Relative efficacies of EGFR-TKIs and immune checkpoint inhibitors for treatment of recurrent non-small cell lung cancer after surgery

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Methods Among 801 patients with NSCLC who underwent pulmonary resection at Kanazawa Medical University between 2017 and 2021, 64 patients had recurrence. We retrospectively compared the efficacies of EGFR-TKIs and ICIs in these patients with recurrent NSCLC who underwent pulmonary resection. Results The 3-year overall survival rates after recurrence were 79.3% in patients who received EGFR-TKIs, 69.5% in patients who received ICIs, and 43.7% in patients who received cytotoxic agents. There was no significant difference in overall survival between patients treated with EGFR-TKIs and ICIs (p = 0.14) or between patients treated with ICIs and cytotoxic agents (p = 0.23), but overall survival was significantly higher in patients treated with EGFR-TKIs compared with cytotoxic agents (p < 0.01) The probabilities of a 2-year response were 88.5%, 61.6%, and 25.9% in patients treated with EGFR-TKIs, ICIs, and cytotoxic agents, respectively. There was no significant difference in response periods between patients treated with EGFR-TKIs and ICIs (p = 0.18), but the response period was significantly better in patients treated with EGFR-TKIs (p < 0.01) or ICIs (p = 0.03) compared with cytotoxic agents. Percent-predicted vital capacity (p = 0.03) and epidermal growth factor receptor gene mutation (p < 0.01) were significant factors affecting the overall response to chemotherapy in multivariate analysis. Conclusion EGFR-TKIs and ICIs are effective for treating recurrent NSCLC after surgery. Although adjuvant chemotherapy for completely resected pathological stage II to IIIA NSCLC, atezolizumab or osimertinib, has also been recently approved as adjuvant chemotherapy, there is a risk that patients who relapse after adjuvant chemotherapy will have less choice. postoperative recurrence chemotherapy non-small cell lung cancer epidermal growth factor receptor-tyrosine kinase inhibitor immune checkpoint inhibiter Figures Figure 1 Figure 2 Figure 3 1. Introduction Lung cancer is the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for more than 80% of all cases [ 1 ]. Treatment strategies for advanced NSCLC have changed over the past decade, and tumors with epidermal growth factor receptor (EGFR) gene mutations can be targeted therapeutically with tyrosine kinase inhibitors (TKIs). Several phase III studies in patients with advanced NSCLC harboring EGFR mutations have shown significant improvements in response rates and prognosis following treatment with EGFR-TKIs compared with platinum-based chemotherapy [ 2 – 5 ]. Furthermore, in addition to cytotoxic chemotherapy and targeted therapy for tumors harboring certain genetic aberrations, immunotherapy targeting immune checkpoints using antibodies to programmed cell death protein-1 (PD-1) and its ligand, PD-L1, has become an established treatment modality for NSCLC [ 6 – 9 ]. Immune checkpoint inhibitors (ICIs) have revealed greater efficacy than cytotoxic chemotherapy in patients with NSCLC whose tumors expressed PD-L1 on tumor cells and/or immune cells [ 6 – 9 ]. However, the relative efficacies of EGFR-TKIs and ICIs for the treatment of recurrent NSCLC after surgery remains unclear. In this study, we retrospectively evaluated the efficacies of EGFR-TKIs and ICIs in patients with recurrent NSCLC who underwent pulmonary resection. 2. Patients and Methods 2.1 Patients Among 801 patients with NSCLC who underwent pulmonary resection at Kanazawa Medical University between 2017 and 2021, 64 patients had recurrence and were enrolled in this retrospective study. This study was conducted in accordance with the principles of the Declaration of Helsinki and the protocol was approved by the institutional review committee of Kanazawa Medical University (approval number: I392). All patients provided written informed consent. Clinical data including sex, age, smoking history, carcinoembryonic antigen, prognostic nutrition index (PNI), neutrophil-to-lymphocyte ratio (NLR), [ 18 ]F-fluorodeoxyglucose positron emission tomography/computed tomography maximum standardized uptake value (SUV max ), and lobe involvement were collected. Respiratory function parameters including percent-predicted vital capacity (%VC) and forced expiratory volume in 1 s as a percentage of forced vital capacity (FEV 1 %) were also collected. Smoking history was assessed using the Brinkman index, which was calculated by multiplying the number of cigarettes smoked per day by the number of years that the patient had smoked [ 10 ]. Preoperative PNI, which has been reported as a prognostic factor in patients with NSCLC [ 11 , 12 ], was calculated by combining serum albumin levels with the total peripheral lymphocyte count in peripheral blood. NLR is used as an indicator of systemic inflammation and stress in critically ill surgical and medical patients [ 13 ], and has also been reported to be a prognostic factor in patients with NSCLC who have undergone pulmonary resection [ 14 , 15 ]. The most effective chemotherapy regimen in each patient was classified as EGFR-TKI, ICI monotherapy or combined with a cytotoxic agent, and cytotoxic agent. 2.2 Pathological factors Data on histological type, lymphatic invasion, vascular invasion, differentiation, pathological stage, EGFR mutation, and PD-L1 expression were collected. 2.3 Statistical analyses Frequencies of variables were compared using Pearson’s χ 2 test of independence. Cumulative survival was calculated by the Kaplan–Meier method, and survival curves were compared using the log-rank test. Cut-off values for factors associated with recurrence were calculated by receiver operating characteristic (ROC) curve analysis, and prognostic analyses were performed using these cut-off values. Significant factors affecting overall response were analyzed by logistic regression. Risk factors for overall survival after recurrence were analyzed by Cox proportional hazards regression. All statistical analyses were two-sided and the statistical significance was set at p < 0.05. Statistical analyses were performed using JMP software v13.2 (SAS Institute Inc., Cary, NC, USA). 