{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:11:32Z","timestamp":1788779492632,"version":"build-2803163510"},"reference-count":80,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2023ZD0501700"],"award-info":[{"award-number":["2023ZD0501700"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>With the accelerating aging of the population, the number of elderly patients with lung cancer continues to increase. Although surgery remains an important treatment modality for lung cancer, early recurrence (ER) is still a major factor affecting long-term survival in elderly patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to integrate peripheral blood T-lymphocyte subset indicators with clinicopathological characteristics to develop and externally validate a machine learning-based prediction model for ER in elderly patients with lung cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study was designed as a multicenter retrospective cohort study. ER was defined as local recurrence or distant metastasis within 2 years after surgery. A total of 23 candidate predictors were included, and dual feature selection was performed using the Boruta algorithm and LASSO regression, followed by a systematic comparison of 14 machine learning algorithms and 7 ensemble optimization strategies. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), calibration curve, Brier score, and decision curve analysis. SHAP was used to interpret the optimal model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 893 elderly patients with lung cancer were included in this study. After dual feature selection, five predictors were ultimately retained: tumor stage, total T-lymphocyte percentage, body mass index (BMI), Karnofsky performance scale (KPS), and suppressor\/cytotoxic T-lymphocyte percentage. After ensemble optimization, SMOTE-Stacking achieved the best performance in internal validation. In external validation, among the four models belonging to the statistically equivalent top-performing tier, Soft Voting Ensemble (SV) demonstrated the most robust performance. SHAP analysis indicated that tumor stage and total T-lymphocyte percentage were the two core drivers of model prediction, both exhibiting clear nonlinear threshold effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ER in elderly patients with lung cancer results from the combined effects of tumor burden, host functional status, and T-lymphocyte immune imbalance. A machine learning model integrating peripheral blood T-lymphocyte subsets has noninvasive and clinically translational potential and may provide evidence for ER risk stratification, individualized follow-up, and early intervention decision-making in elderly patients with lung cancer.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1948080","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T10:44:26Z","timestamp":1788777866000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Peripheral blood T-lymphocyte subsets provide prediction of early postoperative recurrence in elderly patients with lung cancer: model development and external validation"],"prefix":"10.3389","volume":"16","author":[{"given":"Hongtai","family":"Xiong","sequence":"first","affiliation":[{"name":"Department of Oncology, Guang\u2019anmen Hospital, China Academy of Chinese Medical Sciences","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuming","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Oncology, Guang\u2019anmen Hospital, China Academy of Chinese Medical Sciences","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyuan","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Oncology, Guang\u2019anmen Hospital, China Academy of Chinese Medical Sciences","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiyi","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Oncology, Guang\u2019anmen Hospital, China Academy of Chinese Medical Sciences","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Lei","sequence":"additional","affiliation":[{"name":"Center of Integrated Traditional Chinese and Western Medicine Oncology, Sun Yat-sen University Cancer Center Gansu Hospital","place":["Lanzhou, Gansu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhizheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Oncology, Guang\u2019anmen Hospital, China Academy of Chinese Medical Sciences","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honggang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Oncology, Guang\u2019anmen Hospital, China Academy of Chinese Medical Sciences","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3322\/caac.21820","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1038\/s43587-025-00999-8","article-title":"Adapting health, economic and social policies to address population aging in China","volume":"5","author":"Fang","year":"2025","journal-title":"Nat Aging"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.canep.2025.102760","article-title":"Global epidemiological trends of trachea, bronchus, and lung cancer in the elderly","volume":"95","author":"Chen","year":"2025","journal-title":"Cancer Epidemiol"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejcts.2011.05.038","article-title":"Is it safe to include octogenarians at the start of a video-assisted thoracic surgery lobectomy programme","volume":"41","author":"Amer","year":"2012","journal-title":"Eur J Cardiothorac Surg"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.4103\/jcrt.jcrt_973_23","article-title":"Surgery or radiotherapy improves survival in elderly patients with early non-small cell lung cancer: A population-based analysis","volume":"20","author":"Shi","year":"2024","journal-title":"J Cancer Res Ther"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1093\/icvts\/ivs450","article-title":"Post-recurrence survival of patients with non-small-cell lung cancer after curative resection with or without induction\/adjuvant chemotherapy","volume":"16","author":"Saisho","year":"2013","journal-title":"Interact Cardiovasc Thorac