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A study built an NSCLC diagnostic model using multidimensional datasets, combining machine learning feature selection and classification to evaluate the diagnostic value of blood protein metabolic profiles and support a non-invasive clinical approach. LightGBM achieved superior performance using serum protein metabolism indicators and demographic variables.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/integration-of-blood-protein-metabolic-profiles-via-machine-learning-to-enable-the-accurate-early-detection-of-non-small-cell-lung-cancer/363681/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/integration-of-blood-protein-metabolic-profiles-via-machine-learning-to-enable-the-accurate-early-detection-of-non-small-cell-lung-cancer/363681.png","ImageObject",300,407,{"name":92,"@type":93},"Mary Man","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the primary goal of the study?","Question",{"text":112,"@type":113},"To construct a machine learning-based NSCLC diagnostic model using multidimensional data and blood protein metabolic profiles for accurate early detection and differentiation from other respiratory diseases.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were participants categorized for model development?",{"text":117,"@type":113},"The study recruited 144 lung cancer patients, 132 healthy controls, and 130 patients with other respiratory diseases from three medical centers.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning model performed best for early detection of NSCLC?",{"text":121,"@type":113},"LightGBM outperformed logistic regression, SVM, and ANN, achieving the highest performance metrics, including an AUC of 0.867 and an F1 score of 0.807.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},363681,1790273723,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},7421720224475,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Shi et al. Respiratory Research (2026) 27:249 [https://doi.org/10.1186/s12931-026-03686-3](https://doi.org/10.1186/s12931-026-03686-3)  \nRespiratory Research  \nRESEARCH Open Access  \nIntegration of blood protein-metabolic  profiles via machine learning to enable the accurate early detection of non-small cell lung cancer  \nHuiYing Shi1†, JiaMing Sun2†, HuiMin Wei3, Jun Tang4, XiaoLei Zhou1, HongSa Li5, JiongRan Wang6, LiFeng Li7,8, Fang Sun1*, JunWei Zhao9* and ShuLin Zhang10,11*  \nAbstract  \nBackground Non-small cell lung cancer (NSCLC) poses a major threat to human health due to its high morbidity and mortality. Early accurate diagnosis and differential diagnosis from other respiratory diseases, are pivotal to improving patient prognosis. This study aimed to construct an NSCLC diagnostic model based on multidimensional datasets by leveraging machine learning (ML) driven feature selection and classification algorithms, clarifying the diagnostic value of blood protein metabolic profiles, and provide a novel non-invasive diagnostic scheme for clinical practice.  \nMethods A total of 144 lung cancer patients (LC), 132 healthy controls (HCs), and 130 patients with other respiratory diseases (ORDs) were recruited from three medical centers. A panel of 26 serum protein metabolism indicatorsand demographic variables was included as candidate features. Five feature-selection algorithms were used to identify core variables from the high-dimensional data. Ten ML classifiers were subsequently constructed, and  \ntheir performance was comprehensively evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, positive precision, negative precision, positive recall, negative recall, F1 score, and Cohen’s kappa. Moreover, SHapley Additive exPlanations (SHAP) analysis was performed to decipher key predictive factors.  \nResults Significant differences were observed in 21 serum protein metabolic indicators across the LC, HC, and ORD cohorts (p \u003C 0. 05) . Among the tested algorithms, LightGBM emerged as the superior model for early detection of NSCLC, outperforming logistic regression, SVM, and ANN. It yielded the highest performance metrics, including an AUC of 0 . 867, positive recall of 0 . 977, and F1 score of 0.807. Notably, the model minimized both missed and false-  \n†HuiYing Shi and JiaMing Sun contributed equally to this work.  \n*Correspondence: Fang Sun[sfhnxkyy@163.com](sfhnxkyy@163.com)[ ](sfhnxkyy@163.com)JunWei Zhao  \n[junweizhao@alumni.sjtu.edu.cn](junweizhao@alumni.sjtu.edu.cn)[ ](junweizhao@alumni.sjtu.edu.cn)ShuLin Zhang [shulinzhang@fudan.edu.cn](shulinzhang@fudan.edu.cn)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/licenses/by-nc-nd/4.0/](vecommons.org/licenses/by-nc-nd/4.0/.)[.](vecommons.org/licenses/by-nc-nd/4.0/.)  \nShi et al. Respiratory Research (2026) 27:249 Page 2 of 13  \npositive diagnoses-a crucial factor for clinical uti","cbCaicpPlk3CCFK4","https://ap.wps.com/l/cbCaicpPlk3CCFK4","pdf",3570195,13,"English","# Abstract\n# Methods\n# Results\n# Conclusions\n# Keywords\n# Introduction","[{\"question\":\"What is the primary goal of the study?\",\"answer\":\"To construct a machine learning-based NSCLC diagnostic model using multidimensional data and blood protein metabolic profiles for accurate early detection and differentiation from other respiratory diseases.\"},{\"question\":\"How were participants categorized for model development?\",\"answer\":\"The study recruited 144 lung cancer patients, 132 healthy controls, and 130 patients with other respiratory diseases from three medical centers.\"},{\"question\":\"Which machine learning model performed best for early detection of NSCLC?\",\"answer\":\"LightGBM outperformed logistic regression, SVM, and ANN, achieving the highest performance metrics, including an AUC of 0.867 and an F1 score of 0.807.\"}]","Integration of blood protein-metabolic profiles via machine learning to enable the accurate early detection of non-small cell lung cancer | PDF",1790152628,33]