[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122055-en":3,"doc-seo-122055-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122055,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine learning-based prediction of 5-year survival in elderly NSCLC patients using oxidative stress markers","Oxidative stress is central to aging and cancer, yet its prognostic value in elderly non-small cell lung cancer (NSCLC) remains insufficiently examined using machine learning. This research built an oxidative stress score (OSS) from six systemic biomarkers and used decision tree (DT), random forest (RF), and support vector machine (SVM) models to predict 5-year overall survival after radical resection. Data included training, internal validation, and external validation cohorts, with RF showing the best discrimination and calibration. Results support an OSS-based machine-learning prediction tool for elderly NSCLC prognosis.","TYPE Original Research PUBLISHED 24 October 2024 DOI 10.3389/fonc.2024.1482374  \nOPEN ACCESS  \nEDITED BY  \nMohamed Rahouma,  \nNewYork-Presbyterian, United States  \nREVIEWED BY  \nJinwei Zhang,  \nChinese Academy of Sciences (CAS), China Duilio Divisi,  \nUniversity of L’Aquila, Italy  \n*CORRESPONDENCE  \nBindong Xu  \n[xubd2002@163.com](xubd2002@163.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 18 August 2024  \nACCEPTED 24 September 2024  \nPUBLISHED 24 October 2024  \nCITATION  \nChen H, Xu J, Zhang Q, Chen P, Liu Q, Guo Land Xu B (2024) Machine learning-based prediction of 5-year survival in elderly NSCLC patients using oxidative stress markers.  \nFront. Oncol. 14:1482374 .  \ndoi: 10.3389/fonc.2024.1482374  \nCOPYRIGHT  \n© 2024 Chen, Xu, Zhang, Chen, Liu, Guo and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based prediction of 5-year survival in elderly NSCLC patients using oxidative stress markers  \nHao Chen 1†, Jiangjiang Xu 2†, Qiang Zhang 1, Pengfei Chen 1, Qiuxia Liu 1, Lianyi Guo 3 and Bindong Xu 1*  \n1 Department of Thoracic and Cardiovascular Surgery of the Affiliated Hospital of Putian University, Putian, Fujian, China, 2 Fuding Hospital, Fujian University of Traditional Chinese Medicine, Fuding, Fujian, China, 3 Department of Gastroenterology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China  \nBackground: Oxidative stress plays a significant role in aging and cancer, yet there is currently a lack of research utilizing machine learning models to examine the relationship between oxidative stress and prognosis in elderly non-small cell lung cancer (NSCLC) patients.  \nMethods: This study included elderly NSCLC patients who underwent radical lung cancer resection from January 2012 to April 2018, exploring the relationship between Oxidative Stress Score (OSS) and prognosis. Machine learning techniques, including Decision Trees (DT), Random Forest (RF), and Support Vector Machine (SVM), were employed to develop predictive models for 5-year overall survival (OS) .  \nResults: The datasets consisted of 1647 patients in the training set, 705 in the internal validation set, and 516 in the external validation set. An OSS was formulated from six systemic oxidative stress biomarkers, such as albumin, total bilirubin, and blood urea nitrogen, among others. Boruta variable importance analysis identified low OSS as a key indicator of poor prognosis. The OSS was subsequently integrated into the DT, RF, and SVM models for training. These models, optimized through hyperparameter tuning on the training set, were then evaluated on the internal and external validation sets. The RF model demonstrated the highest predictive performance, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.794 in the internal validation set, compared to AUCs of 0.711 and 0.760 for the DT and SVM models, respectively. Similarly, in the external validation set, the RF model achieved an AUC of 0.784, outperforming the DT and SVM models, which had AUCs of 0.699 and 0.730, respectively. Calibration plots confirmed the RF model’s superior calibration, followed by the SVM model, with the DT model performing the poorest.  \nConclusion: The OSS-based clinical prediction model, constructed using machine learning methodologies, effectively predicts the prognosis of elderly NSCLC patients post-radical surgery.  \nKEYWORDS  \nelderly, NSCLC, oxidative stress, machine learning, overall survival  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  ","cbCaivW2Zm6R303b","https://ap.wps.com/l/cbCaivW2Zm6R303b","pdf",1062377,1,10,"English","en",105,"# Introduction\n# Methods\n# Results\n## Model performance\n# Conclusion","[{\"question\":\"What was the study’s main objective for elderly NSCLC patients?\",\"answer\":\"To examine the relationship between oxidative stress and prognosis and to build a machine learning model predicting 5-year overall survival in elderly NSCLC after radical surgery.\"},{\"question\":\"How was the oxidative stress score (OSS) constructed in this study?\",\"answer\":\"The OSS was formulated from six systemic oxidative stress biomarkers, including albumin, total bilirubin, and blood urea nitrogen, among others.\"},{\"question\":\"Which machine learning model performed best and how was it validated?\",\"answer\":\"The random forest (RF) model achieved the highest predictive performance, with AUCs reported in internal and external validation sets, and calibration plots confirmed superior calibration compared with SVM and DT.\"}]","Machine learning-based prediction of 5-year survival in elderly NSCLC patients using oxidative stress markers | PDF",1785808589,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-prediction-of-5-year-survival-in-elderly-nsclc-patients-using-oxidative-stress-markers","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-prediction-of-5-year-survival-in-elderly-nsclc-patients-using-oxidative-stress-markers/122055/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the study’s main objective for elderly NSCLC patients?","Question",{"text":75,"@type":76},"To examine the relationship between oxidative stress and prognosis and to build a machine learning model predicting 5-year overall survival in elderly NSCLC after radical surgery.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the oxidative stress score (OSS) constructed in this study?",{"text":80,"@type":76},"The OSS was formulated from six systemic oxidative stress biomarkers, including albumin, total bilirubin, and blood urea nitrogen, among others.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and how was it validated?",{"text":84,"@type":76},"The random forest (RF) model achieved the highest predictive performance, with AUCs reported in internal and external validation sets, and calibration plots confirmed superior calibration compared with SVM and DT.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]