Surg"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djv059","article-title":"Lung cancer prognosis before and after recurrence in a population-based setting","volume":"107","author":"Consonni","year":"2015","journal-title":"J Natl Cancer Inst"},{"key":"B9","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.cger.2015.08.008","article-title":"Management of lung cancer in the elderly","volume":"32","author":"Gajra","year":"2016","journal-title":"Clin Geriatr Med"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2012.03.031","article-title":"Tumor recurrence after complete resection for non-small cell lung cancer","volume":"93","author":"Taylor","year":"2012","journal-title":"Ann Thorac Surg"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1007\/s00268-020-05612-0","article-title":"Inflammatory risk factors for early recurrence of non-small cell lung cancer within one year following curative resection","volume":"44","author":"Kobayashi","year":"2020","journal-title":"World J Surg"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.21873\/anticanres.14989","article-title":"Tumor size and N2 lymph node metastasis are significant risk factors for early recurrence in completely resected centrally located primary lung cancer patients","volume":"41","author":"Tsukioka","year":"2021","journal-title":"Anticancer Res"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1630663","article-title":"Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning","volume":"15","author":"Zheng","year":"2025","journal-title":"Front Oncol"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1016\/s0169-5002(02)00100-9","article-title":"Immunologic parameters as significant prognostic factors in lung cancer","volume":"37","author":"Nakamura","year":"2002","journal-title":"Lung Cancer"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.680287","article-title":"Immune microenvironment signatures as biomarkers to predict early recurrence of stage ia-b lung cancer","volume":"11","author":"Wang","year":"2021","journal-title":"Front Oncol"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1642829","article-title":"The application of peripheral blood immune profiling in personalized treatment of locally advanced and advanced lung cancer: a nomogram approach","volume":"15","author":"Yin","year":"2025","journal-title":"Front Oncol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1548234","article-title":"Deciphering T-cell exhaustion in the tumor microenvironment: paving the way for innovative solid tumor therapies","volume":"16","author":"Nair","year":"2025","journal-title":"Front Immunol"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.20643","article-title":"Phenotype and function of human T lymphocyte subsets: consensus and issues","volume":"73","author":"Appay","year":"2008","journal-title":"Cytometry A"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1186\/s12575-023-00215-0","article-title":"Full spectrum flow cytometry-powered comprehensive analysis of PBMC as biomarkers for immunotherapy in NSCLC with EGFR-TKI resistance","volume":"25","author":"Zhou","year":"2023","journal-title":"Biol Proced Online"},{"key":"B20","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1186\/s12938-025-01464-8","article-title":"The predictive value of circulating lymphocyte subpopulation characteristics for the prognosis of patients with stage III-IV non-small cell lung cancer treated with EGFR-TKI","volume":"24","author":"Han","year":"2025","journal-title":"BioMed Eng Online"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1111\/jcmm.17214","article-title":"Lymphocyte percentage as a valuable predictor of prognosis in lung cancer","volume":"26","author":"Huang","year":"2022","journal-title":"J Cell Mol Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"250","DOI":"10.21037\/atm-21-6347","article-title":"A validated nomogram integrating baseline peripheral T-lymphocyte subsets and NK cells for predicting survival in stage I-IIIA non-small cell lung cancer after resection","volume":"10","author":"Xu","year":"2022","journal-title":"Ann Transl Med"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1892","DOI":"10.3390\/cancers17111892","article-title":"Effectiveness of artificial intelligence models in predicting lung cancer recurrence: A gene biomarker-driven review","volume":"17","author":"Pourakbar","year":"2025","journal-title":"Cancers (Basel)"},{"key":"B24","doi-asserted-by":"publisher","first-page":"3985","DOI":"10.3390\/cancers17243985","article-title":"Artificial intelligence and machine learning in lung cancer: advances in imaging, detection, and prognosis","volume":"17","author":"Arshad","year":"2025","journal-title":"Cancers (Basel)"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2024.22530","article-title":"The 2024 revision to the declaration of helsinki: modern ethics for medical research","volume":"333","author":"Bibbins-Domingo","year":"2025","journal-title":"JAMA"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.21037\/jtd-2025-1211","article-title":"CT-based body composition and inflammatory nutritional biomarker nomogram for predicting early postoperative recurrence of non-small cell lung cancer: a multicenter study","volume":"17","author":"Zou","year":"2025","journal-title":"J Thorac Dis"},{"key":"B29","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1016\/j.jtcvs.2013.05.041","article-title":"Local recurrence after surgery for non-small cell lung cancer: a recursive partitioning analysis of multi-institutional data","volume":"146","author":"Kelsey","year":"2013","journal-title":"J Thorac Cardiovasc Surg"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr-22-148","article-title":"Prognosis and recurrence patterns in patients with early stage lung cancer: a multi-state model approach","volume":"11","author":"Jeong","year":"2022","journal-title":"Transl Lung Cancer Res"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.34994","article-title":"Multiple primary lung cancer: Updates and perspectives","volume":"155","author":"Wang","year":"2024","journal-title":"Int