3. Results 3.1 Patient characteristics The relationships between the clinicopathological characteristics of the 64 patients with recurrent NSCLC after surgery and their overall responses to chemotherapy are shown in Table 1 . The proportion of males (55.5% vs 100%, p < 0.01) and the Brinkman index (300 vs 800, p < 0.01) were significantly lower among patients with a complete or partial response, compared with those with stable or progressive disease. The proportions of patients with adenocarcinoma (88.9% vs 42.8%, p < 0.01), lymphatic invasion (58.3% vs 32.1%, p = 0.03), and positive EGFR mutation (63.8% vs 3.5%, p < 0.01) were also higher among those with a complete or partial response, compared with those with stable or progressive disease. The objective response rates are shown in Table 2 . The objective response rates were 100%, 38.7%, and 10.0% in patients receiving EGFR-TKIs (n = 23), ICIs (n = 31), and cytotoxic agents (n = 10), respectively. Of those patients, 95% of patients received EGFR-TKIs on the first line, 84% for ICI, and 60% for cytotoxic agent. Table 1 Comparison of patient characteristic between complete response or partial response and stable disease or progressive disease. CR or PR (n = 36) SD or PD (n = 28) p- value Gender (male / female) 20 / 16 28 / 0 < 0.01 Age, median, range (y) 67 (34–87) 71 (52–86) 0.34 Brinkman index, median, range 300 (0–2700) 800 (100–2250) 0.02 CEA, median, range (ng/ml) 3.4 (1.1–70.3) 6.5 (1.0–100.2) 0.15 %VC, median, range 100.2 (83.8-136.3) 93.9 (64.9-129.5) 0.05 FEV 1 %, median, range 73.6 (44.8–88.8) 73.6 (36.8–92.2) 0.65 PNI, median, range 50.0 (41.2–61.3) 47.6 (336.6–60.4) 0.62 NLR, median, range 2.58 (1.12–7.55) 2.60 (0.53–13.71) 0.57 SUV max , median, range 7.91 (1.32–23.35) 8.94 (1.50–22.59) 0.23 Histological type (Ad / Sq / LCNEC / AdSq / Large) 32 / 4 / 0 / 0 / 0 12 / 12 / 1 / 1 / 2 < 0.01 Ad 32 (88.9%) 12 (42.8%) < 0.01 Lobe (RU / RM / RL / LU / LL) 10 / 4 / 11 / 6 / 5 4 / 1 / 8 / 8 / 7 0.08 Lower lobe 16 (44.4%) 15 (53.5%) 0.46 Ly (absent / present) 15 / 21 19 / 9 0.03 V (absent/present) 10 / 26 12 / 16 0.20 G (1 / 2 / 3 / 4) 6 / 23 / 7 / 0 2 / 18 / 6 / 2 0.29 G3-4 7 (19.4%) 8 (28.5%) 0.39 pStage (IA / IB / IIA / IIB / IIIA / IIIB) 9 / 5 / 1 / 9 / 11 / 1 9 / 6 / 1 / 4 / 8 / 0 0.77 pStage ≥ II 22 (61.1%) 13 (46.4%) 0.24 PD-L1, median, range (%) 12.5 (0–95) 25 (0–95) 0.21 Positive of EGFR mutation 23 (63.8%) 1 (3.5%) < 0.01 Effective regimen (ICI / EGFR-TKI / Cytotoxic) 12 / 23 / 1 19 / 0 / 9 < 0.01 Line of effective regimen (1st / 2nd / 3rd / 4th ) 33 / 2 / 0 / 1 21 / 4 / 1/ 2 0.29 Relapse free survival of effective regimen, median, range (days) 731 (133–1847) 169 (21–1094) < 0.01 Overall survival after recurrence, median, range (days) 861 (171–1935) 416 (43–1188) < 0.01 CEA; carcinoembryonic antigen, %VC; % vital capacity, FEV 1 %; forced expiratory volume % in one second, PNI; prognostic nutrition index, NLR; neutrophil-to-lymphocyte ratio, SUV max ; maximum of standardized uptake value, Ad; adenocarcinoma, Sq; squamous cell carcinoma, LCNEC; large cell neuroendocrine carcinoma, AdSq; adenosquamous cell carcinoma, Large; large cell carcinoma, RU; right upper, RM; right middle, RL; right lower, LU; left upper, LL; left lower, Ly; lymphatic invasion, V; vascular invasion, G; grade of differentiation, pStage; pathological stage, PD-L1; programmed death-ligand 1, EGFR; epithelial growth factor receptor, ICI; immune checkpoint inhibitor, TKI; tyrosine kinase inhibitor, Cytotoxic; cytotoxic agent. Table 2 Objective response rate of chemotherapy Objective response rate (%) p -value Epithelial growth factor receptor-tyrosine kinase inhibitor (n = 23) 100 < 0.01 Immune checkpoint inhibitor (n = 31) 38.7 Cytotoxic agent (n = 10) 10.0 3.2 Univariate and multivariate analyses The relationships between the clinicopathological characteristics and overall response to chemotherapy are shown in Table 3 . The following cut-off values for factors associated with recurrence were calculated using ROC curve analysis: age, 72 years; %VC, 90; FEV 1 %, 70; PNI, 47.55; NLR, 4.04; SUV max , 13.36; and PD-L1, 50. Univariate analysis identified Brinkman index (p = 0.01), %VC (p = 0.01), NLR (p = 0.02), SUV max (p = 0.04), adenocarcinoma (p < 0.01), lymphatic invasion (p = 0.03), EGFR mutation (p < 0.01), and ICIs (p < 0.01) as significant factors affecting the overall response to chemotherapy. However, only %VC (odds ratio [OR]: 0.03, 95% confidence interval [CI]: 0.003–0.78, p = 0.03) and EGFR mutation (OR: 681.40, 95% CI: 15.75–29471.21, p < 0.01) were significant factors in multivariate analysis, and ICI was not a significant factor (OR: 6.41, 95%CI: 0.48–85.80, p = 0.16). The relationships between the clinicopathological characteristics and overall survival after recurrence are shown in Table 4 . Univariate analysis identified adenocarcinoma (p = 0.04) and EGFR mutation (p = 0.01) as risk factors for overall survival after recurrence; however, neither adenocarcinoma (HR: 0.54, 95%CI: 0.28–1.66, p = 0.28) nor EGFR mutation (HR: 0.24, 95% CI: 0.05–1.21, p = 0.08) were risk factors in multivariate analysis. Table 3 Univariate analysis and multivariate analysis of significant factors for complete response or partial response. Univariate analysis Multivariate analysis OR 95%CI p -value OR 95%CI p -value Male NA NA Age > 72 0.50 0.18–1.41 0.19 BI ≥ 600 0.26 0.09–0.78 0.01 3.44 0.37–31.34 0.27 CEA > 5 0.39 0.14–1.09 0.07 %VC < 90 0.19 0.05–0.69 0.01 0.05 0.003–0.78 0.03 FEV 1 % < 70 0.51 0.17–1.54 0.23 PNI 4.04 0.19 0.04–0.79 0.02 0.11 0.006-2.10 0.14 SUV max > 13.36 0.29 0.08–0.98 0.04 0.45 0.05–4.08 0.48 Ad 10.66 2.96–38.39 < 0.01 5.70 0.63–50.96 0.11 Lower lobe 0.69 0.25–1.86 0.46 Ly (+) 2.95 1.05–8.30 0.03 0.29 0.02–3.31 0.32 V (+) 1.95 0.68–5.54 0.21 G3-4 0.60 0.18–1.93 0.39 pStag ≥ II 1.81 0.66–4.93 0.24 PD-L1 ≥ 50 0.51 0.18–1.45 0.21 mEGFR (+) 47.76 5.79-393.46 < 0.01 681.40 15.75-29471.21 < 0.01 ICI 0.23 0.08–0.67 < 0.01 6.41 0.48–85.80 0.16 1st line 5.20 1.15–23.38 0.03 4.51 0.37–53.98 0.23 OR; odds ratio, CI; confidence interval, BI; Brinkman index, CEA; carcinoembryonic antigen, %VC; % vital capacity, FEV 1 %; forced expiratory volume % in one second, PNI; prognostic nutrition index, NLR; neutrophil-to-lymphocyte ratio, SUV max ; maximum of standardized uptake value, Ad; adenocarcinoma, Ly; lymphatic invasion, V; vascular invasion, G; grade of differentiation, pStage; pathological stage, PD-L1; programmed death-ligand 1, EGFR; epithelial growth factor receptor, ICI; immune checkpoint inhibitor. Table 4 Univariate analysis and multivariate analysis of risk factors for overall survival after recurrence. Univariate analysis Multivariate analysis HR 95%CI p -value HR 95%CI p -value Male 2.33 0.74–10.23 0.15 Age > 72 1.44 0.51–3.81 0.46 BI ≥ 600 1.60 0.60–4.66 0.34 CEA > 5 1.31 0.49–3.54 0.57 %VC < 90 2.51 0.85–6.70 0.09 FEV 1 % < 70 1.62 0.58–4.28 0.34 PNI 4.04 2.36 0.74–6.52 0.13 SUV max > 13.36 1.66 0.52–4.51 0.35 Ad 0.36 0.13–0.99 0.04 0.60 0.18–1.84 0.38 Lower lobe 1.44 0.55–3.87 0.44 Ly (+) 0.45 0.14–1.23 0.12 V (+) 0.58 0.21–1.62 0.28 G3-4 1.24 0.39–3.37 0.68 pStag ≥ II 0.81 0.30–2.18 0.67 PD-L1 ≥ 50 0.83 0.26–2.26 0.73 mEGFR 0.28 0.07–0.84 0.02 0.37 0.09–1.44 0.15 ICI 1.28 0.47–3.41 0.60 1st line 0.57 0.25–2.54 0.71 HR; hazard ratio, CI; confidence interval, BI; Brinkman index, CEA; carcinoembryonic antigen, %VC; % vital capacity, FEV 1 %; forced expiratory volume % in one second, PNI; prognostic nutrition index, NLR; neutrophil-to-lymphocyte ratio, SUV max ; maximum of standardized uptake value, Ad; adenocarcinoma, Ly; lymphatic invasion, V; vascular invasion, G; grade of differentiation, pStage; pathological stage, PD-L1; programmed death-ligand 1, mEGFR; mutation of epithelial growth factor receptor, ICI; immune checkpoint inhibitor. 