]. Multiple randomized controlled trials (RCTs) have confirmed that, in patients with resected NSCLC, perioperative therapy (neoadjuvant followed by adjuvant) significantly reduces tumor relapse. The addition of immunotherapy is associated with improved outcomes in patients with driver gene-negative NSCLC compared to adjuvant platinum-based doublet chemotherapy alone, leading to guidelines recommending it as a first-line therapy [ 5 – 12 ]. While perioperative immune checkpoint inhibitors (ICIs) are effective in early-stage NSCLC, their prolonged use may lead to increased medical and economic toxicity, highlighting the need to determine the optimal treatment duration to balance therapeutic efficacy with potential risks [ 13 – 15 ]. However, the optimal length of adjuvant therapy has not yet been determined. Although most clinical trials adopt a fixed one-year adjuvant regimen, evidence supporting this duration, particularly analyses of the temporal dynamics of treatment benefits, remains limited[ 16 , 17 ]. Conventional efficacy endpoints provide important measures of therapeutic effect but offer limited insight into how benefits emerge and evolve over time[ 18 – 20 ]. Disease-free survival (DFS) in the adjuvant setting and event-free survival (EFS) in the neoadjuvant or the entire perioperative setting are influenced by the timing and frequency of follow-up assessments, which may obscure the precise onset of clinical benefit. Overall survival (OS), while the most definitive endpoint, requires prolonged observation and is often confounded by subsequent post-recurrence therapies, making it difficult to isolate the direct impact of perioperative treatment. Objective response rate (ORR) reflects early tumor shrinkage but lacks correlation with durable outcomes and does not capture benefit among patients with stable disease. Collectively, these measures describe the magnitude of efficacy but not its temporal trajectory. Their inherent dependence on assessment schedules, data maturity, and subsequent interventions constrains their ability to delineate when clinically meaningful benefits begin or the duration of exposure required to achieve them. Recognizing these structural limitations underscores the importance of developing complementary analytical frameworks that integrate the time-dependent nature of treatment efficacy, thereby enhancing the temporal interpretability of established endpoints and guiding evidence-based optimization of therapy duration in perioperative NSCLC. Time to Benefit (TTB) represents a complementary analytical framework that quantifies the time from treatment initiation to the attainment of a predefined clinical benefit, capturing the precise moment at which survival curves diverge, signaling the onset of therapeutic effect. From a methodological perspective, TTB advances beyond conventional endpoints through the integration of two fundamental components: first, it estimates actual benefit dynamics by fitting a Weibull survival distribution to individual-level participant data derived from published RCTs; second, it establishes a well-defined absolute risk reduction (ARR) threshold via Monte Carlo simulation, thereby translating the abstract notion of therapeutic benefit into a quantifiable clinical benchmark. By integrating temporal dynamics with explicit clinical benefit thresholds, TTB enables a dual-dimensional assessment of both the speed and magnitude of treatment effect [ 21 , 22 ]. Our prior research has assessed TTB across various clinical settings, including the use of SGLT-2 inhibitors in heart failure patients, intensive blood pressure management in the elderly, and androgen deprivation therapy (ADT) for localized prostate cancer [ 23 – 25 ]. Exploring the TTB of perioperative immunotherapy offers a more nuanced evaluation of the time required to achieve therapeutic benefit, thereby addressing the treatment duration determination imperatives in adjuvant immunotherapy.​ This study aim to systematically evaluate the TTB of perioperative immunotherapy in patients with resectable early-stage NSCLC for the first time. It determined the treatment duration required to achieve clinically meaningful effects and contribute to identifying patient subsets that may require extended therapy, thereby providing precise, evidence-based guidance for treatment duration decisions. Study Design and Methods Design This comparative effectiveness research study used secondary data sets based on randomized clinical trials. To ensure the recent results could accurately reflect effectiveness of perioperative immunotherapy, we followed the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) reporting guideline and addressed issues of framing the research question and reporting and interpreting findings. Data Source and Searches This study was performed based on up-to-date published research. To ensure the completeness of including all perioperative immunotherapy, we did a systematic review of the literature. Two independent reviewers (Y.Y.L. and W.M.B.) searched relevant RCTs in PubMed that were published until May, 1, 2025. Both reviewers screened titles and abstracts, followed by full texts, and a third reviewer (X.H.J.) cross-checked the screening decision. The search strategy is illustrated in the eAppendix in Additional File following the previous systematic review and meta-analysis. In the present analysis, we only included RCTs comparing perioperative immunotherapy vs chemotherapy/placebo on EFS, DFS or OS among patients with resectable NSCLC. To serve the purpose of calculating TTB, we included studies having vector Kaplan-Meier (KM) curves, which enabled us to reconstruct individual time-to-event data from the number of patients at risk and the KM graph. A total of 151 articles were initially identified through a PubMed search. Of these, 106 were excluded for the following reasons: non-human studies (2 articles), meta-analyses or reviews (30 articles), and other types (such as letters, comments) (74 articles). Among the remaining 45 articles, we excluded RCTs of patients who did not receive adjuvant immunotherapy (24 articles), articles with post hoc or secondary analyses (2 articles), and articles regarding study protocols or trial baselines (11 articles) ultimately identifying 8 RCTs for inclusion in the analysis. The flow chart was shown in Additional File Figure S1 . In subsequent studies, we included the results of the latest randomized clinical trials that had been published recently. Data Reconstruction To estimate a pooled time lag to benefit across trials, we extracted and reconstruction survival curves for the control and intervention groups for each study before combining the survival curves in our meta-analysis to obtain pooled estimates of ARR. We reconstructed individual time-to-event data in line with our previous publication through a 2-stage process. First, the quality data coordinates (survival probability and time) were extracted from KM curves by DigitizeIt software version 2.5 following the instructions from Liu and Lee. In this stage, we also followed the recommendation when extracting data points. For example, extract as many points as possible and make sure the data points extracted are evenly distributed on the KM curves. Second, a Stata function (ipdfc command) developed by Wei and Royston was used to rebuild the individual data based on the aforementioned extracted raw data of time and survival probability. The algorithm underpinning the ipdfc command has been successfully used in our previous study, and basically aimed to estimate the number of censorings, the number of events, the censoring time, and the event time. Visual comparisons between the reconstructed and original survival curves ( Additional File Figure S2-4 ) confirmed the consistency of the reconstructed individual patient data with the original studies. We found that this algorithm recovered individual participant data from published trials with a high degree of accuracy. Statistics The characteristics of included studies were summarized from publications. Primary endpoints were EFS/progression-free survival (PFS) and DFS. EFS/PFS was the time from randomization to any of: progression precluding surgery, abandonment of surgery for unresectability, postoperative recurrence/progression, or death from any cause. DFS was the time from curative-intent surgery to recurrence or death from any cause. The secondary endpoint was OS, measured from randomization to death from any cause; survivors were censored at last contact. The cumulative rates of primary outcome at each time point in the placebo and perioperative immunotherapy group from the pooled trials were estimated using the KM curve. The hazard ratios (HRs) and 95% confidence interval (95% CI) were calculated using the stratified Cox proportional hazards model to adjust for the clustering of patients from the same trial. We also calculated pooled HRs and 95% CI using study-level meta-analysis to further estimate the efficacy of perioperative immunotherapy. Meanwhile, heterogeneity between included studies was evaluated using the χ 2 and I 2 tests. We fitted Weibull survival curves to estimate the time to specific ARR thresholds (ie, 0.002, 0.005, 0.01, 0.02) using the conventional frequentist method to calculate the TTB and Monte Carlo simulations to derive its 95% CI. We further presented TTB estimations by the following characteristics: individual trials; participants with different types of tumor histology (squamous or nonsquamous); participants with different tumor staging (stage II or III) or different tumor PD-L1 expression (PD-L1 < 1% or ≥ 1%). Statistical analysis was performed from June to August 2025. The TTB calculation was conducted in R version 3.4.0 (R Project for Statistical Computing), and other analyses in this study were performed in Stata version 15.0 (StataCorp). Results Baseline characteristics The study characteristics were summarized in ( Additional File Table S1 ). All trials were assessed as high quality, with a low risk of bias across the 8 trials ( Additional File Table S2 ). A total of 5,123 participants were included in these trials. Preoperative neoadjuvant treatment cycles ranged from 3 to 4 cycles (21 days each), typically in combination with concurrent chemotherapy. Postoperative adjuvant therapy generally lasted for 1 year. A summary of the study characteristics was provided in Table 1 . For detailed information on the treatment regimens, please refer to the supplementary data ( Additional File Table S3 ). Table 1 Time to benefit (months) at specific thresholds of absolute survival benefits across treatment strategies Study characteristics Absolute risk reduction threshold (95% CI) 0.002 0.005 0.01 0.02 0.05 0.1 Perioperative immunotherapy (EFS/PFS, n = 2941) 0.22 (0.08–0.61) 0.49 (0.22–1.11) 1.02 (0.48–2.21) 1.77 (1.05–2.99) 4.29 (2.95–6.24) 9.01 (6.70–12.12) Adjuvant immunotherapy (DFS, n = 2182) 0.47 (0.04–5.68) 1.20 (0.18–8.02) 2.47 (0.55–11.12) 5.24 (1.65–16.68) 15.97 (6.70–38.10) NR Perioperative immunotherapy (OS, n = 2480) 6.59 (1.66–26.25) 8.33 (3.03–22.94) 10.67 (5.02–22.68) 14.51 (8.39–25.08) 24.05 (16.27–35.55) 39.57 (25.64–61.08) Adjuvant immunotherapy (OS, n = 2182) 30.18 (9.53–95.58) 33.89 (12.46–92.17) 39.42 (16.48–94.32) 49.29 (22.42–108.40) 77.31 (31.58–189.25) NR EFS, event-free survival; PFS, progression-free survival; OS, overall survival; NR, not reported. Primary TTB Analysis Outcomes Across pooled trials, the entire perioperative immunotherapy plus chemotherapy significantly improved EFS/PFS (HR 0.57, 95% CI 0.50–0.64, P < 0.001) and OS (HR 0.75, 95% CI 0.64–0.89, P < 0.001). This finding was further confirmed by a meta-analysis at the study level ( Additional File Figure S5-6 ). TTB modeling showed an early and near-linear accrual of benefit: 1.02 months of therapy corresponded to a 1% absolute reduction in EFS-related