J Cancer"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7992","article-title":"Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes","volume":"38","author":"Riley","year":"2019","journal-title":"Stat Med"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9025","article-title":"Minimum sample size for external validation of a clinical prediction model with a binary outcome","volume":"40","author":"Riley","year":"2021","journal-title":"Stat Med"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.3758\/s13428-023-02117-1","article-title":"Solving the many-variables problem in MICE with principal component regression","volume":"56","author":"Costantini","year":"2024","journal-title":"Behav Res Methods"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2006.01.009","article-title":"Using the outcome for imputation of missing predictor values was preferred","volume":"59","author":"Moons","year":"2006","journal-title":"J Clin Epidemiol"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocac002","article-title":"Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review","volume":"29","author":"Yang","year":"2022","journal-title":"J Am Med Inform Assoc"},{"key":"B38","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.jclinepi.2022.11.015","article-title":"Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models","volume":"154","author":"Andaur Navarro","year":"2023","journal-title":"J Clin Epidemiol"},{"key":"B39","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1093\/bib\/bbx124","article-title":"Evaluation of variable selection methods for random forests and omics data sets","volume":"20","author":"Degenhardt","year":"2019","journal-title":"Brief Bioinform"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6782","article-title":"Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events","volume":"35","author":"Pavlou","year":"2016","journal-title":"Stat Med"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200410135","article-title":"Estimation of the Youden Index and its associated cutoff point","volume":"47","author":"Fluss","year":"2005","journal-title":"Biom J"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2024.1430245","article-title":"A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems","volume":"6","author":"Yang","year":"2024","journal-title":"Front Digit Health"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1007\/s10654-018-0390-z","article-title":"Stacked generalization: an introduction to super learning","volume":"33","author":"Naimi","year":"2018","journal-title":"Eur J Epidemiol"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PLoS One"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models: a framework for traditional and novel measures","volume":"21","author":"Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"B48","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1705.07874","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1016\/s0895-4356(96)00236-3","article-title":"A simulation study of the number of events per variable in logistic regression analysis","volume":"49","author":"Peduzzi","year":"1996","journal-title":"J Clin Epidemiol"},{"key":"B51","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1186\/s12885-025-14363-x","article-title":"Tracheal, bronchus, and lung cancer among older adults: thirty-year global burden trends, precision medicine breakthroughs, and lingering barriers","volume":"25","author":"Xing","year":"2025","journal-title":"BMC Cancer"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.21037\/jtd.2019.04.01","article-title":"Treatment strategy and decision-making for elderly surgical candidates with early lung cancer","volume":"11","author":"Okami","year":"2019","journal-title":"J Thorac Dis"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1016\/j.cllc.2020.02.011","article-title":"Hazards of recurrence, second primary, or other tumor at ten years after surgery for non-small-cell lung cancer","volume":"21","author":"Fink-Neuboeck","year":"2020","journal-title":"Clin Lung Cancer"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.1097\/JTO.0b013e31824a9022","article-title":"Recurrence dynamics for non-small-cell lung cancer: effect of surgery on the development of metastases","volume":"7","author":"Demicheli","year":"2012","journal-title":"J Thorac Oncol"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.1007\/s10147-022-02143-7","article-title":"A prospective observational study of postoperative adjuvant chemotherapy for non-small cell lung cancer in elderly patients (>\/= 75 years)","volume":"27","author":"Yano","year":"2022","journal-title":"Int J Clin Oncol"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.4103\/0366-6999.147828","article-title":"Prognostic significance of tumor-infiltrating CD8(+) or CD3(+) T lymphocytes and interleukin-2 expression in radically resected non-small cell lung cancer","volume":"128","author":"Tian","year":"2015","journal-title":"Chin Med J (Engl)"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0242173","article-title":"Prognostic value of the common tumour-infiltrating lymphocyte subtypes for patients with non-small cell lung cancer: a meta-analysis","volume":"15","author":"Chen","year":"2020","journal-title":"PLoS One"},{"key":"B58","doi-asserted-by":"publisher","first-page":"32017","DOI":"10.1038\/s41598-025-17735-2","article-title":"T lymphocyte subsets and mitochondrial index predict outcomes in advanced non small cell lung cancer therapy","volume":"15","author":"Huang","year":"2025","journal-title":"Sci Rep"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccm.2020.02.006","article-title":"Lung cancer staging","volume":"41","author":"Tanoue","year":"2020","journal-title":"Clin Chest Med"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.1055\/s-0029-1185734","article-title":"Early recurrence after surgical resection in patients with pathological stage I non-small cell lung cancer","volume":"57","author":"Kawachi","year":"2009","journal-title":"Thorac Cardiovasc