[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-373372-105":59,"doc-detail-373372-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","nomogram-prediction-for-gastric-cancer-development","Nomogram Prediction for Gastric Cancer Development","","Nomogram-based prediction models are developed to estimate gastric cancer (GC) risk by combining Helicobacter pylori (Hp) serology with gastric atrophy status. A retrospective cohort used health screening data from 2003 to 2018, classifying participants into four Hp titer/atrophy strata and modeling overall, intestinal, and diffuse GC as competing events. Performance was assessed with concordance index and time-tested discrimination via 10-fold cross-validation.",{"@graph":69,"@context":123},[70,84,106],{"@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/nomogram-prediction-for-gastric-cancer-development/373372/",{"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/nomogram-prediction-for-gastric-cancer-development/373372.png","ImageObject",300,407,{"name":92,"@type":93},"Alex Sinclair","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What risk factors were combined to predict gastric cancer in this study?","Question",{"text":113,"@type":114},"Helicobacter pylori serology (anti-Hp antibody titer) was combined with gastric atrophy status to model individual GC risk.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were participants grouped for building the nomogram models?",{"text":118,"@type":114},"Subjects with positive anti-Hp antibody results were classified into four groups based on low vs high titer and presence or absence of atrophy.",{"name":120,"@type":111,"acceptedAnswer":121},"How was model performance evaluated?",{"text":122,"@type":114},"Prediction performance was measured using the concordance index (c-index) and area under the curve through 10-fold cross-validation.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},373372,1790339932,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"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},1099523882182,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","ARTICLE 1  \nNomogram Prediction for Gastric Cancer Development  \nJoo Hyun Lim, MD, PhD 1 ,2 , *, Areum Han, BS3 ,4 , *, Soo-Jeong Cho, MD, PhD1, Seokyung Hahn, PhD5 ,6 ,7 and Sang Gyun Kim, MD, PhD 1  \nINTRODUCTION: Helicobacter pylori (Hp) and gastric atrophy represent significant risk factors for gastric cancer (GC) . Nevertheless, to date, no nomogram has been developed to predict GC based on the specific combination of risk factors present in individual cases.  \nMETHODS: A retrospective cohort study was conducted using health screening data collected between 2003 and  \n2018. Subjects with positive results for anti-Hp antibody were enrolled. Individuals were classified into  \n4 groups: low-B (low titer without atrophy), high-B (high titer without atrophy), high-C (high titer with atrophy), and low-C (low titer with atrophy) . Nomogram prediction models were developed for overall GCs as well as intestinal and diffuse cancers, with each type considered a competing event, by using both Cox proportional and subdistribution hazard models. Prediction performance was evaluated using the concordance index (c-index) and the area under the curve through 10-fold cross-validation.  \nSTOMACH  \n1Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, South Korea; 2Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, South Korea; 3 Interdisciplinary Program of Medical Informatics, Seoul National University College of Medicine, Seoul, South Korea; 4 Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, South Korea; 5Department of Human Systems Medicine, Seoul National University College of Medicine, Seoul, South Korea; 6 Institute of Health Policy and Management, Medical Research Center, Seoul, South Korea; 7 Medical Big Data Research Center, Seoul National University, Seoul, South Korea. Correspondence: Soo Jeong Cho, MD, PhD. E-mail: [crystal5@snu.ac.kr Seokyung Hahn](crystal5@snu.ac.kr Seokyung Hahn), PhD. E-mail: [hahns@snu.ac.kr](hahns@snu.ac.kr).  \n*Joo Hyun Lim and Areum Han contributed equally to this study.  \nReceived December 5, 2024; accepted February 18, 2025; published online March 10, 2025  \n© 2025 The Author(s) . Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology  \nAmerican College of Gastroenterology Clinical and Translational Gastroenterology  \nSTOMACH  \nRESULTS: During a median follow-up period of 5.7 years, 231 new GC cases developed among the total cohort of  \n28,311 subjects, including 159 intestinal type, 68 diffuse type, and 4 cases of unknown type.  \nMultivariable analyses indicated that age, body mass index, family history, smoking, and classification into the high-C or low-C group were significant predictors of GC. The nomograms for intestinal type, diffuse type, and total GC demonstrated area under the curve values of 0.82, 0.62, and 0.75, respectively, and c-indices of 0.85, 0.54, and 0.76, respectively.  \nDISCUSSION: The nomograms for GC prediction would be useful in identifying high-risk individuals, particularly for intestinal type. This would facilitate the implementation of personalized eradication and intensive screening strategies to target those at higher risk for GC.  \nKEYWORDS: Helicobacter pylori; gastric cancer; intestinal type; diffuse type; atrophy  \nClinical and Translational Gastroenterology 2025;16:e00833. [https://doi.org/10.14309/ctg.0000000000000833](https://doi.org/10.14309/ctg.0000000000000833)  \nINTRODUCTION  \nGastric cancer (GC) is the fifth most common cancer worldwide and the fifth most frequent cause of cancer-related death (1) . GC incidence is particularly elevated in East Asia. Notably, early GC has relatively favorable prognosis, with a 5-year survival rate exceeding 95%, whereas that of advanced disease is only 63% . Furthermore, less invasive treatme","cbCaica9MDB9pywE","https://ap.wps.com/l/cbCaica9MDB9pywE","pdf",978698,"English","# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"What risk factors were combined to predict gastric cancer in this study?\",\"answer\":\"Helicobacter pylori serology (anti-Hp antibody titer) was combined with gastric atrophy status to model individual GC risk.\"},{\"question\":\"How were participants grouped for building the nomogram models?\",\"answer\":\"Subjects with positive anti-Hp antibody results were classified into four groups based on low vs high titer and presence or absence of atrophy.\"},{\"question\":\"How was model performance evaluated?\",\"answer\":\"Prediction performance was measured using the concordance index (c-index) and area under the curve through 10-fold cross-validation.\"}]","Nomogram Prediction for Gastric Cancer Development | PDF",1790199412,23]