events (95% CI 0.48–2.21), 4.29 months to a 5% reduction (95% CI 2.95–6.24), and < 1 year (9.01 months; 95% CI 6.70–12.12) to a 10% reduction (EFS/PFS data were derived from the AEGEAN, Neotorch, NADIM II, KEYNOTE-671, CheckMate-77T and RATIONALE-315 trials) (Fig. 1 , Table 1 ). Using OS as the endpoint, benefit became statistically apparent after 6.59 months (95% CI 1.66–26.25), with 8.33 months corresponding to a 0.5% absolute mortality reduction (95% CI 3.03–22.94), 10.67 months to a 1% reduction (95% CI 5.02–22.68), and 14.51 months to a 2% reduction (95% CI 8.39–25.08) (the entire perioperative OS data were derived from the AEGEAN, Neotorch, NADIM II, KEYNOTE-671 and RATIONALE-315 trials) (Fig. 2 ). In contrast, the simple postoperative adjuvant immunotherapy achieved a smaller effect size and required longer exposure to reach comparable thresholds: DFS improved versus control (HR 0.79, 95% CI 0.69–0.90, P < 0.001), yet a 1% absolute reduction in DFS-related events required 2.47 months (95% CI 0.55–11.12), a 2% reduction 5.24 months (95% CI 1.65–16.68), and a 5% reduction 15.97 months (95% CI 6.70–38.10) (Fig. 3 , Table 1 ); no OS advantage was observed with adjuvant immunotherapy alone during available follow-up (HR 0.94, 95% CI 0.78–1.13, P = 0.502) ( Additional File Figure S5 ) (adjuvant DFS and OS data were derived from the IMpower010 and KEYNOTE-091 trials). Taken together, these findings indicate that the entire perioperative immunotherapy delivers earlier onset and greater magnitude of clinically meaningful benefit, achieving EFS and OS thresholds within the first year, whereas adjuvant-only strategies require longer treatment durations and have not demonstrated a survival advantage (1% absolute reduction in EFS-related events: 9.01 months [95% CI, 6.70–12.12]; DFS-related events: 2.47 months [95% CI, 0.55–11.12]), underscoring the temporal and quantitative superiority of the entire perioperative approach. Subgroup analysis of TTB Further subgroup TTB analyses were conducted based on histological subtypes, tumor stages and PD-L1 expression levels (subgroup data were derived from the AEGEAN, CheckMate-77T and RATIONALE-315 trials), and the results indicated significant differences in TTB among patients with different clinical characteristics (Fig. 4 ). Regarding histological subtypes, TTB results suggested that patients with squamous lung cancer experience a more rapid therapeutic benefit from the entire perioperative immunotherapy than non-squamous lung cancer. Using a 1% reduction in EFS-related risk events as a threshold, non-squamous carcinoma patients required twice the treatment duration of squamous cell carcinoma patients (squamous: 0.96 months [95% CI, 0.25–3.62]; non-squamous: 2.59 months [95% CI, 0.41–16.41]). It supported prioritizing the entire perioperative immunotherapy for squamous cell carcinoma patients to derive benefit earlier. Among patients with different disease stages, those with stage III cancer exhibited a markedly shorter TTB than stage II patients. Specifically, a 1% reduction in EFS-related risk events was achieved in 1.19 months (95% CI 0.34–4.19) in the stage III cohort, compared to 4.3 months in the stage II group (95% CI 0.23–78.98). TTB outcomes corresponding to additional ARR thresholds (e.g., 2%, 5%) consistently demonstrate that stage III patients exhibit accelerated clinical benefit. Furthermore, PD-L1 expression levels represent a critical factor influencing TTB. Patients with PD-L1 expression ≥ 1% exhibited a significantly shorter TTB compared to those with PD-L1 expression < 1%. Remarkably, the former group attained a 2% reduction in EFS-related risk events after only one month of treatment (95% CI 0.51–2.13), representing the most rapid response observed across all subgroups (Table 2 ). Table 2 Subgroup analysis of time to benefit (months) at specific absolute risk reduction thresholds Absolute risk reduction threshold(95% CI) Subgroup 0.002 0.005 0.01 0.02 PD-L1 < 1% (n = 572) 0.44 (0.02–8.79) 0.93 (0.09–9.13) 1.64 (0.27–10.18) 2.95 (0.71–12.25) PD-L1 ≥ 1% (n = 1014) 0.10 (0.03–0.28) 0.25 (0.10–0.63) 0.50 (0.22–1.15) 1.04 (0.51–2.13) Stage II (n = 559) 1.45 (0.00–640.44) 2.61 (0.05–149.18) 4.30 (0.23–78.98) 7.48 (0.93–60.38) Stage III (n = 1087) 0.35 (0.03–3.53) 0.68 (0.13–3.57) 1.19 (0.34–4.19) 2.11 (0.81–5.46) Squamous (n = 594) 0.22 (0.02–2.12) 0.51 (0.09–2.73) 0.96 (0.25–3.62) 1.85 (0.66–5.14) Non-squamous (n = 602) 1.04 (0.03–39.37) 1.69 (0.14–20.60) 2.59 (0.41–16.41) 4.18 (1.09–16.02) Discussion Given the inherent limitations of conventional efficacy endpoints in delineating when treatment benefits emerge, it is essential to discuss the clinical relevance of time-to-benefit as a complementary measure that captures the temporal dynamics of therapeutic efficacy. This study presented the first quantitative assessment of the TTB for perioperative immunotherapy by reconstructing individual patient data from published RCTs. The analysis revealed that the entire perioperative immunotherapy confered rapid clinical benefits in patients with resectable, driver mutation-negative NSCLC, wherein a treatment duration of 9 to 10 months was associated with a 10% improvement in EFS/PFS rates and a 1% increase in OS rates. Subgroup analyses indicated that patients with PD-L1 expression ≥ 1%, stage III, or squamous cell carcinoma histology experienced a more rapid clinical benefit from the entire perioperative immunotherapy than other subgroups. These findings demonstrated a quantifiable association between treatment duration and clinical benefit, while revealing differential time-to-initial-response across patient subgroups. This offered an evidence-based framework for determining the optimal duration of perioperative immunotherapy in clinical practice. Although existing guidelines have established immunotherapy combined with chemotherapy as the standard treatment for resectable NSCLC during the perioperative period, the optimal duration of immunotherapy remains unclear. Using TTB analysis, this study characterized the temporal dynamics of clinical benefit and demonstrated that the entire perioperative immunotherapy provides rapid and sustained efficacy, with meaningful effects emerging early. Approximately four months of therapy reduced the risk of recurrence or metastasis by 5%, while extending treatment to 9–10 months achieved a 10% improvement in EFS/PFS and a 1% gain in OS. In contrast, the simple postoperative adjuvant therapy alone required substantially longer exposure to yield comparable benefit, a 1% improvement in DFS after 2.47 months, nearly 2.4 times longer than that of the entire perioperative therapy (1.02 months). This disparity likely reflects the early immune activation triggered by neoadjuvant administration, which promotes tumor downstaging, micrometastasis clearance, and accelerated systemic response[ 10 , 26 , 27 ]. Clinically meaningful thresholds were reached within the first year (around 9–10 months), indicating that key milestones of measurable benefit occur during this period. Although continuing therapy beyond this period may confer incremental efficacy, it also increases toxicity, treatment burden, and cost[ 28 , 29 ]. Consistent evidence from other settings supports optimizing rather than prolonging immunotherapy duration: Bryant et al. reported that reducing durvalumab maintenance after chemoradiation did not compromise outcomes, and meta-analytic data have questioned the biological necessity of fixed one-year regimens[ 17 , 30 ]. While prospective validation remains warranted, converging quantitative and clinical evidence suggests that a 9–10-month course may represent a more balanced approach to efficacy, safety, and practicality than the conventional one-year regimen. Subgroup analyses revealed distinct temporal patterns of benefit. Patients with stage III NSCLC achieved shorter TTB than those with stage II disease, indicating earlier clinical benefit in tumors with greater immunologic activity. This may reflect a more inflamed microenvironment with enhanced angiogenesis and lymphocytic infiltration that amplifies neoadjuvant immune priming[ 31 ]. Similarly, squamous cell carcinoma showed shorter TTB than nonsquamous subtypes, likely due to its higher tumor mutational burden and chronic inflammatory state, aligning with prior evidence of improved outcomes in squamous histology[ 32 ]. Patients with PD-L1 ≥ 1% also derived earlier benefit, reflecting a pre-existing immune-activated phenotype and PD-1/PD-L1 pathway dependence[ 33 , 34 ]. Collectively, these results demonstrate that TTB complements conventional efficacy analyses by uncovering timing heterogeneity across subgroups. By quantifying when meaningful benefit emerges rather than only its magnitude, TTB provides an additional dimension for patient stratification and may inform individualized duration planning and adaptive perioperative immunotherapy strategies. Compared with traditional efficacy evaluation indicators, TTB has obvious advantages in decision support and is an important supplement to the efficacy evaluation system. Traditional efficacy endpoints offer limited guidance for perioperative immunotherapy, whereas TTB provides complementary clinical value by quantifying the temporal dynamics of treatment benefit. Unlike EFS or DFS, which report event-rate differences at predefined timepoints (e.g., a 10% improvement in 2-year EFS) without indicating when such differences arise, TTB estimates the duration required to achieve a specific magnitude of benefit from the onset of therapeutic effect. This temporal perspective enables earlier recognition of clinical benefit and supports timely treatment adjustments before disease progression necessitates regimen modification. Similarly, while OS requires extended follow-up and is influenced by post-recurrence therapies, TTB offers additional temporal insight into long-term outcomes, allowing earlier contextualization of survival trends. By translating abstract statistics (e.g., HR = 0.57) into tangible thresholds, such as a 2% reduction in recurrence risk after one month of therapy in patients with PD-L1 ≥ 1%, TTB enhances interpretability for both clinicians and patients, facilitating shared decision-making and dynamic monitoring of efficacy. Derived from validated endpoints including EFS, DFS, and OS, TTB remains grounded in clinically meaningful outcomes while adding a time-based interpretive layer. Beyond methodological refinement, TTB serves as a practical framework for optimizing perioperative immunotherapy strategies. It supports individualized decision-making in early-stage NSCLC, where long-term data remain limited, and its integration with toxicity and cost assessments may improve evidence-based duration planning, resource allocation, and future guideline development. This present study has several limitations that warrant consideration. This secondary analysis of published RCTs, lacking original safety data, precluded directly simultaneous assessment of "time-to-harm", despite the clinical need to balance benefit rapidity and toxicity timing. Future prospective studies should integrate temporal efficacy and safety analyses. In addition, TTB calculations utilized patient-level data reconstructed from KM curves. While methodological consistency was validated between original and digitized curves, minor estimation errors from the digitization process remain possible. Future studies using original patient data are needed for validation. Conclusion This study provides the first quantitative assessment of TTB for perioperative immunotherapy in resectable NSCLC, establishing a temporal framework linking treatment duration with clinical outcomes. TTB analysis revealed a near-linear accumulation of benefit, with meaningful efficacy achieved within approximately 9–10 months. These findings highlight TTB as a complementary endpoint, supporting evidence-based