3.3 Survival curves The overall survival curves after recurrence according to chemotherapy regimen are shown in Fig. 1 . The 3-year overall survival rates after recurrence were 79.3% 69.5%, and 43.7% in patients receiving EGFR-TKIs, ICIs, and cytotoxic agents. There was no significant difference in overall survival between patients receiving EGFR-TKIs and ICIs (p = 0.14) or between patients receiving ICIs and cytotoxic agents (p = 0.23); however, overall survival was significantly higher in patients treated with EGFR-TKIs compared with those treated with cytotoxic agents (p < 0.01). The response periods according to the chemotherapy regimens are shown in Fig. 2 . The median response periods were 821, 232, and 250 days in patients receiving EGFR-TKIs, ICIs, and cytotoxic agents, respectively, and the respective probabilities of a 2-year response were 88.5%, 61.6%, and 25.9%. There was no significant difference in response periods between patients receiving EGFR-TKIs and ICIs (p = 0.18), but the response periods were significantly higher in patients receiving EGFR-TKIs (p < 0.01) or ICIs (p = 0.03) compared with those receiving cytotoxic agents. The results of a sub-analysis of the response periods in relation to PD-L1 expression in patients receiving ICIs are shown in Fig. 3 . There was no significant difference in the probability of a 2-year response between patients with and without high expression levels of PD-L1 (p = 0.91), or between patients with and without PD-L1 expression (p = 0.57). 4. Discussion In this study, we evaluated and compared the efficacies of EGFR-TKIs and ICIs for the treatment of recurrent NSCLC in patients who underwent pulmonary resection. Although EGFR-TKIs have demonstrated significant improvements in response rates and prognosis in patients with advanced NSCLC harboring EGFR mutations in several studies [ 2 – 5 ], EGFR-TKIs were also associated with significantly higher complete or partial response rates and a longer response period compared with cytotoxic agents in patients with recurrent NSCLC after surgery in this study. Although the overall survival curves suggested that EGFR-TKIs were significantly more effective than cytotoxic agents in patients with recurrent NSCLC after surgery, there was no significant difference between EGFR-TKIs and ICIs. ICIs have demonstrated efficacy in patients with advanced NSCLC [ 16 – 19 ]. In the current study, the response period was significantly longer in patients treated with ICIs compared with cytotoxic agents, suggesting that ICIs may be an effective treatment for recurrent NSCLC after surgery. However, although the efficacy of ICIs has been reported to depend on PD-L1 expression [ 16 , 17 , 19 ], the response periods in the current study did not differ between patients with and without PD-L1 expression, suggesting that PD-L1 expression might not be a useful predictor of ICI response in patients with postoperative recurrence. Adjuvant chemotherapy is currently preformed in patients with completely resected pathological stage II to IIIA NSCLC. Atezolizumab and osimertinib have also recently been approved as adjuvant chemotherapeutic agents, and were shown to significantly improve disease-free survival in patients receiving postoperative adjuvant chemotherapy [ 20 , 21 ]. However, it cannot deny the possibility of administering unnecessary adjuvant chemotherapy even for cases that do not recurrence, and there is a risk that patients who relapse after adjuvant chemotherapy will have less choice. This study had several limitations. First, it was a retrospective study and may have included unobserved confounding and/or selection biases. Second, the study was performed at a single institution, and the study population was relatively small. In summary, our findings revealed that EGFR-TKIs and ICIs could provide effective treatment for patients with recurrent NSCLC after surgery. Although adjuvant chemotherapy for completely resected pathological stage II to IIIA NSCLC, atezolizumab or osimertinib, has also been recently approved as adjuvant chemotherapy, there is a risk that patients who relapse after adjuvant chemotherapy will have less choice. Declarations Acknowledgment: We thank Susan Furness, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. Ethics approval and consent to participate The present study was conducted in accordance with the amended Declaration of Helsinki. The Institutional Review Boards of Kanazawa Medical University approved the protocol (approval number: I392), and written informed consent was obtained from all of the patients. Consent to publish Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to [our institutional restrictions e.g., them containing information that could compromise research participant privacy/consent], but are available from the corresponding author on reasonable request. C ompeting interests The authors declare that they have no competing interests. F unding This study has not been funded. Author’s contributions N. M. performed the research, collected and analyzed the data and wrote the paper. T.M., M.I., S. I., and Y.I. contributed to sample collection. H. U. contributed to supervision of this study and revision of the manuscript. All authors have read and approved the manuscript, and ensure that this is the case. 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Immunopharmacol 2021;96:107594. Felip E, Altorki N, Zhou C, Csőszi T, Vynnychenko I, Goloborodko O, 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 2021; 398: 1344–57. Wu YL, John T, Grohe C, Majem M, Goldman JW, Kim SW, et al. Postoperative chemotherapy use and outcomes from ADAURA: osimertinib as adjuvant therapy for resected EGFR-mutated NSCLC. J Thorac Oncol 2021;17:423-433. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-3022315","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":207348935,"identity":"c1fcaead-0e23-4ce1-a266-9f8cb60144dd","order_by":0,"name":"Nozomu Motono","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYPACGwNkHpjDjFs1WCqNdC2HMbXgBLrt/QcfF1ScN+af3fz4w882O6BI8waGHzUM7OY4tJidOcxsPOPMbTOJO8cMDHvbkoEixwoYe44xMFs24NByI5lNmrfttg3DjQSDBN5tzPXbbuQYMPA2MDAbHMCr5ZyN/I30Dwf/bqtnMLv/xoDxL2EtB8wMbuQYNvNuOwwU4TFgxmvLmcPGxjxnko0Nb+QUM8v+Ow4USSs4LHNMArdfjjc+fMxTYWc470b65o9vzlQDRQ5vfPimxiYZV4hhB0AnSSTjjx1swI50LaNgFIyCUTBMAQAwyFmlk0zUmwAAAABJRU5ErkJggg==","orcid":"","institution":"Kanazawa Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Nozomu","middleName":"","lastName":"Motono","suffix":""},{"id":207348936,"identity":"86be077a-0b87-4daa-9625-24a4e7096b4c","order_by":1,"name":"Takaki Mizoguchi","email":"","orcid":"","institution":"Kanazawa Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Takaki","middleName":"","lastName":"Mizoguchi","suffix":""},{"id":207348937,"identity":"21b4280b-e071-4627-8277-6e25e91b5f07","order_by":2,"name":"Masahito Ishikawa","email":"","orcid":"","institution":"Kanazawa Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Masahito","middleName":"","lastName":"Ishikawa","suffix":""},{"id":207348938,"identity":"ea42b196-dd58-42b5-b9e4-f3f8046bcaad","order_by":3,"name":"Shun Iwai","email":"","orcid":"","institution":"Kanazawa Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shun","middleName":"","lastName":"Iwai","suffix":""},{"id":207348939,"identity":"577c9fb2-aabd-4629-afc6-e9924fe0f93d","order_by":4,"name":"Yoshihito Iijima","email":"","orcid":"","institution":"Kanazawa Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yoshihito","middleName":"","lastName":"Iijima","suffix":""},{"id":207348940,"identity":"98f7070e-6f88-45d1-ab7b-3e367c50dbb6","order_by":5,"name":"Hidetaka