[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117606-en":3,"doc-seo-117606-105":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},117606,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning-Based Diagnostic Models for Early Gastric Cancer Using Clinical Laboratory Indicators","Machine learning methods are used to improve early gastric cancer (EGC) diagnosis by leveraging clinical laboratory indicators. Clinical data from gastric cancer patients treated at a single center between 2016 and 2023 were collected, then five algorithms—XGBoost, random forest, SVM-RFE, LGBM, and rpart—were trained with a 60% subset and evaluated on a 40% test subset. Performance was measured using AUROC, F1-score, sensitivity, and specificity. XGBoost achieved the highest diagnostic accuracy, and key contributing biomarkers included GR, CA724, RBC, CA242, and ALB, with tumor size identified as an independent risk factor.","downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nInternational Journal of General Medicine  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nMachine Learning-Based Diagnostic Models for Early Gastric Cancer Using Clinical Laboratory Indicators  \nRunbi Ji 1 , 2 , Ruoyu Yang 1 , 2 , Jun Yao 1 , Shenglan Dai 1 , Xin Zhu 1 , Qiang Ye 1  \n1The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People’s Republic of China; 2Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212013, People’s Republic of China  \nCorrespondence: Runbi Ji, The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People’s Republic of China, Tel +86 511 88915575, Fax +86 511 85234387, Email [runbiji@163.com](runbiji@163.com)  \n\n| Background: The occurrence of gastric cancer is a complex pathological process leading to multiple abnormalities in clinical laboratory indicators. Machine learning techniques can make it easy to handle millions of variables to make more accurate predictions and diagnoses of diseases.\u003Cbr>Methods: Clinical data from gastric cancer patients in a single-center who underwent surgery between 2016 and 2023 were collected. Five machine learning algorithms (extreme gradient boosting, XGBoost; random forest, RF; support vector machine-recursive feature elimination, SVM-RFE; light gradient boosting machine, LGBM; and recursive partitioning, rpart) were utilized to develop diagnostic models. Among the date, 60% were randomly selected to train the models, while the remaining 40% were used for testing. We used the area under the receiver operating characteristic curve (AUROC), F1-score value, sensitivity, and specificity to evaluate the performance of models. Results: The XGBoost algorithm showed the best performance in gastric cancer diagnosis, with significantly higher area under curve (AUC) (combining blood indicators and pathological parameters, AUC=0.9909) value than other models. Glutathione reductase (GR), carbohydrate antigen 724 (CA724), erythrocytes (RBC), carbohydrate antigen 242 (CA242), and albumin (ALB) contributed the most to the diagnosis. The tumor size were independent risk factors for early gastric cancer.\u003Cbr>Conclusion: Machine learning combined blood indicators and pathological parameters could predict gastric cancer risk more accurately. The XGBoost model had the best diagnostic performance. The study provides confirmatory data support for the preclinical implementation of the model.\u003Cbr>Keywords: early gastric cancer, machine learning, diagnostic model, clinical laboratory indicators, glutathione reductase |\n| --- |\n| Introduction\u003Cbr>Gastric cancer (GC) remains one of the most prevalent malignant tumors worldwide, ranks as the fifth in incidence rate and the fourth in mortality.1,2 Early gastric cancer (EGC) refers to lesions confined to the mucosal and submucosal, regardless of size or lymph node metastasis. There are often no obvious symptoms in EGC, occasionally accompanied by discomfort similar to\u003Cbr>chronic gastritis or gastric ulcers. Because of the atypical symptoms, the diagnosis rate of EGC is relatively low.3–5\u003Cbr>The 5-year survival rate of EGC after surgery (or endoscopic resection) can reach 90% to 95%, much higher than that of advanced gastric cancer (AGC) . Early screening and diagnosis are crucial to early management. The methods for early screening and diagnosis include X-ray barium contrast examination, endoscopic examination, serological examination and pathological diagnosis.6 Upper gastrointestinal X-ray barium contrast examination is radioactive and has a low positive rate, which has gradually been phased out in clinical practice. Gastroscopy and histopathological examination are currently considered as the gold standard for diagnosing GC. But the acceptance of this technology is relatively low for its high cost, limit","cbCaidpiy2gtysXW","https://ap.wps.com/l/cbCaidpiy2gtysXW","pdf",8080274,1,13,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"Which machine learning algorithms were used to build the diagnostic models?\",\"answer\":\"Five algorithms were used: XGBoost, random forest (RF), SVM-RFE, LGBM, and rpart.\"},{\"question\":\"How was model performance evaluated in the study?\",\"answer\":\"Models were assessed using AUROC, F1-score, sensitivity, and specificity on a 40% test set.\"},{\"question\":\"Which model performed best and which indicators contributed most?\",\"answer\":\"XGBoost showed the best diagnostic performance. GR, CA724, RBC, CA242, and ALB contributed the most to diagnosis.\"}]","Machine Learning-Based Diagnostic Models for Early Gastric Cancer Using Clinical Laboratory Indicators | PDF",1785677249,33,{"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-diagnostic-models-for-early-gastric-cancer-using-clinical-laboratory-indicators","",{"@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-diagnostic-models-for-early-gastric-cancer-using-clinical-laboratory-indicators/117606/",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","2026-08-02",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},"Which machine learning algorithms were used to build the diagnostic models?","Question",{"text":75,"@type":76},"Five algorithms were used: XGBoost, random forest (RF), SVM-RFE, LGBM, and rpart.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was model performance evaluated in the study?",{"text":80,"@type":76},"Models were assessed using AUROC, F1-score, sensitivity, and specificity on a 40% test set.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and which indicators contributed most?",{"text":84,"@type":76},"XGBoost showed the best diagnostic performance. 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