optimization of perioperative immunotherapy duration and individualized treatment planning. Abbreviations ARR Absolute risk reduction; CI Confidence interval; DFS Disease-free survival; EFS Event-free survival; HR Hazard ratio; ICIs Immune checkpoint inhibitors; KM Kaplan-Meier; NSCLC Non-small-cell lung cancer; OS Overall survival; PD-L1 Programmed death-ligand 1; PFS Progression-free survival; RCT Randomized controlled trial; TTB Time to benefit Declarations Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate The Xi’an Jiaotong University Health Science Centre institutional review board (IRB) approved this study. The patient consent requirement was waived by the IRB because this was a secondary data analysis based on publications. Funding This work was supported by grants from the Shaanxi Sanqin Scholars Innovation Team (No. 2021-No. 32), Innovation Capacity Support Program of Shaanxi Province (No. 23YXYJ0014), Major project of innovation Fund of Chinese Society of Clinical Oncology-MSD (Y-MSD2020-024), National Natural Science Foundation of China (NO. 82473732) and Key Research and Development Program of Shaanxi (No.2025SF-YBXM-338). Author Contribution YL, WB, and XJ contributed equally to this work. YL, CL, HG, and LJ had the idea for and designed the study. ML, YZ, XF, ZL, LH and LJ supervised the study. YL and WB did the statistical analysis, wrote the draft manuscript, and revised the manuscript. YL, XJ, JL, CL, HG, and LJ contributed to the acquisition, analysis, or interpretation of data, and revised the manuscript. The order of the co-first authors was assigned on the basis of their relative contributions to the study. All authors read and approved the final manuscript. Acknowledgement The authors thank the Department of Epidemiology and Health Statistics, School of Public Health in the Xi'an Jiaotong University Health Science Centre for assistance in statistical analyses. Data Availability The datasets used and/or analyzed during the current study are available fromthe corresponding author upon reasonable request. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a Cancer Journal For Clinicians 2024, 74(3):229-263. Travis WD, Brambilla E, Riely GJ: New pathologic classification of lung cancer: relevance for clinical practice and clinical trials. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology 2013, 31(8). Liu S-Y, Feng W-N, Wu Y-L: Immunotherapy in resectable NSCLC: Answering the question or questioning the answer? Cancer Cell 2024, 42(5):727-731. Liu X, Li X, Yang F: [Pattern of Recurrence and Metastasis after Radical Resection of Non-small Cell Lung Cancer]. Zhongguo Fei Ai Za Zhi = Chinese Journal of Lung Cancer 2022, 25(1):26-33. Cascone T, Awad MM, Spicer JD, He J, Lu S, Sepesi B, Tanaka F, Taube JM, Cornelissen R, Havel L et al : Perioperative Nivolumab in Resectable Lung Cancer. The New England Journal of Medicine 2024, 390(19):1756-1769. Felip E, Altorki N, Zhou C, Csőszi T, Vynnychenko I, Goloborodko O, Luft A, Akopov A, Martinez-Marti A, Kenmotsu H et al : Adjuvant atezolizumab after adjuvant chemotherapy in resected stage IB-IIIA non-small-cell lung cancer (IMpower010): a randomised, multicentre, open-label, phase 3 trial. Lancet (London, England) 2021, 398(10308):1344-1357. Heymach JV, Harpole D, Mitsudomi T, Taube JM, Galffy G, Hochmair M, Winder T, Zukov R, Garbaos G, Gao S et al : Perioperative Durvalumab for Resectable Non-Small-Cell Lung Cancer. The New England Journal of Medicine 2023, 389(18):1672-1684. O'Brien M, Paz-Ares L, Marreaud S, Dafni U, Oselin K, Havel L, Esteban E, Isla D, Martinez-Marti A, Faehling M et al : Pembrolizumab versus placebo as adjuvant therapy for completely resected stage IB-IIIA non-small-cell lung cancer (PEARLS/KEYNOTE-091): an interim analysis of a randomised, triple-blind, phase 3 trial. The Lancet Oncology 2022, 23(10):1274-1286. Wakelee H, Liberman M, Kato T, Tsuboi M, Lee S-H, Gao S, Chen K-N, Dooms C, Majem M, Eigendorff E et al : Perioperative Pembrolizumab for Early-Stage Non-Small-Cell Lung Cancer. The New England Journal of Medicine 2023, 389(6):491-503. Forde PM, Spicer J, Lu S, Provencio M, Mitsudomi T, Awad MM, Felip E, Broderick SR, Brahmer JR, Swanson SJ et al : Neoadjuvant Nivolumab plus Chemotherapy in Resectable Lung Cancer. The New England Journal of Medicine 2022, 386(21):1973-1985. Provencio M, Nadal E, González-Larriba JL, Martínez-Martí A, Bernabé R, Bosch-Barrera J, Casal-Rubio J, Calvo V, Insa A, Ponce S et al : Perioperative Nivolumab and Chemotherapy in Stage III Non-Small-Cell Lung Cancer. The New England Journal of Medicine 2023, 389(6):504-513. Yue D, Wang W, Liu H, Chen Q, Chen C, Liu L, Zhang P, Zhao G, Yang F, Han G et al : Perioperative tislelizumab plus neoadjuvant chemotherapy for patients with resectable non-small-cell lung cancer (RATIONALE-315): an interim analysis of a randomised clinical trial. The Lancet Respiratory Medicine 2024, 13(2):119-129. Waterhouse DM, Garon EB, Chandler J, McCleod M, Hussein M, Jotte R, Horn L, Daniel DB, Keogh G, Creelan B et al : Continuous Versus 1-Year Fixed-Duration Nivolumab in Previously Treated Advanced Non-Small-Cell Lung Cancer: CheckMate 153. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology 2020, 38(33):3863-3873. Thompson JA, Schneider BJ, Brahmer J, Andrews S, Armand P, Bhatia S, Budde LE, Costa L, Davies M, Dunnington D et al : NCCN Guidelines Insights: Management of Immunotherapy-Related Toxicities, Version 1.2020. Journal of the National Comprehensive Cancer Network : JNCCN 2020, 18(3):230-241. Mountzios G, Remon J, Hendriks LEL, García-Campelo R, Rolfo C, Van Schil P, Forde PM, Besse B, Subbiah V, Reck M et al : Immune-checkpoint inhibition for resectable non-small-cell lung cancer - opportunities and challenges. Nature Reviews Clinical Oncology 2023, 20(10):664-677. Desai A, Schwed K, Kalesinskas L, Yuan Q, Bryan J, Keane C, Fidyk E, Castellanos E, Cohen AB, Harrison K et al : Clinical Outcomes of Perioperative Immunotherapy in Resectable Non-Small Cell Lung Cancer. JAMA Network Open 2025, 8(6):e2517953. Bryant AK, Sankar K, Zhao L, Strohbehn GW, Elliott D, Moghanaki D, Kelley MJ, Ramnath N, Green MD: De-escalating adjuvant durvalumab treatment duration in stage III non-small cell lung cancer. European Journal of Cancer (Oxford, England : 1990) 2022, 171:55-63. Chen T-T: Milestone Survival: A Potential Intermediate Endpoint for Immune Checkpoint Inhibitors. Journal of the National Cancer Institute 2015, 107(9). West HJ, Kim JY: Rapid Advances in Resectable Non-Small Cell Lung Cancer: A Narrative Review. JAMA Oncology 2024, 10(2):249-255. Anagnostou V, Yarchoan M, Hansen AR, Wang H, Verde F, Sharon E, Collyar D, Chow LQM, Forde PM: Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity. Clinical Cancer Research : an Official Journal of the American Association For Cancer Research 2017, 23(17):4959-4969. Lee SJ, Boscardin WJ, Stijacic-Cenzer I, Conell-Price J, O'Brien S, Walter LC: Time lag to benefit after screening for breast and colorectal cancer: meta-analysis of survival data from the United States, Sweden, United Kingdom, and Denmark. BMJ (Clinical Research ed) 2013, 346:e8441. Deardorff WJ, Cenzer I, Nguyen B, Lee SJ: Time to Benefit of Bisphosphonate Therapy for the Prevention of Fractures Among Postmenopausal Women With Osteoporosis: A Meta-analysis of Randomized Clinical Trials. JAMA Intern Med 2022, 182(1):33-41. Chen K, Nie Z, Shi R, Yu D, Wang Q, Shao F, Wu G, Wu Z, Chen T, Li C: Time to Benefit of Sodium-Glucose Cotransporter-2 Inhibitors Among Patients With Heart Failure. JAMA Network Open 2023, 6(8):e2330754. Chen T, Shao F, Chen K, Wang Y, Wu Z, Wang Y, Gao Y, Cornelius V, Li C, Jiang Z: Time to Clinical Benefit of Intensive Blood Pressure Lowering in Patients 60 Years and Older With Hypertension: A Secondary Analysis of Randomized Clinical Trials. JAMA Intern Med 2022, 182(6):660-667. Wang J, Hou X, Peng L, Dang Y, Xu X, Wei C, Guo R, Song W, He C, Jiang J et al : Time to Benefit of Androgen Deprivation Therapy in Patients With Localized Prostate Cancer Undergoing Radiotherapy. JCO Precision Oncology 2025, 9:e2400605. Hui Z, Ren Y, Zhang D, Chen Y, Yu W, Cao J, Liu L, Wang T, Xiao S, Zheng L et al : PD-1 blockade potentiates neoadjuvant chemotherapy in NSCLC via increasing CD127+ and KLRG1+ CD8 T cells. NPJ Precision Oncology 2023, 7(1):48. Schuler M, Cuppens K, Plönes T, Wiesweg M, Du Pont B, Hegedus B, Köster J, Mairinger F, Darwiche K, Paschen A et al : Neoadjuvant nivolumab with or without relatlimab in resectable non-small-cell lung cancer: a randomized phase 2 trial. Nature Medicine 2024, 30(6):1602-1611. Sun L, Bleiberg B, Hwang W-T, Marmarelis ME, Langer CJ, Singh A, Cohen RB, Mamtani R, Aggarwal C: Association Between Duration of Immunotherapy and Overall Survival in Advanced Non-Small Cell Lung Cancer. JAMA Oncology 2023, 9(8):1075-1082. Ghisoni E, Wicky A, Bouchaab H, Imbimbo M, Delyon J, Gautron Moura B, Gérard CL, Latifyan S, Özdemir BC, Caikovski M et al : Late-onset and long-lasting immune-related adverse events from immune checkpoint-inhibitors: An overlooked aspect in immunotherapy. European Journal of Cancer (Oxford, England : 1990) 2021, 149:153-164. Nuccio A, Viscardi G, Salomone F, Servetto A, Venanzi FM, Riva ST, Oresti S, Ogliari FR, Viganò M, Bulotta A et al : Systematic review and meta-analysis of immune checkpoint inhibitors as single agent or in combination with chemotherapy in early-stage non-small cell lung cancer: Impact of clinicopathological factors and indirect comparison between treatment strategies. European Journal of Cancer (Oxford, England : 1990) 2023, 195:113404. Topalian SL, Forde PM, Emens LA, Yarchoan M, Smith KN, Pardoll DM: Neoadjuvant immune checkpoint blockade: A window of opportunity to advance cancer immunotherapy. Cancer Cell 2023, 41(9):1551-1566. Parra ER, Zhang J, Duose DY, Gonzalez-Kozlova E, Redman MW, Chen H, Manyam GC, Kumar G, Zhang J, Song X et al : Multi-omics Analysis Reveals Immune Features Associated with Immunotherapy Benefit in Patients with Squamous Cell Lung Cancer from Phase III Lung-MAP S1400I Trial. Clinical Cancer Research : an Official Journal of the American Association For Cancer Research 2024, 30(8):1655-1668. Mo D-C, Huang J-F, Lin P, Huang S-X, Wang H-L, Luo P-H, Liang X-J: The role of PD-L1 in patients with non-small cell lung cancer receiving neoadjuvant immune checkpoint inhibitor plus chemotherapy: a meta-analysis. Scientific Reports 2024, 14(1):26200. Spicer JD, Cascone T, Wynes MW, Ahn M-J, Dacic S, Felip E, Forde PM, Higgins KA, Kris MG, Mitsudomi T et al : Neoadjuvant and Adjuvant Treatments for Early Stage Resectable NSCLC: Consensus Recommendations From the International Association for the Study of Lung Cancer. Journal of Thoracic Oncology : Official Publication of the International Association For the Study of Lung Cancer 2024, 19(10):1373-1414. Additional Declarations No competing interests reported. Supplementary Files AdditionalFiles.pdf Description of additional files Search Strategy of PubMed for RCT.txt Additional File Figure S1.pdf Flow chart of the search, selection, and inclusion of the studies. Additional File Figure S2.pdfReconstruct Kaplan-Meier curve and original Kaplan-Meier curve (event-free survival/progression-free survival). (A)Original Kaplan-Meier curve (AEGEAN), (B)Reconstruct Kaplan-Meier curve (AEGEAN), (C)Original Kaplan-Meier curve (Neotorch), (D)Reconstruct Kaplan-Meier curve (Neotorch), (E)Original Kaplan-Meier curve (KEYNOTE-671), (F)Reconstruct Kaplan-Meier curve (KEYNOTE-671), (G)Original Kaplan-Meier curve (CheckMate-77T), (H)Reconstruct Kaplan-Meier curve (CheckMate-77T), (I)Original Kaplan-Meier curve (NADIM II), (J)Reconstruct Kaplan-Meier curve (NADIM II), (K)Original Kaplan-Meier curve (RATIONALE-315), (L)Reconstruct Kaplan-Meier curve (RATIONALE-315). Additional File Figure S3.pdfReconstruct Kaplan-Meier curve and original Kaplan-Meier