Uramoto","email":"","orcid":"","institution":"Kanazawa Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hidetaka","middleName":"","lastName":"Uramoto","suffix":""}],"badges":[],"createdAt":"2023-06-05 03:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3022315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3022315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38239707,"identity":"6fa47a0e-8e90-486d-89bf-bb4470011e01","added_by":"auto","created_at":"2023-06-08 16:08:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1219797,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival after recurrence by chemotherapy regimens\u003c/p\u003e\n\u003cp\u003eThere was not significant difference of OS between EGFR-TKI and ICI (p=0.14) or ICI and cytotoxic agent (p=0.23). There was significant difference between EGFR-TKI and cytotoxic agent (p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3022315/v1/ce0c83b6b8f3bf21fb1ecf1e.jpg"},{"id":38239705,"identity":"738d750a-a985-4029-a0ec-d20e7e058d80","added_by":"auto","created_at":"2023-06-08 16:08:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1295526,"visible":true,"origin":"","legend":"\u003cp\u003eResponse period by chemotherapy regimens\u003c/p\u003e\n\u003cp\u003eThere was not significant difference of response period between EGFR-TKI and ICI (p=0.18). There was significant difference between EGFR-TKI and cytotoxic agent (p\u0026lt;0.01) or ICI and cytotoxic agent (p=0.03).\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3022315/v1/df7f8e31cfbe6a875718dfff.jpg"},{"id":38239706,"identity":"0c74571d-58a0-4089-9c87-aa54523ef4d1","added_by":"auto","created_at":"2023-06-08 16:08:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1812443,"visible":true,"origin":"","legend":"\u003cp\u003eResponse period of ICI\u003c/p\u003e\n\u003cp\u003e(a) There was not significantly different between with or without high expression of PD-L1 (p=0.91). (b) There was not significant difference between with or without expression of PD-L1 (p=0.57).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3022315/v1/577a49a3cb5a813b76996c82.jpg"},{"id":42093383,"identity":"6cbaebb7-d869-4405-b5b9-0ce4b48c3d10","added_by":"auto","created_at":"2023-08-24 15:37:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":435927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3022315/v1/df55432c-81a0-47e3-8dad-2d88df6d471e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relative efficacies of EGFR-TKIs and immune checkpoint inhibitors for treatment of recurrent non-small cell lung cancer after surgery","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for more than 80% of all cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Treatment strategies for advanced NSCLC have changed over the past decade, and tumors with epidermal growth factor receptor (EGFR) gene mutations can be targeted therapeutically with tyrosine kinase inhibitors (TKIs). Several phase III studies in patients with advanced NSCLC harboring \u003cem\u003eEGFR\u003c/em\u003e mutations have shown significant improvements in response rates and prognosis following treatment with EGFR-TKIs compared with platinum-based chemotherapy [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, in addition to cytotoxic chemotherapy and targeted therapy for tumors harboring certain genetic aberrations, immunotherapy targeting immune checkpoints using antibodies to programmed cell death protein-1 (PD-1) and its ligand, PD-L1, has become an established treatment modality for NSCLC [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Immune checkpoint inhibitors (ICIs) have revealed greater efficacy than cytotoxic chemotherapy in patients with NSCLC whose tumors expressed PD-L1 on tumor cells and/or immune cells [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the relative efficacies of EGFR-TKIs and ICIs for the treatment of recurrent NSCLC after surgery remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we retrospectively evaluated the efficacies of EGFR-TKIs and ICIs in patients with recurrent NSCLC who underwent pulmonary resection.\u003c/p\u003e"},{"header":"2. Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eAmong 801 patients with NSCLC who underwent pulmonary resection at Kanazawa Medical University between 2017 and 2021, 64 patients had recurrence and were enrolled in this retrospective study. This study was conducted in accordance with the principles of the Declaration of Helsinki and the protocol was approved by the institutional review committee of Kanazawa Medical University (approval number: I392). All patients provided written informed consent.\u003c/p\u003e \u003cp\u003eClinical data including sex, age, smoking history, carcinoembryonic antigen, prognostic nutrition index (PNI), neutrophil-to-lymphocyte ratio (NLR), [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]F-fluorodeoxyglucose positron emission tomography/computed tomography maximum standardized uptake value (SUV\u003csub\u003emax\u003c/sub\u003e), and lobe involvement were collected. Respiratory function parameters including percent-predicted vital capacity (%VC) and forced expiratory volume in 1 s as a percentage of forced vital capacity (FEV\u003csub\u003e1\u003c/sub\u003e%) were also collected. Smoking history was assessed using the Brinkman index, which was calculated by multiplying the number of cigarettes smoked per day by the number of years that the patient had smoked [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Preoperative PNI, which has been reported as a prognostic factor in patients with NSCLC [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], was calculated by combining serum albumin levels with the total peripheral lymphocyte count in peripheral blood. NLR is used as an indicator of systemic inflammation and stress in critically ill surgical and medical patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and has also been reported to be a prognostic factor in patients with NSCLC who have undergone pulmonary resection [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The most effective chemotherapy regimen in each patient was classified as EGFR-TKI, ICI monotherapy or combined with a cytotoxic agent, and cytotoxic agent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Pathological factors\u003c/h2\u003e \u003cp\u003eData on histological type, lymphatic invasion, vascular invasion, differentiation, pathological stage, \u003cem\u003eEGFR\u003c/em\u003e mutation, and PD-L1 expression were collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e \u003cp\u003eFrequencies of variables were compared using Pearson\u0026rsquo;s χ\u003csup\u003e2\u003c/sup\u003e test of independence. Cumulative survival was calculated by the Kaplan\u0026ndash;Meier method, and survival curves were compared using the log-rank test. Cut-off values for factors associated with recurrence were calculated by receiver operating characteristic (ROC) curve analysis, and prognostic analyses were performed using these cut-off values. Significant factors affecting overall response were analyzed by logistic regression. Risk factors for overall survival after recurrence were analyzed by Cox proportional hazards regression. All statistical analyses were two-sided and the statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analyses were performed using JMP software v13.2 (SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient characteristics\u003c/h2\u003e \u003cp\u003eThe relationships between the clinicopathological characteristics of the 64 patients with recurrent NSCLC after surgery and their overall responses to chemotherapy are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The proportion of males (55.5% vs 100%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the Brinkman index (300 vs 800, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significantly lower among patients with a complete or partial response, compared with those with stable or progressive disease. The proportions of patients with adenocarcinoma (88.9% vs 42.