[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-331441-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-331441-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","machine-learning-identifies-a-5-serum-cytokine-panel-for-the-early-detection-of-chronic-atrophy-gastritis-patients","Machine learning identifies a 5-serum cytokine panel for the early detection of chronic atrophy gastritis patients","","Chronic atrophy gastritis (CAG) is a high-risk pre-cancerous condition for gastric cancer (GC), yet non-invasive diagnosis that accurately distinguishes CAG from benign gastritis remains limited. This study quantified 40 cytokines in blood from 247 endoscopy-screened patients using multiplex immunoassay. Feature importance was ranked with Boruta, then modeled with LightGBM using a discovery/validation design.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/machine-learning-identifies-a-5-serum-cytokine-panel-for-the-early-detection-of-chronic-atrophy-gastritis-patients/331441/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/machine-learning-identifies-a-5-serum-cytokine-panel-for-the-early-detection-of-chronic-atrophy-gastritis-patients/331441.png","ImageObject",300,407,{"name":42,"@type":43},"Sage","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What clinical problem does the study address?","Question",{"text":63,"@type":64},"The study targets the lack of accurate non-invasive methods to detect and discriminate chronic atrophy gastritis (CAG) from benign gastritis forms for optimal gastric cancer management.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were cytokines measured and selected for modeling?",{"text":68,"@type":64},"Blood samples were collected from 247 endoscopy-screened patients, and 40 cytokines were quantified via multiplex immunoassay. Cytokine importance was ranked with Boruta, and the top cytokines were used to build machine learning models.",{"name":70,"@type":61,"acceptedAnswer":71},"Which cytokine panel and performance were achieved?",{"text":72,"@type":64},"Five serum cytokines—IL-10, TNF-α, Eotaxin, IP-10, and SDF-1α—were identified to discriminate CAG from CSG. The model achieved AUC=0.88 and Accuracy=0.78, outperforming the conventional PGI/PGII ratio (AUC=0.59).","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},331441,1790288178,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":108,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Cancer Biomarkers 41 (2024) 25–40 25  \nDOI 10.3233/CBM-240023 IOS Press  \nMachine learning identifies a 5-serum cytokine panel for the early detection of chronic atrophy gastritis patients  \nFangmei Ana ; 1 , Yan Geb ; 1 , Wei Yeb , Lin Jia , Ke Chena , Yunfei Wanga , Xiaoxue Zhanga , Shengrong Donga , Yao Shena , Jiamin Zhaoa , Xiaojuan Gaoa , Simon Junankarc , Robin Barry Chanb , Dimitris Christodouloub , Wen Wenb , Peihua Lud ;􀀃 and Qiang Zhana ;􀀃  \na Department of Gastroenterology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, National Clinical Research Center for Digestive  \nDiseases (Xi 'an) Jiangsu Branch Wuxi, Jiangsu, China bAliveX Biotech, Shanghai, China  \ncAliveX Biotech, Darlington, Australia  \nd Department of Medical Oncology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China  \nReceived 3 March 2024 Accepted 19 July 2024  \nAbstract.  \nBACKGROUND: Chronic atrophy gastritis (CAG) is a high-risk pre-cancerous lesion for gastric cancer (GC) . The early and accurate detection and discrimination of CAG from benign forms of gastritis (e.g. chronic superficial gastritis, CSG) is critical for optimal management of GC. However, accurate non-invasive methods for the diagnosis of CAG are currently lacking. Cytokines cause inflammation and drive cancer transformation in GC, but their utility as a diagnostic for CAG is poorly characterized. METHODS: Blood samples were collected, and 40 cytokines were quantified using a multiplexed immunoassay from 247 patients undergoing screening via endoscopy. Patients were divided into discovery and validation sets. Each cytokine importance was ranked using the feature selection algorithm Boruta. The cytokines with the highest feature importance were selected for machine learning (ML), using the LightGBM algorithm.  \nRESULTS: Five serum cytokines (IL-10, TNF-􀀋, Eotaxin, IP-10 and SDF-1a) that could discriminate between CAG and CSG patients were identified and used for predictive model construction. This model was robust and could identify CAG patients with high performance (AUC = 0.88, Accuracy = 0.78) . This compared favorably to the conventional approach using the PGI/PGII ratio (AUC = 0.59) .  \nCONCLUSION: Using state-of-the-art ML and a blood-based immunoassay, we developed an improved non-invasive screening method for the detection of precancerous GC lesions.  \nFUNDING: Supported in part by grants from: Jiangsu Science and Technology Project (no. BK20211039); Top Talent Support Program for young and middle-aged people of Wuxi Health Committee (BJ2023008); Medical Key Discipline Program of Wuxi Health Commission (ZDXK2021010), Wuxi Science and Technology Bureau Project (no. N20201004); Scientific Research Program of Wuxi Health Commission (Z202208, J202104) .  \nKeywords: Chronic Atrophy Gastritis (CAG), Chronic Superficial Gastritis (CSG), Screening, Cytokines, Machine Learning (ML) Algorithms  \n1Contributed equally to this paper.  \n􀀃 Corresponding authors: Peihua Lu, Department of Medical Oncology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China. E-mail: [lphty1_1@163.com. Qiang](lphty1_1@163.com. Qiang)[ ](lphty1_1@163.com. Qiang)Zhan, Department of Gastroenterology, Affiliated Wuxi People's Hos-  \npital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, National Clinical Research Center for Digestive Diseases (Xi'an) Jiangsu Branch Wuxi, Jiangsu, China. E-mail: [qiangzhan@njmu.edu.cn](qiangzhan@njmu.edu.cn).  \nISSN 1574-0153 􀀍c 2024 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4 .0) .  \n26 F. An et al. / Machine learn","cbCaiqS7HNB1WUAH","https://ap.wps.com/l/cbCaiqS7HNB1WUAH","pdf",2706393,16,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n## Funding\n# Introduction","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study targets the lack of accurate non-invasive methods to detect and discriminate chronic atrophy gastritis (CAG) from benign gastritis forms for optimal gastric cancer management.\"},{\"question\":\"How were cytokines measured and selected for modeling?\",\"answer\":\"Blood samples were collected from 247 endoscopy-screened patients, and 40 cytokines were quantified via multiplex immunoassay. Cytokine importance was ranked with Boruta, and the top cytokines were used to build machine learning models.\"},{\"question\":\"Which cytokine panel and performance were achieved?\",\"answer\":\"Five serum cytokines—IL-10, TNF-α, Eotaxin, IP-10, and SDF-1α—were identified to discriminate CAG from CSG. The model achieved AUC=0.88 and Accuracy=0.78, outperforming the conventional PGI/PGII ratio (AUC=0.59).\"}]","Machine learning identifies a 5-serum cytokine panel for the early detection of chronic atrophy gastritis patients | PDF",1789993264]