curve (overall survival). (A)Original Kaplan-Meier curve (AEGEAN), (B)Reconstruct Kaplan-Meier curve (AEGEAN), (C)Original Kaplan-Meier curve (Neotorch), (D)Reconstruct Kaplan-Meier curve (Neotorch), (E)Original Kaplan-Meier curve (NADIM II), (F)Reconstruct Kaplan-Meier curve (NADIM II), (G)Original Kaplan-Meier curve (KEYNOTE-671), (H)Reconstruct Kaplan-Meier curve (KEYNOTE-671), (I)Original Kaplan-Meier curve (ATIONALE-315), (J)Reconstruct Kaplan-Meier curve (ATIONALE-315). Additional File Figure S4.pdfReconstruct Kaplan-Meier curve and original Kaplan-Meier curve (disease-free survival). (A)Original Kaplan-Meier curve (IMpower010), (B)Reconstruct Kaplan-Meier curve (IMpower010), (C)Original Kaplan-Meier curve (KEYNOTE-091), (D)Reconstruct Kaplan-Meier curve (KEYNOTE-091). Additional File Figure S5.pdfCumulative Risk and Hazard Ratio (HR) of Primary Outcome. (A)Kaplan-Meier plots of event-free survival of perioperative immunotherapy combined with chemotherapy vs chemotherapy alone; (B)Kaplan-Meier plots of overall survival of perioperative immunotherapy combined with chemotherapy vs chemotherapy alone; (C)Kaplan-Meier plots of disease-free survival of adjuvant immunotherapy combined with chemotherapy vs chemotherapy alone; (D)Kaplan-Meier plots of overall survival of adjuvant immunotherapy combined with chemotherapy vs chemotherapy alone. Additional File Figure S6.pdfMeta-analysis at the Study Level of Primary Outcome. (A)Meta-analysis of event-free survival of perioperative immunotherapy combined with chemotherapy vs chemotherapy alone; (B)Meta-analysis of overall survival of perioperative immunotherapy combined with chemotherapy vs chemotherapy alone. Additional File Table S1.xls Characteristics of Included Studies. Additional File Table S2.xls Risk of Bias Assessment of included trials. Additional File Table S3.xls Definition for the primary outcome for each include trial. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 08 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor invited by journal 05 Feb, 2026 Editor assigned by journal 04 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 04 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8788627","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590733729,"identity":"2d7591b2-36aa-4933-88a0-a14bf2bcde72","order_by":0,"name":"Yongyan Lu","email":"","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yongyan","middleName":"","lastName":"Lu","suffix":""},{"id":590733730,"identity":"64b38df9-879b-431c-95f8-3f067b3e1d46","order_by":1,"name":"Wenming Bian","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Wenming","middleName":"","lastName":"Bian","suffix":""},{"id":590733731,"identity":"29c5f7e3-a4ff-4977-b886-96b3329c1a63","order_by":2,"name":"Xiaohui Jia","email":"","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Jia","suffix":""},{"id":590733732,"identity":"5a4ebf9a-d84a-4118-b661-4471a430ced2","order_by":3,"name":"Jingyi Li","email":"","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Li","suffix":""},{"id":590733733,"identity":"b332252e-87c0-4d63-b63b-33a893fdac22","order_by":4,"name":"Mengjie Liu","email":"","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Mengjie","middleName":"","lastName":"Liu","suffix":""},{"id":590733734,"identity":"8140aa59-89d2-46a4-9fdc-ad212f11d44a","order_by":5,"name":"Ying Zan","email":"","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zan","suffix":""},{"id":590733735,"identity":"06e22b3e-7e3d-4be0-a262-36a8aa3df21c","order_by":6,"name":"Xiaolan Fu","email":"","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolan","middleName":"","lastName":"Fu","suffix":""},{"id":590733736,"identity":"d1b92afe-abe8-4825-8c77-6b56ff76bdca","order_by":7,"name":"Zhiyan Liu","email":"","orcid":"","institution":"Third Hospital of Xi'an","correspondingAuthor":false,"prefix":"","firstName":"Zhiyan","middleName":"","lastName":"Liu","suffix":""},{"id":590733737,"identity":"cfa3b48b-f56d-48db-84cb-6d818788ecba","order_by":8,"name":"Lanxuan Huang","email":"","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Lanxuan","middleName":"","lastName":"Huang","suffix":""},{"id":590733738,"identity":"815b6074-d3e9-4052-8c5c-ee60ff41f042","order_by":9,"name":"Chao Li","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Li","suffix":""},{"id":590733739,"identity":"f69e5ff4-e778-42aa-9e7b-d32470808ff1","order_by":10,"name":"Hui Guo","email":"","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Guo","suffix":""},{"id":590733740,"identity":"0dfcfe3d-abf5-4ee7-9ccd-c0ee1c211472","order_by":11,"name":"Lili Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACxmYQWSAhx9jefPBBQkUNsVoMJIyZe44lGzw4c4xYuwwYEttn5JhJPmxhJqyYuZ352cMvBhbGvEAtFYkNbAz87d0JBBzGZm4sYyAhJ9nzrOxG4g4ZBokzZzcQ8ouZtATQL4btydtuJJ5hA/orl5AW9m8gLYn7DySYFSS2MROjhcdM8gNQS2NHihkDsVrKpEGBzAgMZImEM8d4CPrFsP/4NskfFXXgqPz4o6JGjr+9l4CWBmBA8yAJ8OBSCQfyIMf9IKhsFIyCUTAKRjQAAFCGRzPMxdtrAAAAAElFTkSuQmCC","orcid":"","institution":"Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Lili","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-02-04 16:10:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8788627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8788627/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102982064,"identity":"45538e15-9330-4dc2-9bda-c1bfc1b00818","added_by":"auto","created_at":"2026-02-19 09:12:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86655,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of EFS-related risk events in the perioperative immunotherapy and chemotherapy groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8788627/v1/32010561434691463ab77e87.png"},{"id":102982026,"identity":"69a8f257-0830-461f-b216-acbe39bae2c7","added_by":"auto","created_at":"2026-02-19 09:12:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92890,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of OS-related risk events in the perioperative immunotherapy and chemotherapy groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8788627/v1/b9f42e73841b85bb5f32d0b1.png"},{"id":102981981,"identity":"9905ce02-f8c7-4291-baf5-da2cb44ef0fa","added_by":"auto","created_at":"2026-02-19 09:11:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89191,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of DFS-related risk events in the adjuvant immunotherapy and chemotherapy groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8788627/v1/a4538345d2c5a43108d9f77b.png"},{"id":102982096,"identity":"6e1fb0bf-7060-468f-ab31-3521d32e85f1","added_by":"auto","created_at":"2026-02-19 09:12:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125084,"visible":true,"origin":"","legend":"\u003cp\u003eIntegration of perioperative immunotherapy in subgroups.\u003c/p\u003e\n\u003cp\u003e(A) squamous, (B) non-squamous, (C) stage II, (D) stage III, (E) PD-L1 ≥1%, and (F) PD-L1 \u0026lt;1%.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8788627/v1/8d55fcabdf3da9158f7e5aa8.png"},{"id":103049652,"identity":"9e5df001-7a60-490d-901c-af9a3b8980fe","added_by":"auto","created_at":"2026-02-20 07:44:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1005694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8788627/v1/7ae9fa24-2e20-475b-81c0-3c3f379a0fc4.pdf"},{"id":102982031,"identity":"3f5ac70f-985f-46b2-9dbe-8fc80d51b921","added_by":"auto","created_at":"2026-02-19 09:12:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10683243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDescription of additional files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch Strategy of PubMed for RCT.txt\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Figure S1.pdf\u003c/strong\u003e Flow chart of the search, selection, and inclusion of the studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Figure S2.pdf\u003c/strong\u003eReconstruct Kaplan-Meier curve and original Kaplan-Meier curve (event-free survival/progression-free survival).\u003c/p\u003e\n\u003cp\u003e(A)Original Kaplan-Meier curve (AEGEAN), (B)Reconstruct Kaplan-Meier curve (AEGEAN), (C)Original Kaplan-Meier curve (Neotorch), (D)Reconstruct Kaplan-Meier curve (Neotorch), (E)Original Kaplan-Meier curve (KEYNOTE-671), (F)Reconstruct Kaplan-Meier curve (KEYNOTE-671), (G)Original Kaplan-Meier curve (CheckMate-77T), (H)Reconstruct Kaplan-Meier curve (CheckMate-77T), (I)Original Kaplan-Meier curve (NADIM II), (J)Reconstruct Kaplan-Meier curve (NADIM II), (K)Original Kaplan-Meier curve (RATIONALE-315), (L)Reconstruct Kaplan-Meier curve (RATIONALE-315).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Figure S3.pdf\u003c/strong\u003eReconstruct Kaplan-Meier curve and original Kaplan-Meier curve (overall survival).\u003c/p\u003e\n\u003cp\u003e(A)Original Kaplan-Meier curve (AEGEAN), (B)Reconstruct Kaplan-Meier curve (AEGEAN), (C)Original Kaplan-Meier curve (Neotorch), (D)Reconstruct Kaplan-Meier curve (Neotorch), (E)Original Kaplan-Meier curve (NADIM II), (F)Reconstruct Kaplan-Meier curve (NADIM II), (G)Original Kaplan-Meier curve (KEYNOTE-671), (H)Reconstruct Kaplan-Meier curve (KEYNOTE-671), (I)Original Kaplan-Meier curve (ATIONALE-315), (J)Reconstruct Kaplan-Meier curve (ATIONALE-315).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Figure S4.pdf\u003c/strong\u003eReconstruct Kaplan-Meier curve and original Kaplan-Meier curve (disease-free survival).\u003c/p\u003e\n\u003cp\u003e(A)Original Kaplan-Meier curve (IMpower010), (B)Reconstruct Kaplan-Meier curve (IMpower010), (C)Original Kaplan-Meier curve (KEYNOTE-091), (D)Reconstruct Kaplan-Meier curve (KEYNOTE-091).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Figure S5.pdf\u003c/strong\u003eCumulative Risk and Hazard Ratio (HR) of Primary Outcome.\u003c/p\u003e\n\u003cp\u003e(A)Kaplan-Meier plots of event-free survival of perioperative immunotherapy combined with chemotherapy vs chemotherapy alone; (B)Kaplan-Meier plots of overall survival of perioperative immunotherapy combined with chemotherapy vs chemotherapy alone; (C)Kaplan-Meier plots of disease-free survival of adjuvant immunotherapy combined with chemotherapy vs chemotherapy alone; (D)Kaplan-Meier plots of overall survival of adjuvant immunotherapy combined with chemotherapy vs chemotherapy alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Figure S6.pdf\u003c/strong\u003eMeta-analysis at the Study Level of Primary Outcome.\u003c/p\u003e\n\u003cp\u003e(A)Meta-analysis of event-free survival of perioperative immunotherapy combined with chemotherapy vs chemotherapy alone; (B)Meta-analysis of overall survival of perioperative immunotherapy combined with chemotherapy vs chemotherapy alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Table S1.xls \u003c/strong\u003eCharacteristics of Included Studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Table S2.xls \u003c/strong\u003eRisk of Bias Assessment of included trials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File Table S3.xls \u003c/strong\u003eDefinition for the primary outcome for each include trial.\u003c/p\u003e","description":"","filename":"AdditionalFiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8788627/v1/5e30dd8addb03ed5df9f33db.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time to Benefit of Perioperative Immunotherapy Among Patients With Resectable Non–Small-Cell Lung Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer represents the most common cause of cancer-related deaths, with non-small-cell lung cancer (NSCLC) accounting for approximately 85% of cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While surgical resection remains the standard treatment for early-stage NSCLC management, 30\u0026ndash;55% of patients will relapse and develop metastases post-surgery [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Multiple randomized controlled trials (RCTs) have confirmed that, in patients with resected NSCLC, perioperative therapy (neoadjuvant followed by adjuvant) significantly reduces tumor relapse. The addition of immunotherapy is associated with improved outcomes in patients with driver gene-negative NSCLC compared to adjuvant platinum-based doublet chemotherapy alone, leading to guidelines recommending it as a first-line therapy [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While perioperative immune checkpoint inhibitors (ICIs) are effective in early-stage NSCLC, their prolonged use may lead to increased medical and economic toxicity, highlighting the need to determine the optimal treatment duration to balance therapeutic efficacy with potential risks [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the optimal length of adjuvant therapy has not yet been determined.