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), lymphatic invasion (58.3% vs 32.1%, p\u0026thinsp;=\u0026thinsp;0.03), and positive \u003cem\u003eEGFR\u003c/em\u003e mutation (63.8% vs 3.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were also higher among those with a complete or partial response, compared with those with stable or progressive disease. The objective response rates are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The objective response rates were 100%, 38.7%, and 10.0% in patients receiving EGFR-TKIs (n\u0026thinsp;=\u0026thinsp;23), ICIs (n\u0026thinsp;=\u0026thinsp;31), and cytotoxic agents (n\u0026thinsp;=\u0026thinsp;10), respectively. Of those patients, 95% of patients received EGFR-TKIs on the first line, 84% for ICI, and 60% for cytotoxic agent.\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\u003eComparison of patient characteristic between complete response or partial response and stable disease or progressive disease.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR or PR (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD or PD (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male / female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 / 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 / 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median, range (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (34\u0026ndash;87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (52\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrinkman index, median, range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 (0\u0026ndash;2700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e800 (100\u0026ndash;2250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA, median, range (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4 (1.1\u0026ndash;70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (1.0\u0026ndash;100.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%VC, median, range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.2 (83.8-136.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.9 (64.9-129.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e%, median, range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.6 (44.8\u0026ndash;88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.6 (36.8\u0026ndash;92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI, median, range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0 (41.2\u0026ndash;61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.6 (336.6\u0026ndash;60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, median, range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.58 (1.12\u0026ndash;7.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60 (0.53\u0026ndash;13.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e, median, range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.91 (1.32\u0026ndash;23.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.94 (1.50\u0026ndash;22.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type (Ad / Sq / LCNEC / AdSq / Large)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 / 4 / 0 / 0 / 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 / 12 / 1 / 1 / 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobe (RU / RM / RL / LU / LL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 / 4 / 11 / 6 / 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 / 1 / 8 / 8 / 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLy (absent / present)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 / 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 / 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV (absent/present)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 / 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 / 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG (1 / 2 / 3 / 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 / 23 / 7 / 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 / 18 / 6 / 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epStage (IA / IB / IIA / IIB / IIIA / IIIB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 / 5 / 1 / 9 / 11 / 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 / 6 / 1 / 4 / 8 / 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epStage\u0026thinsp;\u0026ge;\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1, median, range (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5 (0\u0026ndash;95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (0\u0026ndash;95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive of EGFR mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (63.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffective regimen (ICI / EGFR-TKI / Cytotoxic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 / 23 / 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 / 0 / 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of effective regimen (1st / 2nd / 3rd / 4th )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 / 2 / 0 / 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 / 4 / 1/ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse free survival of effective regimen, median, range (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e731 (133\u0026ndash;1847)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (21\u0026ndash;1094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall survival after recurrence, median, range (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e861 (171\u0026ndash;1935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416 (43\u0026ndash;1188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCEA; carcinoembryonic antigen, %VC; % vital capacity, FEV\u003csub\u003e1\u003c/sub\u003e%; forced expiratory volume % in one second, PNI; prognostic nutrition index, NLR; neutrophil-to-lymphocyte ratio, SUV\u003csub\u003emax\u003c/sub\u003e; maximum of standardized uptake value, Ad; adenocarcinoma, Sq; squamous cell carcinoma, LCNEC; large cell neuroendocrine carcinoma, AdSq; adenosquamous cell carcinoma, Large; large cell carcinoma, RU; right upper, RM; right middle, RL; right lower, LU; left upper, LL; left lower, Ly; lymphatic invasion, V; vascular invasion, G; grade of differentiation, pStage; pathological stage, PD-L1; programmed death-ligand 1, EGFR; epithelial growth factor receptor, ICI; immune checkpoint inhibitor, TKI; tyrosine kinase inhibitor, Cytotoxic; cytotoxic agent.