[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-357847-105":59,"doc-detail-357847-en":135},{"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":128,"head_meta":130,"extra_data":132,"updated_unix":134},105,"en","evaluation-of-computer-aided-detection-for-identifying-missed-gastric-cancer-after-endoscopic-submucosal-dissection","Evaluation of Computer-aided Detection for Identifying Missed Gastric Cancer After Endoscopic Submucosal Dissection","","Computer-aided detection (CADe) using deep learning is assessed for reducing missed gastric cancers (MGCs) and helping physicians re-check endoscopic images after endoscopic submucosal dissection (ESD). A dataset of 2,324 endoscopic images was analyzed, including 60 MGC cases found during surveillance endoscopy within 18 months after initial ESD. CADe performance was compared with 10 endoscopists using per-lesion sensitivity, per-image diagnostic metrics, and reading time. CADe achieved faster review time but without a significant sensitivity difference.",{"@graph":69,"@context":127},[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/evaluation-of-computer-aided-detection-for-identifying-missed-gastric-cancer-after-endoscopic-submucosal-dissection/357847/",{"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/evaluation-of-computer-aided-detection-for-identifying-missed-gastric-cancer-after-endoscopic-submucosal-dissection/357847.png","ImageObject",300,407,{"name":92,"@type":93},"PakDamar76","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What clinical problem does this study address?","Question",{"text":113,"@type":114},"It targets missed gastric cancers (MGCs) occurring during post-ESD surveillance, which can delay effective diagnosis and treatment.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were the CADe and endoscopist performances evaluated?",{"text":118,"@type":114},"Using 2,324 collected endoscopic images, the study compared per-lesion sensitivity, per-image diagnostic performance (including specificity and predictive values), and diagnostic time between CADe and 10 endoscopists.",{"name":120,"@type":111,"acceptedAnswer":121},"What were the main findings regarding diagnostic sensitivity and speed?",{"text":122,"@type":114},"Per-lesion sensitivity for MGCs was 15.0% (CADe) versus 13.8% (endoscopists) without statistical significance. Diagnostic time was much shorter with CADe (0.03 s vs. 2.86 s).",{"name":124,"@type":111,"acceptedAnswer":125},"What conclusion is drawn about clinical deployment of CADe?",{"text":126,"@type":114},"The study concludes that sensitivity did not differ significantly and remained low, so further improvements are required before using CADe clinically as a double-checking tool.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},357847,1790381115,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149},962090893057,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","DEN Open   \nORIGINAL ARTICLE   \nEvaluation of Computer-aided Detection for Identifying Missed Gastric Cancer After Endoscopic Submucosal Dissection  \nTatsunori Minamide1, 2  Tomonori Sato3  Seiichiro Sakaguchi3  Noboru Kawata1  Yuki Maeda1  Masao Yoshida1  Yoichi Yamamoto1  Taishi Okumura1  Tetsuya Suwa1  Hiroshi Ashizawa1  Kohei Shigeta1  Haruka Nakamura1  Akifumi Notsu4  Toshio Uraoka5  Hiroyuki Ono1  \n1 Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan  2 Department of Gastroenterology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan  3Advanced Technology Research Division, Olympus Corporation, Tokyo, Japan  4 Clinical Research Center, Shizuoka  \nCancer Center, Shizuoka, Japan  5 Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Maebashi, Japan Correspondence: Hiroyuki Ono ([h.ono@scchr.jp](h.ono@scchr.jp))  \nReceived: 10 March 2026  Revised: 14 May 2026  Accepted: 18 May 2026  \nKeywords: artificial intelligence | endoscopy | gastric cancer | missed diagnosis | surveillance  \nABSTRACT  \nObjectives: Computer-aided detection (CADe) using deep learning is promising for reducing missed gastric cancers (MGCs) and supporting physicians in double-checking endoscopic images. We aimed to evaluate the CADe efficacy for MGCs after endoscopic submucosal dissection (ESD) .  \nMethods: We collected 2324 endoscopic images, including 60 of MGCs detected during surveillance esophagogastroduodenoscopy within 18 months after initial ESD. The performance of the CADe system, developed for early GC detection using deep learning, was compared with that of 10 endoscopists in a detection study using collected images. Per-lesion sensitivity, per-image detection performance, and diagnostic time were compared.  \nResults: The per-lesion sensitivity for MGCs was 15.0%(9/60) and 13.8%(83/600) for CADe and endoscopist detection, respectively (p = 0.538). The respective per-image computer-aided and endoscopist performance sensitivity was 11.3% and 10.6%(p = 0.548), specificity 88.5% and 94.8%(p \u003C 0.001), positive predictive value 4.4% and 8.3%, and negative predictive value 95.7% and 96.0% . The per-image CADe time was significantly shorter (0.03 s vs. 2.86 s, p \u003C 0.001). CADe showed higher sensitivity in certain subgroups, although these findings should be interpreted cautiously given the small sample size.  \nConclusions: No significant difference in sensitivity was observed between CADe and endoscopist detection for MGCs after ESD, and the absolute sensitivity remained low. Further improvements are needed before clinical implementation of CADe as a double-checking tool.  \nTrial Registration: The authors have confirmed clinical trial registration is not needed for this submission.  \n1  Introduction  \nGastric cancer (GC) is the fifth leading cause of cancer-related death worldwide, with approximately one million new cases  \ndiagnosed annually [1] . Early detection considerably improves prognosis, with a 5-year survival rate of nearly 90% in early-stage disease [2] . Esophagogastroduodenoscopy (EGD) is widely used for screening early GC (EGC) and reduces mortality [3–6] . When  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2026 The Author(s). DEN Open published by John Wiley & Sons Australia, Ltd on behalf of Japan Gastroenterological Endoscopy Society.  \nDEN Open, 2027; 7:e70357  \n[https://doi.org/10.1002/deo2.70357](https://doi.org/10.1002/deo2.70357)  \n1 of 9  \nFIGURE 1  Patient flowchart. EGC, early gastric cancer; ESD, endoscopic submucosal dissection; MGC, missed gastric cancer.  \nEGC is detected, minimally invasive endoscopic treatments such as endoscopic submucosal dissection (ESD) can be performed instead of surgery [7] .  \nEven after curative ESD for EGC, regular endoscopic surveillance is crucia","cbCaisnp6F4tpMDS","https://ap.wps.com/l/cbCaisnp6F4tpMDS","pdf",5712159,"English","# Introduction\n## Gastric cancer, surveillance, and missed lesions\n## Rationale for CADe in double-checking\n# Methods\n## Study population and eligibility\n## Image data and clinicopathological variables","[{\"question\":\"What clinical problem does this study address?\",\"answer\":\"It targets missed gastric cancers (MGCs) occurring during post-ESD surveillance, which can delay effective diagnosis and treatment.\"},{\"question\":\"How were the CADe and endoscopist performances evaluated?\",\"answer\":\"Using 2,324 collected endoscopic images, the study compared per-lesion sensitivity, per-image diagnostic performance (including specificity and predictive values), and diagnostic time between CADe and 10 endoscopists.\"},{\"question\":\"What were the main findings regarding diagnostic sensitivity and speed?\",\"answer\":\"Per-lesion sensitivity for MGCs was 15.0% (CADe) versus 13.8% (endoscopists) without statistical significance. Diagnostic time was much shorter with CADe (0.03 s vs. 2.86 s).\"},{\"question\":\"What conclusion is drawn about clinical deployment of CADe?\",\"answer\":\"The study concludes that sensitivity did not differ significantly and remained low, so further improvements are required before using CADe clinically as a double-checking tool.\"}]","Evaluation of Computer-aided Detection for Identifying Missed Gastric Cancer After Endoscopic Submucosal Dissection | PDF",1790132055,23]