\u003c/p\u003e \u003cp\u003eAlthough most clinical trials adopt a fixed one-year adjuvant regimen, evidence supporting this duration, particularly analyses of the temporal dynamics of treatment benefits, remains limited[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Conventional efficacy endpoints provide important measures of therapeutic effect but offer limited insight into how benefits emerge and evolve over time[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Disease-free survival (DFS) in the adjuvant setting and event-free survival (EFS) in the neoadjuvant or the entire perioperative setting are influenced by the timing and frequency of follow-up assessments, which may obscure the precise onset of clinical benefit. Overall survival (OS), while the most definitive endpoint, requires prolonged observation and is often confounded by subsequent post-recurrence therapies, making it difficult to isolate the direct impact of perioperative treatment. Objective response rate (ORR) reflects early tumor shrinkage but lacks correlation with durable outcomes and does not capture benefit among patients with stable disease. Collectively, these measures describe the magnitude of efficacy but not its temporal trajectory. Their inherent dependence on assessment schedules, data maturity, and subsequent interventions constrains their ability to delineate when clinically meaningful benefits begin or the duration of exposure required to achieve them. Recognizing these structural limitations underscores the importance of developing complementary analytical frameworks that integrate the time-dependent nature of treatment efficacy, thereby enhancing the temporal interpretability of established endpoints and guiding evidence-based optimization of therapy duration in perioperative NSCLC.\u003c/p\u003e \u003cp\u003eTime to Benefit (TTB) represents a complementary analytical framework that quantifies the time from treatment initiation to the attainment of a predefined clinical benefit, capturing the precise moment at which survival curves diverge, signaling the onset of therapeutic effect. From a methodological perspective, TTB advances beyond conventional endpoints through the integration of two fundamental components: first, it estimates actual benefit dynamics by fitting a Weibull survival distribution to individual-level participant data derived from published RCTs; second, it establishes a well-defined absolute risk reduction (ARR) threshold via Monte Carlo simulation, thereby translating the abstract notion of therapeutic benefit into a quantifiable clinical benchmark. By integrating temporal dynamics with explicit clinical benefit thresholds, TTB enables a dual-dimensional assessment of both the speed and magnitude of treatment effect [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our prior research has assessed TTB across various clinical settings, including the use of SGLT-2 inhibitors in heart failure patients, intensive blood pressure management in the elderly, and androgen deprivation therapy (ADT) for localized prostate cancer [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Exploring the TTB of perioperative immunotherapy offers a more nuanced evaluation of the time required to achieve therapeutic benefit, thereby addressing the treatment duration determination imperatives in adjuvant immunotherapy.​\u003c/p\u003e \u003cp\u003eThis study aim to systematically evaluate the TTB of perioperative immunotherapy in patients with resectable early-stage NSCLC for the first time. It determined the treatment duration required to achieve clinically meaningful effects and contribute to identifying patient subsets that may require extended therapy, thereby providing precise, evidence-based guidance for treatment duration decisions.\u003c/p\u003e\n\u003ch3\u003eStudy Design and Methods\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThis comparative effectiveness research study used secondary data sets based on randomized clinical trials. To ensure the recent results could accurately reflect effectiveness of perioperative immunotherapy, we followed the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) reporting guideline and addressed issues of framing the research question and reporting and interpreting findings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Source and Searches\u003c/h3\u003e\n\u003cp\u003eThis study was performed based on up-to-date published research. To ensure the completeness of including all perioperative immunotherapy, we did a systematic review of the literature. Two independent reviewers (Y.Y.L. and W.M.B.) searched relevant RCTs in PubMed that were published until May, 1, 2025. Both reviewers screened titles and abstracts, followed by full texts, and a third reviewer (X.H.J.) cross-checked the screening decision.\u003c/p\u003e \u003cp\u003eThe search strategy is illustrated in the eAppendix in Additional File following the previous systematic review and meta-analysis. In the present analysis, we only included RCTs comparing perioperative immunotherapy vs chemotherapy/placebo on EFS, DFS or OS among patients with resectable NSCLC.\u003c/p\u003e \u003cp\u003eTo serve the purpose of calculating TTB, we included studies having vector Kaplan-Meier (KM) curves, which enabled us to reconstruct individual time-to-event data from the number of patients at risk and the KM graph. A total of 151 articles were initially identified through a PubMed search. Of these, 106 were excluded for the following reasons: non-human studies (2 articles), meta-analyses or reviews (30 articles), and other types (such as letters, comments) (74 articles). Among the remaining 45 articles, we excluded RCTs of patients who did not receive adjuvant immunotherapy (24 articles), articles with post hoc or secondary analyses (2 articles), and articles regarding study protocols or trial baselines (11 articles) ultimately identifying 8 RCTs for inclusion in the analysis. The flow chart was shown in Additional File \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. In subsequent studies, we included the results of the latest randomized clinical trials that had been published recently.\u003c/p\u003e\n\u003ch3\u003eData Reconstruction\u003c/h3\u003e\n\u003cp\u003eTo estimate a pooled time lag to benefit across trials, we extracted and reconstruction survival curves for the control and intervention groups for each study before combining the survival curves in our meta-analysis to obtain pooled estimates of ARR. We reconstructed individual time-to-event data in line with our previous publication through a 2-stage process. First, the quality data coordinates (survival probability and time) were extracted from KM curves by DigitizeIt software version 2.5 following the instructions from Liu and Lee. In this stage, we also followed the recommendation when extracting data points. For example, extract as many points as possible and make sure the data points extracted are evenly distributed on the KM curves. Second, a Stata function (ipdfc command) developed by Wei and Royston was used to rebuild the individual data based on the aforementioned extracted raw data of time and survival probability. The algorithm underpinning the ipdfc command has been successfully used in our previous study, and basically aimed to estimate the number of censorings, the number of events, the censoring time, and the event time. Visual comparisons between the reconstructed and original survival curves (\u003cb\u003eAdditional File Figure S2-4\u003c/b\u003e) confirmed the consistency of the reconstructed individual patient data with the original studies. We found that this algorithm recovered individual participant data from published trials with a high degree of accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eThe characteristics of included studies were summarized from publications. Primary endpoints were EFS/progression-free survival (PFS) and DFS. EFS/PFS was the time from randomization to any of: progression precluding surgery, abandonment of surgery for unresectability, postoperative recurrence/progression, or death from any cause. DFS was the time from curative-intent surgery to recurrence or death from any cause. The secondary endpoint was OS, measured from randomization to death from any cause; survivors were censored at last contact. The cumulative rates of primary outcome at each time point in the placebo and perioperative immunotherapy group from the pooled trials were estimated using the KM curve. The hazard ratios (HRs) and 95% confidence interval (95% CI) were calculated using the stratified Cox proportional hazards model to adjust for the clustering of patients from the same trial. We also calculated pooled HRs and 95% CI using study-level meta-analysis to further estimate the efficacy of perioperative immunotherapy. Meanwhile, heterogeneity between included studies was evaluated using the \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e tests.\u003c/p\u003e \u003cp\u003eWe fitted Weibull survival curves to estimate the time to specific ARR thresholds (ie, 0.002, 0.005, 0.01, 0.02) using the conventional frequentist method to calculate the TTB and Monte Carlo simulations to derive its 95% CI. We further presented TTB estimations by the following characteristics: individual trials; participants with different types of tumor histology (squamous or nonsquamous); participants with different tumor staging (stage II or III) or different tumor PD-L1 expression (PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;1% or \u0026ge;\u0026thinsp;1%). Statistical analysis was performed from June to August 2025. The TTB calculation was conducted in R version 3.4.0 (R Project for Statistical Computing), and other analyses in this study were performed in Stata version 15.0 (StataCorp).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe study characteristics were summarized in (\u003cb\u003eAdditional File Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). All trials were assessed as high quality, with a low risk of bias across the 8 trials (\u003cb\u003eAdditional File Table S2\u003c/b\u003e). A total of 5,123 participants were included in these trials. Preoperative neoadjuvant treatment cycles ranged from 3 to 4 cycles (21 days each), typically in combination with concurrent chemotherapy. Postoperative adjuvant therapy generally lasted for 1 year. A summary of the study characteristics was provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For detailed information on the treatment regimens, please refer to the supplementary data (\u003cb\u003eAdditional File Table S3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime to benefit (months) at specific thresholds of absolute survival benefits across treatment strategies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eAbsolute risk reduction threshold (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerioperative immunotherapy (EFS/PFS, n\u0026thinsp;=\u0026thinsp;2941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.08\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.22\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.48\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.77 (1.05\u0026ndash;2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.29 (2.95\u0026ndash;6.