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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\u003eObjective response rate of chemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObjective response rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpithelial growth factor receptor-tyrosine kinase inhibitor (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune checkpoint inhibitor (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytotoxic agent (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate and multivariate analyses\u003c/h2\u003e \u003cp\u003eThe relationships between the clinicopathological characteristics and overall response to chemotherapy are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The following cut-off values for factors associated with recurrence were calculated using ROC curve analysis: age, 72 years; %VC, 90; FEV\u003csub\u003e1\u003c/sub\u003e%, 70; PNI, 47.55; NLR, 4.04; SUV\u003csub\u003emax\u003c/sub\u003e, 13.36; and PD-L1, 50. Univariate analysis identified Brinkman index (p\u0026thinsp;=\u0026thinsp;0.01), %VC (p\u0026thinsp;=\u0026thinsp;0.01), NLR (p\u0026thinsp;=\u0026thinsp;0.02), SUV\u003csub\u003emax\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.04), adenocarcinoma (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), lymphatic invasion (p\u0026thinsp;=\u0026thinsp;0.03), \u003cem\u003eEGFR\u003c/em\u003e mutation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and ICIs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) as significant factors affecting the overall response to chemotherapy. However, only %VC (odds ratio [OR]: 0.03, 95% confidence interval [CI]: 0.003\u0026ndash;0.78, p\u0026thinsp;=\u0026thinsp;0.03) and \u003cem\u003eEGFR\u003c/em\u003e mutation (OR: 681.40, 95% CI: 15.75\u0026ndash;29471.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significant factors in multivariate analysis, and ICI was not a significant factor (OR: 6.41, 95%CI: 0.48\u0026ndash;85.80, p\u0026thinsp;=\u0026thinsp;0.16). The relationships between the clinicopathological characteristics and overall survival after recurrence are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Univariate analysis identified adenocarcinoma (p\u0026thinsp;=\u0026thinsp;0.04) and \u003cem\u003eEGFR\u003c/em\u003e mutation (p\u0026thinsp;=\u0026thinsp;0.01) as risk factors for overall survival after recurrence; however, neither adenocarcinoma (HR: 0.54, 95%CI: 0.28\u0026ndash;1.66, p\u0026thinsp;=\u0026thinsp;0.28) nor \u003cem\u003eEGFR\u003c/em\u003e mutation (HR: 0.24, 95% CI: 0.05\u0026ndash;1.21, p\u0026thinsp;=\u0026thinsp;0.08) were risk factors in multivariate analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis and multivariate analysis of significant factors for complete response or partial response.\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI\u0026thinsp;\u0026ge;\u0026thinsp;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u0026ndash;31.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%VC\u0026thinsp;\u0026lt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026ndash;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e% \u0026lt; 70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u0026thinsp;\u0026lt;\u0026thinsp;47.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026ndash;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u0026thinsp;\u0026gt;\u0026thinsp;4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006-2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e \u0026gt; 13.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u0026ndash;4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.96\u0026ndash;38.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u0026ndash;50.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026ndash;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLy (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026ndash;8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u0026ndash;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026ndash;5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026ndash;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epStag\u0026thinsp;\u0026ge;\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u0026ndash;4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emEGFR (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.79-393.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e681.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.75-29471.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026ndash;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u0026ndash;85.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u0026ndash;23.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u0026ndash;53.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOR; odds ratio, CI; confidence interval, BI; Brinkman index, CEA; carcinoembryonic antigen, %VC; % vital capacity, FEV\u003csub\u003e1\u003c/sub\u003e%; forced expiratory volume % in one second, PNI; prognostic nutrition index, NLR; neutrophil-to-lymphocyte ratio, SUV\u003csub\u003emax\u003c/sub\u003e; maximum of standardized uptake value, Ad; adenocarcinoma, Ly; lymphatic invasion, V; vascular invasion, G; grade of differentiation, pStage; pathological stage, PD-L1; programmed death-ligand 1, EGFR; epithelial growth factor receptor, ICI; immune checkpoint inhibitor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis and multivariate analysis of risk factors for overall survival after recurrence.\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026ndash;10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026ndash;3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI\u0026thinsp;\u0026ge;\u0026thinsp;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60\u0026ndash;4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026ndash;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%VC\u0026thinsp;\u0026lt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e% \u0026lt; 70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u0026ndash;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u0026thinsp;\u0026lt;\u0026thinsp;47.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u0026ndash;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u0026thinsp;\u0026gt;\u0026thinsp;4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026ndash;6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e \u0026gt; 13.