Surg"},{"key":"B61","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1007\/s12672-025-02514-2","article-title":"Prediction model for the early recurrence of stage IA-IIA non-small cell lung cancer based on hematological indexes and imaging features","volume":"16","author":"Zhao","year":"2025","journal-title":"Discov Oncol"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2014.05.061","article-title":"Predictors of early recurrence for node-negative t1 to t2b non-small cell lung cancer","volume":"98","author":"Kiankhooy","year":"2014","journal-title":"Ann Thorac Surg"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.2147\/CMAR.S337830","article-title":"Predictive risk factors for early recurrence of stage pIIIA-N2 non-small cell lung cancer","volume":"13","author":"Yu","year":"2021","journal-title":"Cancer Manag Res"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.5761\/atcs.oa.17-00215","article-title":"Loss of muscle mass is a novel predictor of postoperative early recurrence in N2-positive non-small-cell lung cancer","volume":"24","author":"Tsukioka","year":"2018","journal-title":"Ann Thorac Cardiovasc Surg"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejcts.2009.08.005","article-title":"Vascular endothelial growth factor C: the predicator of early recurrence in patients with N2 non-small-cell lung cancer","volume":"37","author":"Chen","year":"2010","journal-title":"Eur J Cardiothorac Surg"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1016\/j.lungcan.2020.09.011","article-title":"Impact of the preoperative body mass index on the postoperative outcomes in patients with completely resected non-small cell lung cancer: a retrospective analysis of 16,503 cases in a Japanese Lung Cancer Registry Study","volume":"149","author":"Fukumoto","year":"2020","journal-title":"Lung Cancer"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.1097\/XCS.0000000000001360","article-title":"Postoperative sarcopenia and association with recurrence in resected early-stage non-small cell cancer","volume":"241","author":"Khan","year":"2025","journal-title":"J Am Coll Surg"},{"key":"B68","doi-asserted-by":"publisher","DOI":"10.1016\/s0360-3016(01)02741-9","article-title":"Comorbidity and KPS are independent prognostic factors in stage I non-small-cell lung cancer","volume":"52","author":"Firat","year":"2002","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B69","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.12913","article-title":"Prognostic factors and outcome of surgically treated patients with brain metastases of non-small cell lung cancer","volume":"10","author":"She","year":"2019","journal-title":"Thorac Cancer"},{"key":"B70","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2019.01719","article-title":"T cell dysfunction in cancer immunity and immunotherapy","volume":"10","author":"Xia","year":"2019","journal-title":"Front Immunol"},{"key":"B71","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1038\/s41568-023-00560-y","article-title":"Dynamics and specificities of T cells in cancer immunotherapy","volume":"23","author":"Oliveira","year":"2023","journal-title":"Nat Rev Cancer"},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.7150\/ijms.56928","article-title":"The relationship between lymphocyte subsets and the prognosis and genomic features of lung cancer: a retrospective study","volume":"18","author":"Dai","year":"2021","journal-title":"Int J Med Sci"},{"key":"B73","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1562188","article-title":"GammadeltaT cells, a key subset of T cell for cancer immunotherapy","volume":"16","author":"Lv","year":"2025","journal-title":"Front Immunol"},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.1159\/000438523","article-title":"Prognostic role of tumor-infiltrating lymphocytes in lung cancer: a meta-analysis","volume":"37","author":"Geng","year":"2015","journal-title":"Cell Physiol Biochem"},{"key":"B75","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.ejca.2017.08.026","article-title":"Determination of poor prognostic immune features of tumour microenvironment in non-smoking patients with lung adenocarcinoma","volume":"86","author":"Kinoshita","year":"2017","journal-title":"Eur J Cancer"},{"key":"B76","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2021-002356","article-title":"Massive PD-L1 and CD8 double positive TILs characterize an immunosuppressive microenvironment with high mutational burden in lung cancer","volume":"9","author":"Zhang","year":"2021","journal-title":"J Immunother Cancer"},{"key":"B77","doi-asserted-by":"publisher","DOI":"10.1038\/s41577-021-00537-8","article-title":"CD8(+) T cell metabolism in infection and cancer","volume":"21","author":"Reina-Campos","year":"2021","journal-title":"Nat Rev Immunol"},{"key":"B78","doi-asserted-by":"publisher","first-page":"2810","DOI":"10.3390\/ijms20112810","article-title":"The evolving role of CD8(+)CD28(-) immunosenescent T cells in cancer immunology","volume":"20","author":"Huff","year":"2019","journal-title":"Int J Mol Sci"},{"key":"B79","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1186\/s12967-025-06291-y","article-title":"New insights on anti-tumor immunity of CD8(+) T cells: cancer stem cells, tumor immune microenvironment and immunotherapy","volume":"23","author":"Lin","year":"2025","journal-title":"J Transl Med"},{"key":"B80","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1146\/annurev-immunol-090222-110914","article-title":"T cell exhaustion","volume":"42","author":"Baessler","year":"2024","journal-title":"Annu Rev Immunol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1948080\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T10:44:27Z","timestamp":1788777867000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1948080\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,7]]},"references-count":80,"alternative-id":["10.3389\/fonc.2026.1948080"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1948080","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,7]]},"article-number":"1948080"}}