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.01 (6.70\u0026ndash;12.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant immunotherapy (DFS, n\u0026thinsp;=\u0026thinsp;2182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.04\u0026ndash;5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.18\u0026ndash;8.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47 (0.55\u0026ndash;11.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.24 (1.65\u0026ndash;16.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.97 (6.70\u0026ndash;38.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerioperative immunotherapy (OS, n\u0026thinsp;=\u0026thinsp;2480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.59 (1.66\u0026ndash;26.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.33 (3.03\u0026ndash;22.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.67 (5.02\u0026ndash;22.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.51 (8.39\u0026ndash;25.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.05 (16.27\u0026ndash;35.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.57 (25.64\u0026ndash;61.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant immunotherapy (OS, n\u0026thinsp;=\u0026thinsp;2182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.18 (9.53\u0026ndash;95.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.89 (12.46\u0026ndash;92.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.42 (16.48\u0026ndash;94.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.29 (22.42\u0026ndash;108.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.31 (31.58\u0026ndash;189.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eEFS, event-free survival; PFS, progression-free survival; OS, overall survival; NR, not reported.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary TTB Analysis Outcomes\u003c/h3\u003e\n\u003cp\u003eAcross pooled trials, the entire perioperative immunotherapy plus chemotherapy significantly improved EFS/PFS (HR 0.57, 95% CI 0.50\u0026ndash;0.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OS (HR 0.75, 95% CI 0.64\u0026ndash;0.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding was further confirmed by a meta-analysis at the study level (\u003cb\u003eAdditional File Figure S5-6\u003c/b\u003e). TTB modeling showed an early and near-linear accrual of benefit: 1.02 months of therapy corresponded to a 1% absolute reduction in EFS-related events (95% CI 0.48\u0026ndash;2.21), 4.29 months to a 5% reduction (95% CI 2.95\u0026ndash;6.24), and \u0026lt;\u0026thinsp;1 year (9.01 months; 95% CI 6.70\u0026ndash;12.12) to a 10% reduction (EFS/PFS data were derived from the AEGEAN, Neotorch, NADIM II, KEYNOTE-671, CheckMate-77T and RATIONALE-315 trials) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Using OS as the endpoint, benefit became statistically apparent after 6.59 months (95% CI 1.66\u0026ndash;26.25), with 8.33 months corresponding to a 0.5% absolute mortality reduction (95% CI 3.03\u0026ndash;22.94), 10.67 months to a 1% reduction (95% CI 5.02\u0026ndash;22.68), and 14.51 months to a 2% reduction (95% CI 8.39\u0026ndash;25.08) (the entire perioperative OS data were derived from the AEGEAN, Neotorch, NADIM II, KEYNOTE-671 and RATIONALE-315 trials) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the simple postoperative adjuvant immunotherapy achieved a smaller effect size and required longer exposure to reach comparable thresholds: DFS improved versus control (HR 0.79, 95% CI 0.69\u0026ndash;0.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), yet a 1% absolute reduction in DFS-related events required 2.47 months (95% CI 0.55\u0026ndash;11.12), a 2% reduction 5.24 months (95% CI 1.65\u0026ndash;16.68), and a 5% reduction 15.97 months (95% CI 6.70\u0026ndash;38.10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); no OS advantage was observed with adjuvant immunotherapy alone during available follow-up (HR 0.94, 95% CI 0.78\u0026ndash;1.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.502) (\u003cb\u003eAdditional File Figure S5\u003c/b\u003e) (adjuvant DFS and OS data were derived from the IMpower010 and KEYNOTE-091 trials). Taken together, these findings indicate that the entire perioperative immunotherapy delivers earlier onset and greater magnitude of clinically meaningful benefit, achieving EFS and OS thresholds within the first year, whereas adjuvant-only strategies require longer treatment durations and have not demonstrated a survival advantage (1% absolute reduction in EFS-related events: 9.01 months [95% CI, 6.70\u0026ndash;12.12]; DFS-related events: 2.47 months [95% CI, 0.55\u0026ndash;11.12]), underscoring the temporal and quantitative superiority of the entire perioperative approach.\u003c/p\u003e\n\u003ch3\u003eSubgroup analysis of TTB\u003c/h3\u003e\n\u003cp\u003eFurther subgroup TTB analyses were conducted based on histological subtypes, tumor stages and PD-L1 expression levels (subgroup data were derived from the AEGEAN, CheckMate-77T and RATIONALE-315 trials), and the results indicated significant differences in TTB among patients with different clinical characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Regarding histological subtypes, TTB results suggested that patients with squamous lung cancer experience a more rapid therapeutic benefit from the entire perioperative immunotherapy than non-squamous lung cancer. Using a 1% reduction in EFS-related risk events as a threshold, non-squamous carcinoma patients required twice the treatment duration of squamous cell carcinoma patients (squamous: 0.96 months [95% CI, 0.25\u0026ndash;3.62]; non-squamous: 2.59 months [95% CI, 0.41\u0026ndash;16.41]). It supported prioritizing the entire perioperative immunotherapy for squamous cell carcinoma patients to derive benefit earlier. Among patients with different disease stages, those with stage III cancer exhibited a markedly shorter TTB than stage II patients. Specifically, a 1% reduction in EFS-related risk events was achieved in 1.19 months (95% CI 0.34\u0026ndash;4.19) in the stage III cohort, compared to 4.3 months in the stage II group (95% CI 0.23\u0026ndash;78.98). TTB outcomes corresponding to additional ARR thresholds (e.g., 2%, 5%) consistently demonstrate that stage III patients exhibit accelerated clinical benefit. Furthermore, PD-L1 expression levels represent a critical factor influencing TTB. Patients with PD-L1 expression\u0026thinsp;\u0026ge;\u0026thinsp;1% exhibited a significantly shorter TTB compared to those with PD-L1 expression\u0026thinsp;\u0026lt;\u0026thinsp;1%. Remarkably, the former group attained a 2% reduction in EFS-related risk events after only one month of treatment (95% CI 0.51\u0026ndash;2.13), representing the most rapid response observed across all subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis of time to benefit (months) at specific absolute risk reduction thresholds\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAbsolute risk reduction threshold(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u0026thinsp;\u0026lt;\u0026thinsp;1% (n\u0026thinsp;=\u0026thinsp;572)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.02\u0026ndash;8.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.09\u0026ndash;9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64 (0.27\u0026ndash;10.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.95 (0.71\u0026ndash;12.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u0026thinsp;\u0026ge;\u0026thinsp;1% (n\u0026thinsp;=\u0026thinsp;1014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10 (0.03\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.10\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.22\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04 (0.51\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II (n\u0026thinsp;=\u0026thinsp;559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45 (0.00\u0026ndash;640.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61 (0.05\u0026ndash;149.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.30 (0.23\u0026ndash;78.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.48 (0.93\u0026ndash;60.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III (n\u0026thinsp;=\u0026thinsp;1087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35 (0.03\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.13\u0026ndash;3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (0.34\u0026ndash;4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11 (0.81\u0026ndash;5.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous (n\u0026thinsp;=\u0026thinsp;594)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.02\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.09\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.25\u0026ndash;3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85 (0.66\u0026ndash;5.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-squamous (n\u0026thinsp;=\u0026thinsp;602)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.03\u0026ndash;39.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69 (0.14\u0026ndash;20.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59 (0.41\u0026ndash;16.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.18 (1.09\u0026ndash;16.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGiven the inherent limitations of conventional efficacy endpoints in delineating when treatment benefits emerge, it is essential to discuss the clinical relevance of time-to-benefit as a complementary measure that captures the temporal dynamics of therapeutic efficacy. This study presented the first quantitative assessment of the TTB for perioperative immunotherapy by reconstructing individual patient data from published RCTs. The analysis revealed that the entire perioperative immunotherapy confered rapid clinical benefits in patients with resectable, driver mutation-negative NSCLC, wherein a treatment duration of 9 to 10 months was associated with a 10% improvement in EFS/PFS rates and a 1% increase in OS rates. Subgroup analyses indicated that patients with PD-L1 expression\u0026thinsp;\u0026ge;\u0026thinsp;1%, stage III, or squamous cell carcinoma histology experienced a more rapid clinical benefit from the entire perioperative immunotherapy than other subgroups. These findings demonstrated a quantifiable association between treatment duration and clinical benefit, while revealing differential time-to-initial-response across patient subgroups. This offered an evidence-based framework for determining the optimal duration of perioperative immunotherapy in clinical practice.\u003c/p\u003e \u003cp\u003eAlthough existing guidelines have established immunotherapy combined with chemotherapy as the standard treatment for resectable NSCLC during the perioperative period, the optimal duration of immunotherapy remains unclear. Using TTB analysis, this study characterized the temporal dynamics of clinical benefit and demonstrated that the entire perioperative immunotherapy provides rapid and sustained efficacy, with meaningful effects emerging early. Approximately four months of therapy reduced the risk of recurrence or metastasis by 5%, while extending treatment to 9\u0026ndash;10 months achieved a 10% improvement in EFS/PFS and a 1% gain in OS. In contrast, the simple postoperative adjuvant therapy alone required substantially longer exposure to yield comparable benefit, a 1% improvement in DFS after 2.47 months, nearly 2.4 times longer than that of the entire perioperative therapy (1.02 months). This disparity likely reflects the early immune activation triggered by neoadjuvant administration, which promotes tumor downstaging, micrometastasis clearance, and accelerated systemic response[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Clinically meaningful thresholds were reached within the first year (around 9\u0026ndash;10 months), indicating that key milestones of measurable benefit occur during this period. Although continuing therapy beyond this period may confer incremental efficacy, it also increases toxicity, treatment burden, and cost[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consistent evidence from other settings supports optimizing rather than prolonging immunotherapy duration: Bryant et al. reported that reducing durvalumab maintenance after chemoradiation did not compromise outcomes, and meta-analytic data have questioned the biological necessity of fixed one-year regimens[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While prospective validation remains warranted, converging quantitative and clinical evidence suggests that a 9\u0026ndash;10-month course may represent a more balanced approach to efficacy, safety, and practicality than the conventional one-year regimen.