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026ndash;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026ndash;3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLy (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026ndash;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u0026ndash;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epStag\u0026thinsp;\u0026ge;\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026ndash;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026ndash;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026ndash;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u0026ndash;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026ndash;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eHR; hazard ratio, CI; confidence interval, BI; Brinkman index, CEA; carcinoembryonic antigen, %VC; % vital capacity, FEV\u003csub\u003e1\u003c/sub\u003e%; forced expiratory volume % in one second, PNI; prognostic nutrition index, NLR; neutrophil-to-lymphocyte ratio, SUV\u003csub\u003emax\u003c/sub\u003e; maximum of standardized uptake value, Ad; adenocarcinoma, Ly; lymphatic invasion, V; vascular invasion, G; grade of differentiation, pStage; pathological stage, PD-L1; programmed death-ligand 1, mEGFR; mutation of epithelial growth factor receptor, ICI; immune checkpoint inhibitor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Survival curves\u003c/h2\u003e \u003cp\u003eThe overall survival curves after recurrence according to chemotherapy regimen are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The 3-year overall survival rates after recurrence were 79.3% 69.5%, and 43.7% in patients receiving EGFR-TKIs, ICIs, and cytotoxic agents. There was no significant difference in overall survival between patients receiving EGFR-TKIs and ICIs (p\u0026thinsp;=\u0026thinsp;0.14) or between patients receiving ICIs and cytotoxic agents (p\u0026thinsp;=\u0026thinsp;0.23); however, overall survival was significantly higher in patients treated with EGFR-TKIs compared with those treated with cytotoxic agents (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The response periods according to the chemotherapy regimens are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The median response periods were 821, 232, and 250 days in patients receiving EGFR-TKIs, ICIs, and cytotoxic agents, respectively, and the respective probabilities of a 2-year response were 88.5%, 61.6%, and 25.9%. There was no significant difference in response periods between patients receiving EGFR-TKIs and ICIs (p\u0026thinsp;=\u0026thinsp;0.18), but the response periods were significantly higher in patients receiving EGFR-TKIs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) or ICIs (p\u0026thinsp;=\u0026thinsp;0.03) compared with those receiving cytotoxic agents. The results of a sub-analysis of the response periods in relation to PD-L1 expression in patients receiving ICIs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There was no significant difference in the probability of a 2-year response between patients with and without high expression levels of PD-L1 (p\u0026thinsp;=\u0026thinsp;0.91), or between patients with and without PD-L1 expression (p\u0026thinsp;=\u0026thinsp;0.57).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we evaluated and compared the efficacies of EGFR-TKIs and ICIs for the treatment of recurrent NSCLC in patients who underwent pulmonary resection. Although EGFR-TKIs have demonstrated significant improvements in response rates and prognosis in patients with advanced NSCLC harboring \u003cem\u003eEGFR\u003c/em\u003e mutations in several studies [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], EGFR-TKIs were also associated with significantly higher complete or partial response rates and a longer response period compared with cytotoxic agents in patients with recurrent NSCLC after surgery in this study. Although the overall survival curves suggested that EGFR-TKIs were significantly more effective than cytotoxic agents in patients with recurrent NSCLC after surgery, there was no significant difference between EGFR-TKIs and ICIs. ICIs have demonstrated efficacy in patients with advanced NSCLC [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the current study, the response period was significantly longer in patients treated with ICIs compared with cytotoxic agents, suggesting that ICIs may be an effective treatment for recurrent NSCLC after surgery. However, although the efficacy of ICIs has been reported to depend on PD-L1 expression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the response periods in the current study did not differ between patients with and without PD-L1 expression, suggesting that PD-L1 expression might not be a useful predictor of ICI response in patients with postoperative recurrence.\u003c/p\u003e \u003cp\u003eAdjuvant chemotherapy is currently preformed in patients with completely resected pathological stage II to IIIA NSCLC. Atezolizumab and osimertinib have also recently been approved as adjuvant chemotherapeutic agents, and were shown to significantly improve disease-free survival in patients receiving postoperative adjuvant chemotherapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, it cannot deny the possibility of administering unnecessary adjuvant chemotherapy even for cases that do not recurrence, and there is a risk that patients who relapse after adjuvant chemotherapy will have less choice.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, it was a retrospective study and may have included unobserved confounding and/or selection biases. Second, the study was performed at a single institution, and the study population was relatively small.\u003c/p\u003e \u003cp\u003eIn summary, our findings revealed that EGFR-TKIs and ICIs could provide effective treatment for patients with recurrent NSCLC after surgery. Although adjuvant chemotherapy for completely resected pathological stage II to IIIA NSCLC, atezolizumab or osimertinib, has also been recently approved as adjuvant chemotherapy, there is a risk that patients who relapse after adjuvant chemotherapy will have less choice.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Susan Furness, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was conducted in accordance with the amended Declaration of Helsinki. The Institutional Review Boards of Kanazawa Medical University approved the protocol (approval number: I392), and written informed consent was obtained from all of the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to [our institutional restrictions e.g., them containing information that could compromise research participant privacy/consent], but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has not been funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN. M. performed the research, collected and analyzed the data and wrote the paper. T.M., M.I., S. I., and Y.I. contributed to sample collection. H. U. contributed to supervision of this study and revision of the manuscript. All authors have read and approved the manuscript, and ensure that this is the case.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin 68 (2018) 7-30.\u003c/li\u003e\n \u003cli\u003eMok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 2009;361:947-957.\u003c/li\u003e\n \u003cli\u003eRosell R, Carcereny E, Gervais R, Vergnenegre A, Massuti B, Felip E, et al. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutiotion-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomized phase 3 trial. Lancet Oncol 2012;13:239-246.\u003c/li\u003e\n \u003cli\u003eWu YL, Zhou C, Hu CP, Feng J, Lu S, Huang Y, et al. Afatinib versus cisplatin plus gemcitabine for first-line treatment of Asian patients with advanced non-small-cell lung cancer harboning EGFR mutations (LUX-Lung 6): an open-label, randomized phase 3 trial. Lancet Oncol 2014;15:213-222.