[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125388-en":3,"doc-seo-125388-105":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},125388,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Machine learning assisted breathomic approach for early-stage thoracic cancer detection - Original Research","This original research evaluates the feasibility of a non-invasive diagnostic strategy using breathomics biomarkers interpreted through machine learning to separate benign from malignant thoracic lesions. Exhaled breath samples were analyzed by thermal desorption-gas chromatography-mass spectrometry, and a logistic regression model was trained on 80 cases and validated on 52 samples. A 13-VOC model distinguished benign versus malignant disease with an AUC of 0.85, and sensitivity surpassed a 4-serum tumor marker panel.","TYPE Original Research PUBLISHED 17 September 2025 DOI 10.3389/fonc.2025.1635280  \nOPEN ACCESS  \nEDITED BY  \nMichael N. Kammer,  \nUniversite´ Toulouse 1 Capitole, France  \nREVIEWED BY  \nYuanpin Zhou,  \nZhejiang University, China Qing-Qing Yu,  \nJining First People’s Hospital, China  \n*CORRESPONDENCE  \nZhenguang Chen  \n [chzheng@mail.sysu.edu.cn](chzheng@mail.sysu.edu.cn)[ ](chzheng@mail.sysu.edu.cn)Junqi Wang  \n[junqi.wang@chromxhealth.com](junqi.wang@chromxhealth.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 26 May 2025  \nACCEPTED 25 August 2025  \nPUBLISHED 17 September 2025  \nCITATION  \nChen Z, Peng M, Fan P, Chen S, Cheng X, Xu B, Chen R, Hu X, Wei W, Zhao T, Kong J, Liang W, Qiu X, Chen S and Wang J (2025) Machine learning assisted breathomic  \napproach for early-stage thoracic cancer detection.  \nFront. Oncol. 15:1635280 .  \ndoi: 10.3389/fonc.2025.1635280  \nCOPYRIGHT  \n© 2025 Chen, Peng, Fan, Chen, Cheng, Xu, Chen, Hu, Wei, Zhao, Kong, Liang, Qiu, Chen and Wang. 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 assisted breathomic approach for early-stage thoracic cancer detection  \nZhenguang Chen 1*†, Minhua Peng 2†, Pengnan Fan 2, Sai Chen 3, Xinxin Cheng 4, Bo Xu 1, Ruiping Chen 1, Xiao Hu 5, Wei Wei 5, Tingting Zhao 5, Jun Kong 2, Weiliang Liang 2, Xiangcheng Qiu 2, Sitong Chen 2 and Junqi Wang 2,6*  \n1 Department of Thoracic Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China, 2ChromX Health Co., Ltd., Guangzhou, Guangdong, China, 3Center for Private Medical Service & Healthcare, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China, 4State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China, 5 Department of Thoracic Surgery, Guizhou Hospital of the First Affiliated Hospital of Sun Yat-sen University, Guiyang, Guizhou, China, 6Jingjinji National Center of Technology Innovation, Beijing, China  \nObjective: This study explores the feasibility of using breathomic biomarkers analyzed by machine learning as a non-invasive diagnostic tool to differentiate between benign and malignant thoracic lesions, aiming to enhance early detection of thoracic cancers and inform clinical decision-making.  \nMethods: This study enrolled 132 participants with confirmed diagnosis of lung cancer, esophageal cancer, thymoma, and benign diseases. Exhaled breath samples were analyzed by thermal desorption-gas chromatography-mass spectrometry. A logistic regression algorithm was employed to construct aclassification model for benign and malignant thoracic lesions. This model was trained on a subset of 80 cases and subsequently validated in a separate set comprising 52 samples.  \nResults: A logistic regression model based on thirteen exhaled volatile organic compounds (VOCs) was developed to differentiate benign and malignant thoracic lesions. The 13-VOC model achieved an AUC of 0 . 85 (0 .72, 0 . 96), accuracy of 0.79 (0.66, 0.88), sensitivity of 0.82 (0.67, 0.91), and a specificity of 0.71 (0 .45, 0 . 88) . It correctly classified 80% of lung cancer, 80% of thymoma, and 100% of esophageal cancer cases, distinguishing 71 .4% of benign lesions. For lung cancer, the model achieved an AUC of 0.79 (0.57, 0.98), sensitivity of 0.80 (0.63, 0.91), and specificity of 0.63 (0.31, 0.86), with 81.8% accuracy in detecting early-stage (Stage 0 + I + II) disease. The model outperformed a 4-serum tumor marker panel in sensitivity (0 . 90 vs. 0.39, p \u003C 0 . 001) . Additionally, in a co","cbCairU4SrlIYJmK","https://ap.wps.com/l/cbCairU4SrlIYJmK","pdf",6237523,1,17,"English","en",105,"# Objective\n# Methods\n# Results\n# Conclusion\n# Introduction\n## Lung cancer and survival burden\n## Esophageal cancer prognosis","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To assess whether breathomics biomarkers analyzed by machine learning can serve as a non-invasive diagnostic tool for differentiating benign and malignant thoracic lesions and supporting early detection.\"},{\"question\":\"How were breath samples collected and analyzed?\",\"answer\":\"Exhaled breath samples were collected and analyzed using thermal desorption-gas chromatography-mass spectrometry to quantify volatile organic compounds.\"},{\"question\":\"What model was developed and how did it perform?\",\"answer\":\"A logistic regression model based on 13 exhaled VOCs was trained on 80 cases and validated on 52. It achieved an AUC of 0.85 and improved sensitivity compared with a 4-serum tumor marker panel, with additional evidence of decreased predicted risk after surgery.\"}]","Machine learning assisted breathomic approach for early-stage thoracic cancer detection - Original Research | PDF",1785898605,43,{"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-assisted-breathomic-approach-for-early-stage-thoracic-cancer-detection-original-research","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-assisted-breathomic-approach-for-early-stage-thoracic-cancer-detection-original-research/125388/",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","2026-08-05",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 is the main objective of the study?","Question",{"text":75,"@type":76},"To assess whether breathomics biomarkers analyzed by machine learning can serve as a non-invasive diagnostic tool for differentiating benign and malignant thoracic lesions and supporting early detection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were breath samples collected and analyzed?",{"text":80,"@type":76},"Exhaled breath samples were collected and analyzed using thermal desorption-gas chromatography-mass spectrometry to quantify volatile organic compounds.",{"name":82,"@type":73,"acceptedAnswer":83},"What model was developed and how did it perform?",{"text":84,"@type":76},"A logistic regression model based on 13 exhaled VOCs was trained on 80 cases and validated on 52. 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