\u003c/p\u003e \u003cp\u003eSubgroup analyses revealed distinct temporal patterns of benefit. Patients with stage III NSCLC achieved shorter TTB than those with stage II disease, indicating earlier clinical benefit in tumors with greater immunologic activity. This may reflect a more inflamed microenvironment with enhanced angiogenesis and lymphocytic infiltration that amplifies neoadjuvant immune priming[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similarly, squamous cell carcinoma showed shorter TTB than nonsquamous subtypes, likely due to its higher tumor mutational burden and chronic inflammatory state, aligning with prior evidence of improved outcomes in squamous histology[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Patients with PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;1% also derived earlier benefit, reflecting a pre-existing immune-activated phenotype and PD-1/PD-L1 pathway dependence[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Collectively, these results demonstrate that TTB complements conventional efficacy analyses by uncovering timing heterogeneity across subgroups. By quantifying when meaningful benefit emerges rather than only its magnitude, TTB provides an additional dimension for patient stratification and may inform individualized duration planning and adaptive perioperative immunotherapy strategies.\u003c/p\u003e \u003cp\u003eCompared with traditional efficacy evaluation indicators, TTB has obvious advantages in decision support and is an important supplement to the efficacy evaluation system. Traditional efficacy endpoints offer limited guidance for perioperative immunotherapy, whereas TTB provides complementary clinical value by quantifying the temporal dynamics of treatment benefit. Unlike EFS or DFS, which report event-rate differences at predefined timepoints (e.g., a 10% improvement in 2-year EFS) without indicating when such differences arise, TTB estimates the duration required to achieve a specific magnitude of benefit from the onset of therapeutic effect. This temporal perspective enables earlier recognition of clinical benefit and supports timely treatment adjustments before disease progression necessitates regimen modification. Similarly, while OS requires extended follow-up and is influenced by post-recurrence therapies, TTB offers additional temporal insight into long-term outcomes, allowing earlier contextualization of survival trends. By translating abstract statistics (e.g., HR\u0026thinsp;=\u0026thinsp;0.57) into tangible thresholds, such as a 2% reduction in recurrence risk after one month of therapy in patients with PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;1%, TTB enhances interpretability for both clinicians and patients, facilitating shared decision-making and dynamic monitoring of efficacy. Derived from validated endpoints including EFS, DFS, and OS, TTB remains grounded in clinically meaningful outcomes while adding a time-based interpretive layer. Beyond methodological refinement, TTB serves as a practical framework for optimizing perioperative immunotherapy strategies. It supports individualized decision-making in early-stage NSCLC, where long-term data remain limited, and its integration with toxicity and cost assessments may improve evidence-based duration planning, resource allocation, and future guideline development.\u003c/p\u003e \u003cp\u003eThis present study has several limitations that warrant consideration. This secondary analysis of published RCTs, lacking original safety data, precluded directly simultaneous assessment of \"time-to-harm\", despite the clinical need to balance benefit rapidity and toxicity timing. Future prospective studies should integrate temporal efficacy and safety analyses. In addition, TTB calculations utilized patient-level data reconstructed from KM curves. While methodological consistency was validated between original and digitized curves, minor estimation errors from the digitization process remain possible. Future studies using original patient data are needed for validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first quantitative assessment of TTB for perioperative immunotherapy in resectable NSCLC, establishing a temporal framework linking treatment duration with clinical outcomes. TTB analysis revealed a near-linear accumulation of benefit, with meaningful efficacy achieved within approximately 9\u0026ndash;10 months. These findings highlight TTB as a complementary endpoint, supporting evidence-based optimization of perioperative immunotherapy duration and individualized treatment planning.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Absolute risk reduction;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence interval;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDFS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Disease-free survival;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEFS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Event-free survival;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hazard ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immune checkpoint inhibitors;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kaplan-Meier;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNSCLC \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-small-cell lung cancer;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Overall survival;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePD-L1 \u0026nbsp; \u0026nbsp; \u0026nbsp; Programmed death-ligand 1;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePFS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Progression-free survival;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Randomized controlled trial;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTTB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Time to benefit\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Xi\u0026rsquo;an Jiaotong University Health Science Centre institutional review board (IRB) approved this study. The patient consent requirement was waived by the IRB because this was a secondary data analysis based on publications.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by grants from the Shaanxi Sanqin Scholars Innovation Team (No. 2021-No. 32), Innovation Capacity Support Program of Shaanxi Province (No. 23YXYJ0014), Major project of innovation Fund of Chinese Society of Clinical Oncology-MSD (Y-MSD2020-024), National Natural Science Foundation of China (NO. 82473732) and Key Research and Development Program of Shaanxi (No.2025SF-YBXM-338).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eYL, WB, and XJ contributed equally to this work. YL, CL, HG, and LJ had the idea for and designed the study. ML, YZ, XF, ZL, LH and LJ supervised the study. YL and WB did the statistical analysis, wrote the draft manuscript, and revised the manuscript. YL, XJ, JL, CL, HG, and LJ contributed to the acquisition, analysis, or interpretation of data, and revised the manuscript. The order of the co-first authors was assigned on the basis of their relative contributions to the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors thank the Department of Epidemiology and Health Statistics, School of Public Health in the Xi\u0026apos;an Jiaotong University Health Science Centre for assistance in statistical analyses.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available fromthe corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA: a Cancer Journal For Clinicians \u003c/em\u003e2024, 74(3):229-263.\u003c/li\u003e\n\u003cli\u003eTravis WD, Brambilla E, Riely GJ: New pathologic classification of lung cancer: relevance for clinical practice and clinical trials. \u003cem\u003eJournal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology \u003c/em\u003e2013, 31(8).\u003c/li\u003e\n\u003cli\u003eLiu S-Y, Feng W-N, Wu Y-L: Immunotherapy in resectable NSCLC: Answering the question or questioning the answer? \u003cem\u003eCancer Cell \u003c/em\u003e2024, 42(5):727-731.\u003c/li\u003e\n\u003cli\u003eLiu X, Li X, Yang F: [Pattern of Recurrence and Metastasis after Radical Resection of \u2029Non-small Cell Lung Cancer]. \u003cem\u003eZhongguo Fei Ai Za Zhi = Chinese Journal of Lung Cancer \u003c/em\u003e2022, 25(1):26-33.\u003c/li\u003e\n\u003cli\u003eCascone T, Awad MM, Spicer JD, He J, Lu S, Sepesi B, Tanaka F, Taube JM, Cornelissen R, Havel L\u003cem\u003e et al\u003c/em\u003e: Perioperative Nivolumab in Resectable Lung Cancer. \u003cem\u003eThe New England Journal of Medicine \u003c/em\u003e2024, 390(19):1756-1769.\u003c/li\u003e\n\u003cli\u003eFelip E, Altorki N, Zhou C, Csőszi T, Vynnychenko I, Goloborodko O, Luft A, Akopov A, Martinez-Marti A, Kenmotsu H\u003cem\u003e et al\u003c/em\u003e: Adjuvant atezolizumab after adjuvant chemotherapy in resected stage IB-IIIA non-small-cell lung cancer (IMpower010): a randomised, multicentre, open-label, phase 3 trial. \u003cem\u003eLancet (London, England) \u003c/em\u003e2021, 398(10308):1344-1357.\u003c/li\u003e\n\u003cli\u003eHeymach JV, Harpole D, Mitsudomi T, Taube JM, Galffy G, Hochmair M, Winder T, Zukov R, Garbaos G, Gao S\u003cem\u003e et al\u003c/em\u003e: Perioperative Durvalumab for Resectable Non-Small-Cell Lung Cancer. \u003cem\u003eThe New England Journal of Medicine \u003c/em\u003e2023, 389(18):1672-1684.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Brien M, Paz-Ares L, Marreaud S, Dafni U, Oselin K, Havel L, Esteban E, Isla D, Martinez-Marti A, Faehling M\u003cem\u003e et al\u003c/em\u003e: Pembrolizumab versus placebo as adjuvant therapy for completely resected stage IB-IIIA non-small-cell lung cancer (PEARLS/KEYNOTE-091): an interim analysis of a randomised, triple-blind, phase 3 trial. \u003cem\u003eThe Lancet Oncology \u003c/em\u003e2022, 23(10):1274-1286.\u003c/li\u003e\n\u003cli\u003eWakelee H, Liberman M, Kato T, Tsuboi M, Lee S-H, Gao S, Chen K-N, Dooms C, Majem M, Eigendorff E\u003cem\u003e et al\u003c/em\u003e: Perioperative Pembrolizumab for Early-Stage Non-Small-Cell Lung Cancer. \u003cem\u003eThe New England Journal of Medicine \u003c/em\u003e2023, 389(6):491-503.\u003c/li\u003e\n\u003cli\u003eForde PM, Spicer J, Lu S, Provencio M, Mitsudomi T, Awad MM, Felip E, Broderick SR, Brahmer JR, Swanson SJ\u003cem\u003e et al\u003c/em\u003e: Neoadjuvant Nivolumab plus Chemotherapy in Resectable Lung Cancer. \u003cem\u003eThe New England Journal of Medicine \u003c/em\u003e2022, 386(21):1973-1985.\u003c/li\u003e\n\u003cli\u003eProvencio M, Nadal E, Gonz\u0026aacute;lez-Larriba JL, Mart\u0026iacute;nez-Mart\u0026iacute; A, Bernab\u0026eacute; R, Bosch-Barrera J, Casal-Rubio J, Calvo V, Insa A, Ponce S\u003cem\u003e et al\u003c/em\u003e: Perioperative Nivolumab and Chemotherapy in Stage III Non-Small-Cell Lung Cancer. \u003cem\u003eThe New England Journal of Medicine \u003c/em\u003e2023, 389(6):504-513.