\u003c/li\u003e\n \u003cli\u003eSoria JC, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B, Lee KH, et al. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N Engl J Med 2018;378:113-125.\u003c/li\u003e\n \u003cli\u003eBrahmer J, Reckamp KL, Baas P, Crin\u0026ograve; L, Eberhardt WEE, Poddubskaya E, et al. Nivolumab versus docetaxel in advanced squamous-cell non\u0026ndash;small-cell lung cancer. N Engl J Med 2015;373:123-135.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus docetaxel in advanced nonsquamous non\u0026ndash;small-cell lung cancer. N Engl J Med 2015; 373:1627-1639.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGaron, EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, et al. Pembrolizumab for the treatment of non\u0026ndash;small-cell lung cancer. N Engl J Med 2015;372:2018-2028.\u003c/li\u003e\n \u003cli\u003eFehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J, Park K, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet 2016;387:1837-1846.\u003c/li\u003e\n \u003cli\u003eBrinkman GL, Coates EO. The effect of bronchitis, smoking, and occupation on ventilation. Am Rev Respir Dis 87 (1963) 684-693.\u003c/li\u003e\n \u003cli\u003eLi D, Yuan X, Liu J, Li C, Li W. Prognostic value of prognostic nutritional index in lung cancer: a meta-analysis. J Thorac Dis 10 (2018) 5298-5307.\u003c/li\u003e\n \u003cli\u003eQiu C, Qu X, Shen X, Zheng C, Zhu L, Meng L, et al. Evaluation of Prognostic Nutritional Index in Patients Undergoing Radical Surgery with Nonsmall Cell Lung \u003cem\u003eCancer. Nutr Cancer\u003c/em\u003e 67 (2015) 741-747.\u003c/li\u003e\n \u003cli\u003eZahorec R: Ratio of neutrophil to lymphocyte counts - rapid and simple parameter of systemic inflammation and stress in critically ill. Bratisl Lek Listy 102 (2001) 5-14\u003c/li\u003e\n \u003cli\u003eShimizu K, Okita R, Saisho S, Maeda A, Nojima Y, Nakata M. Preoperative neutrophil/lymphocyte ratio and prognostic nutritional index predict survival in patients with non-small cell lung cancer World J Surg Oncol 13 (2015) 291.\u003c/li\u003e\n \u003cli\u003eMizuguchi S, Izumi N, Tsukioka T, Komatsu H, Nishiyama N. Neutrophil-lymphocyte ratio predicts recurrence in patients with resected stage 1 non-small cell lung cancer. J Cardiothorac Surg 13 (2018) 78.\u003c/li\u003e\n \u003cli\u003eSiciliano MA, Carid\u0026aacute;\u0026nbsp;G, Ciliberto D,\u0026nbsp;ďApolito M, Pelaia C, Caracciolo D, et al. Efficacy and safety of first-line checkpoint inhibitors-based treatments for non-oncogene-addicted non-small-cell lung cancer: a systematic review and meta-analysis. ESMO Open 2022;7:100465.\u003c/li\u003e\n \u003cli\u003eWang Y, Han H, Zhang F, Lv T, Zhan P, Ye M, et al. Immune checkpoint inhibitors alone vs immune checkpoint inhibitors-combined chemotherapy for NSCLC patients with high PD-L1 expression: a network meta-analysis. Br J Cancer 2022;127:948-956.\u003c/li\u003e\n \u003cli\u003eWang C, Li J, Zhang Q, Wi J, Xiao Y, Song L, et al. The landscape of immune checkpoint inhibitor therapy in advanced lung cancer. BMC Cancer 2021;21:968.\u003c/li\u003e\n \u003cli\u003eMo DC, Huang JF, Luo PH, Huang SX, Wang HL. The efficacy and safety of combination therapy with immune checkpoint inhibitors in non-small cell lung cancer: a meta-analysis. Immunopharmacol 2021;96:107594.\u003c/li\u003e\n \u003cli\u003eFelip E, Altorki N, Zhou C, Csőszi T, Vynnychenko I, Goloborodko O, et al. Adjuvant atezolizumab after adjuvant chemotherapy in resected stage IB\u0026ndash;IIIA non-small-cell lung cancer (IMpower010): a randomised, multicentre, open-label, phase 3 trial. Lancet 2021; 398: 1344\u0026ndash;57.\u003c/li\u003e\n \u003cli\u003eWu YL, John T, Grohe C, Majem M, Goldman JW, Kim SW, et al. Postoperative chemotherapy use and outcomes from ADAURA: osimertinib as adjuvant therapy for resected EGFR-mutated NSCLC. J Thorac Oncol 2021;17:423-433.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"postoperative recurrence, chemotherapy, non-small cell lung cancer, epidermal growth factor receptor-tyrosine kinase inhibitor, immune checkpoint inhibiter","lastPublishedDoi":"10.21203/rs.3.rs-3022315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3022315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relative efficacies of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) and immune checkpoint inhibitors (ICIs) for the treatment of recurrent non-small cell lung cancer (NSCLC) after surgery remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAmong 801 patients with NSCLC who underwent pulmonary resection at Kanazawa Medical University between 2017 and 2021, 64 patients had recurrence. We retrospectively compared the efficacies of EGFR-TKIs and ICIs in these patients with recurrent NSCLC who underwent pulmonary resection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe 3-year overall survival rates after recurrence were 79.3% in patients who received EGFR-TKIs, 69.5% in patients who received ICIs, and 43.7% in patients who received cytotoxic agents. There was no significant difference in overall survival between patients treated with EGFR-TKIs and ICIs (p\u0026thinsp;=\u0026thinsp;0.14) or between patients treated with ICIs and cytotoxic agents (p\u0026thinsp;=\u0026thinsp;0.23), but overall survival was significantly higher in patients treated with EGFR-TKIs compared with cytotoxic agents (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) The probabilities of a 2-year response were 88.5%, 61.6%, and 25.9% in patients treated with EGFR-TKIs, ICIs, and cytotoxic agents, respectively. There was no significant difference in response periods between patients treated with EGFR-TKIs and ICIs (p\u0026thinsp;=\u0026thinsp;0.18), but the response period was significantly better in patients treated with EGFR-TKIs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) or ICIs (p\u0026thinsp;=\u0026thinsp;0.03) compared with cytotoxic agents. Percent-predicted vital capacity (p\u0026thinsp;=\u0026thinsp;0.03) and epidermal growth factor receptor gene mutation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significant factors affecting the overall response to chemotherapy in multivariate analysis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEGFR-TKIs and ICIs are effective for treating recurrent NSCLC after surgery. Although adjuvant chemotherapy for completely resected pathological stage II to IIIA NSCLC, atezolizumab or osimertinib, has also been recently approved as adjuvant chemotherapy, there is a risk that patients who relapse after adjuvant chemotherapy will have less choice.\u003c/p\u003e","manuscriptTitle":"Relative efficacies of EGFR-TKIs and immune checkpoint inhibitors for treatment of recurrent non-small cell lung cancer after surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-08 16:08:25","doi":"10.21203/rs.3.rs-3022315/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f8408de-85ba-45a7-a6ed-c95cea0db174","owner":[],"postedDate":"June 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-24T15:29:24+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-08 16:08:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3022315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3022315","identity":"rs-3022315","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}