\u003c/li\u003e\n\u003cli\u003eYue D, Wang W, Liu H, Chen Q, Chen C, Liu L, Zhang P, Zhao G, Yang F, Han G\u003cem\u003e et al\u003c/em\u003e: Perioperative tislelizumab plus neoadjuvant chemotherapy for patients with resectable non-small-cell lung cancer (RATIONALE-315): an interim analysis of a randomised clinical trial. \u003cem\u003eThe Lancet Respiratory Medicine \u003c/em\u003e2024, 13(2):119-129.\u003c/li\u003e\n\u003cli\u003eWaterhouse DM, Garon EB, Chandler J, McCleod M, Hussein M, Jotte R, Horn L, Daniel DB, Keogh G, Creelan B\u003cem\u003e et al\u003c/em\u003e: Continuous Versus 1-Year Fixed-Duration Nivolumab in Previously Treated Advanced Non-Small-Cell Lung Cancer: CheckMate 153. \u003cem\u003eJournal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology \u003c/em\u003e2020, 38(33):3863-3873.\u003c/li\u003e\n\u003cli\u003eThompson JA, Schneider BJ, Brahmer J, Andrews S, Armand P, Bhatia S, Budde LE, Costa L, Davies M, Dunnington D\u003cem\u003e et al\u003c/em\u003e: NCCN Guidelines Insights: Management of Immunotherapy-Related Toxicities, Version 1.2020. \u003cem\u003eJournal of the National Comprehensive Cancer Network : JNCCN \u003c/em\u003e2020, 18(3):230-241.\u003c/li\u003e\n\u003cli\u003eMountzios G, Remon J, Hendriks LEL, Garc\u0026iacute;a-Campelo R, Rolfo C, Van Schil P, Forde PM, Besse B, Subbiah V, Reck M\u003cem\u003e et al\u003c/em\u003e: Immune-checkpoint inhibition for resectable non-small-cell lung cancer - opportunities and challenges. \u003cem\u003eNature Reviews Clinical Oncology \u003c/em\u003e2023, 20(10):664-677.\u003c/li\u003e\n\u003cli\u003eDesai A, Schwed K, Kalesinskas L, Yuan Q, Bryan J, Keane C, Fidyk E, Castellanos E, Cohen AB, Harrison K\u003cem\u003e et al\u003c/em\u003e: Clinical Outcomes of Perioperative Immunotherapy in Resectable Non-Small Cell Lung Cancer. \u003cem\u003eJAMA Network Open \u003c/em\u003e2025, 8(6):e2517953.\u003c/li\u003e\n\u003cli\u003eBryant AK, Sankar K, Zhao L, Strohbehn GW, Elliott D, Moghanaki D, Kelley MJ, Ramnath N, Green MD: De-escalating adjuvant durvalumab treatment duration in stage III non-small cell lung cancer. \u003cem\u003eEuropean Journal of Cancer (Oxford, England : 1990) \u003c/em\u003e2022, 171:55-63.\u003c/li\u003e\n\u003cli\u003eChen T-T: Milestone Survival: A Potential Intermediate Endpoint for Immune Checkpoint Inhibitors. \u003cem\u003eJournal of the National Cancer Institute \u003c/em\u003e2015, 107(9).\u003c/li\u003e\n\u003cli\u003eWest HJ, Kim JY: Rapid Advances in Resectable Non-Small Cell Lung Cancer: A Narrative Review. \u003cem\u003eJAMA Oncology \u003c/em\u003e2024, 10(2):249-255.\u003c/li\u003e\n\u003cli\u003eAnagnostou V, Yarchoan M, Hansen AR, Wang H, Verde F, Sharon E, Collyar D, Chow LQM, Forde PM: Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity. \u003cem\u003eClinical Cancer Research : an Official Journal of the American Association For Cancer Research \u003c/em\u003e2017, 23(17):4959-4969.\u003c/li\u003e\n\u003cli\u003eLee SJ, Boscardin WJ, Stijacic-Cenzer I, Conell-Price J, O\u0026apos;Brien S, Walter LC: Time lag to benefit after screening for breast and colorectal cancer: meta-analysis of survival data from the United States, Sweden, United Kingdom, and Denmark. \u003cem\u003eBMJ (Clinical Research ed) \u003c/em\u003e2013, 346:e8441.\u003c/li\u003e\n\u003cli\u003eDeardorff WJ, Cenzer I, Nguyen B, Lee SJ: Time to Benefit of Bisphosphonate Therapy for the Prevention of Fractures Among Postmenopausal Women With Osteoporosis: A Meta-analysis of Randomized Clinical Trials. \u003cem\u003eJAMA Intern Med \u003c/em\u003e2022, 182(1):33-41.\u003c/li\u003e\n\u003cli\u003eChen K, Nie Z, Shi R, Yu D, Wang Q, Shao F, Wu G, Wu Z, Chen T, Li C: Time to Benefit of Sodium-Glucose Cotransporter-2 Inhibitors Among Patients With Heart Failure. \u003cem\u003eJAMA Network Open \u003c/em\u003e2023, 6(8):e2330754.\u003c/li\u003e\n\u003cli\u003eChen T, Shao F, Chen K, Wang Y, Wu Z, Wang Y, Gao Y, Cornelius V, Li C, Jiang Z: Time to Clinical Benefit of Intensive Blood Pressure Lowering in Patients 60 Years and Older With Hypertension: A Secondary Analysis of Randomized Clinical Trials. \u003cem\u003eJAMA Intern Med \u003c/em\u003e2022, 182(6):660-667.\u003c/li\u003e\n\u003cli\u003eWang J, Hou X, Peng L, Dang Y, Xu X, Wei C, Guo R, Song W, He C, Jiang J\u003cem\u003e et al\u003c/em\u003e: Time to Benefit of Androgen Deprivation Therapy in Patients With Localized Prostate Cancer Undergoing Radiotherapy. \u003cem\u003eJCO Precision Oncology \u003c/em\u003e2025, 9:e2400605.\u003c/li\u003e\n\u003cli\u003eHui Z, Ren Y, Zhang D, Chen Y, Yu W, Cao J, Liu L, Wang T, Xiao S, Zheng L\u003cem\u003e et al\u003c/em\u003e: PD-1 blockade potentiates neoadjuvant chemotherapy in NSCLC via increasing CD127+ and KLRG1+ CD8 T cells. \u003cem\u003eNPJ Precision Oncology \u003c/em\u003e2023, 7(1):48.\u003c/li\u003e\n\u003cli\u003eSchuler M, Cuppens K, Pl\u0026ouml;nes T, Wiesweg M, Du Pont B, Hegedus B, K\u0026ouml;ster J, Mairinger F, Darwiche K, Paschen A\u003cem\u003e et al\u003c/em\u003e: Neoadjuvant nivolumab with or without relatlimab in resectable non-small-cell lung cancer: a randomized phase 2 trial. \u003cem\u003eNature Medicine \u003c/em\u003e2024, 30(6):1602-1611.\u003c/li\u003e\n\u003cli\u003eSun L, Bleiberg B, Hwang W-T, Marmarelis ME, Langer CJ, Singh A, Cohen RB, Mamtani R, Aggarwal C: Association Between Duration of Immunotherapy and Overall Survival in Advanced Non-Small Cell Lung Cancer. \u003cem\u003eJAMA Oncology \u003c/em\u003e2023, 9(8):1075-1082.\u003c/li\u003e\n\u003cli\u003eGhisoni E, Wicky A, Bouchaab H, Imbimbo M, Delyon J, Gautron Moura B, G\u0026eacute;rard CL, Latifyan S, \u0026Ouml;zdemir BC, Caikovski M\u003cem\u003e et al\u003c/em\u003e: Late-onset and long-lasting immune-related adverse events from immune checkpoint-inhibitors: An overlooked aspect in immunotherapy. \u003cem\u003eEuropean Journal of Cancer (Oxford, England : 1990) \u003c/em\u003e2021, 149:153-164.\u003c/li\u003e\n\u003cli\u003eNuccio A, Viscardi G, Salomone F, Servetto A, Venanzi FM, Riva ST, Oresti S, Ogliari FR, Vigan\u0026ograve; M, Bulotta A\u003cem\u003e et al\u003c/em\u003e: Systematic review and meta-analysis of immune checkpoint inhibitors as single agent or in combination with chemotherapy in early-stage non-small cell lung cancer: Impact of clinicopathological factors and indirect comparison between treatment strategies. \u003cem\u003eEuropean Journal of Cancer (Oxford, England : 1990) \u003c/em\u003e2023, 195:113404.\u003c/li\u003e\n\u003cli\u003eTopalian SL, Forde PM, Emens LA, Yarchoan M, Smith KN, Pardoll DM: Neoadjuvant immune checkpoint blockade: A window of opportunity to advance cancer immunotherapy. \u003cem\u003eCancer Cell \u003c/em\u003e2023, 41(9):1551-1566.\u003c/li\u003e\n\u003cli\u003eParra ER, Zhang J, Duose DY, Gonzalez-Kozlova E, Redman MW, Chen H, Manyam GC, Kumar G, Zhang J, Song X\u003cem\u003e et al\u003c/em\u003e: Multi-omics Analysis Reveals Immune Features Associated with Immunotherapy Benefit in Patients with Squamous Cell Lung Cancer from Phase III Lung-MAP S1400I Trial. \u003cem\u003eClinical Cancer Research : an Official Journal of the American Association For Cancer Research \u003c/em\u003e2024, 30(8):1655-1668.\u003c/li\u003e\n\u003cli\u003eMo D-C, Huang J-F, Lin P, Huang S-X, Wang H-L, Luo P-H, Liang X-J: The role of PD-L1 in patients with non-small cell lung cancer receiving neoadjuvant immune checkpoint inhibitor plus chemotherapy: a meta-analysis. \u003cem\u003eScientific Reports \u003c/em\u003e2024, 14(1):26200.\u003c/li\u003e\n\u003cli\u003eSpicer JD, Cascone T, Wynes MW, Ahn M-J, Dacic S, Felip E, Forde PM, Higgins KA, Kris MG, Mitsudomi T\u003cem\u003e et al\u003c/em\u003e: Neoadjuvant and Adjuvant Treatments for Early Stage Resectable NSCLC: Consensus Recommendations From the International Association for the Study of Lung Cancer. \u003cem\u003eJournal of Thoracic Oncology : Official Publication of the International Association For the Study of Lung Cancer \u003c/em\u003e2024, 19(10):1373-1414.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non–small cell lung cancer, Perioperative therapy, Immune checkpoint inhibitors, Time to benefit","lastPublishedDoi":"10.21203/rs.3.rs-8788627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8788627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe optimal duration of perioperative immunotherapy for resectable non\u0026ndash;small-cell lung cancer (NSCLC) remains undefined, and conventional efficacy measures provide limited insight into the temporal pattern of benefit emergence, for which time-to-benefit (TTB) serves as a complementary metric that quantifies how therapeutic effects accumulate over time and bridges the gap between treatment duration and clinical benefit. This study aimed to quantitatively assess the TTB of perioperative immunotherapy and determine the treatment duration required to achieve predefined absolute risk reduction (ARR) thresholds.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis comparative effectiveness study reconstructed individual patient data from eight randomized controlled trials (RCTs) using digitized Kaplan-Meier curves. Pooled survival analyses and Weibull survival modeling with Monte Carlo simulation were applied to estimate TTB at ARR thresholds. Subgroup analyses were conducted by histology, stage, and PD-L1 expression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 5,123 participants, the entire perioperative immunotherapy significantly improved event-free survival (EFS) and overall survival (OS). TTB analysis showed a near-linear accumulation of benefit: 1.02 months of therapy were required for a 1% EFS-related risk reduction and 9.01 months for a 10% reduction. For OS, a 1% mortality risk reduction required 10.67 months. Subgroup analyses demonstrated that, for the same ARR thresholds, patients with squamous histology, stage III disease, or PD-L1 expression\u0026thinsp;\u0026ge;\u0026thinsp;1% achieved shorter TTB.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study quantitatively evaluated TTB in perioperative immunotherapy for resectable NSCLC, revealing a near-linear accumulation of benefit with meaningful efficacy by 9\u0026ndash;10 months, and establishing TTB as a complementary endpoint to guide evidence-based, individualized treatment duration optimization.\u003c/p\u003e","manuscriptTitle":"Time to Benefit of Perioperative Immunotherapy Among Patients With Resectable Non–Small-Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 09:10:53","doi":"10.21203/rs.3.rs-8788627/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T11:58:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T16:43:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228423006743080533072867874777947819143","date":"2026-04-29T15:32:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T12:47:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41005919319654973323828541063211960529","date":"2026-02-24T06:28:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T08:22:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T13:36:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T01:12:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T01:11:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-02-04T15:48:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30bd194e-e1a7-4cee-b3f0-3b6b35f5277e","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T11:58:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T16:43:26+00:00","index":80,"fulltext":""},{"type":"reviewerAgreed","content":"228423006743080533072867874777947819143","date":"2026-04-29T15:32:06+00:00","index":63,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T12:42:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 09:10:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8788627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8788627","identity":"rs-8788627","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}