[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-340423-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-340423-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","identification-of-histopathological-classification-and-establishment-of-prognostic-indicators-of-gastric-adenocarcinoma-based-on-deep-learning-algorithm","Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm","","A deep learning study establishes a model to predict the pathological type of gastric adenocarcinoma using whole-slide images (WSIs). Histopathological images from 356 gastric adenocarcinoma (STAD) patients are used for training, validation, and testing, with 80 external-validation H&E-stained WSIs collected separately. The CLAM tool generates slide tiles and enables a DL pipeline that achieves over 90% accuracy for identifying histopathological subtypes.",{"@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/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/identification-of-histopathological-classification-and-establishment-of-prognostic-indicators-of-gastric-adenocarcinoma-based-on-deep-learning-algorithm/340423/",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/identification-of-histopathological-classification-and-establishment-of-prognostic-indicators-of-gastric-adenocarcinoma-based-on-deep-learning-algorithm/340423.png","ImageObject",300,407,{"name":42,"@type":43},"Quinn","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-25","2026-09-22",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main goal of the study?","Question",{"text":63,"@type":64},"To build a deep learning model that predicts the pathological subtype of gastric adenocarcinoma from whole-slide images and supports prognosis evaluation.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the datasets prepared for training and validation?",{"text":68,"@type":64},"356 gastric adenocarcinoma histopathology images were downloaded and split into training, validation, and test sets in an 8:1:1 ratio. An additional 80 H&E-stained WSIs were collected for external validation.",{"name":70,"@type":61,"acceptedAnswer":71},"Which model approach is used to process the WSIs and learn features?",{"text":72,"@type":64},"The CLAM (Clustering-constrained Attention Multiple Instance Learning) tool cuts WSIs and uses attention-based weak supervision with instance-level clustering to learn diagnostically valuable regions and refine the feature space.","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},340423,1790955678,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,100,105,109,114,119,122,126],{"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":55,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},7,40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",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":55,"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":106,"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":145},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Medical Molecular Morphology (2024) 57:286–298 [https://doi.org/10.1007/s00795-024-00399-8](https://doi.org/10.1007/s00795-024-00399-8)  \nIdentification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm  \nZhihui Wang1 · Hui Peng2 · Jie Wan3 · Anping Song2,4  \nReceived: 11 March 2024 / Accepted: 15 July 2024 / Published online: 1 August 2024 © The Author(s) 2024  \nAbstract  \nThe aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1) . Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.  \nKeywords Gastric adenocarcinoma · Subtype · Deep learning · Whole-slide images  \nIntroduction  \nGastric adenocarcinoma (STAD) accounts for more than 95% of all gastric malignancies and is the most common cause of cancer-related death [1, 2]. According to the Global Cancer Statistics Report 2020, there are about 1.07 million new cases of STAD, accounting for 5.6% of all new cancer cases and ranking fifth in terms of incidence rate, and about  \n* Anping Song [anpingsong@tjh.tjmu.edu.cn](anpingsong@tjh.tjmu.edu.cn)  \n1 Department of Ultrasound Imaging, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430101, Hubei, China  \n2 Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430101, Hubei, China  \n3 Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430101, Hubei, China  \n4 Department of Oncology, Tongji Hospital Sino-French New City Branch, Caidian District, No.288 Xintian Avenue, Wuhan 430101, Hubei, China  \n769,000 deaths, accounting for 7.7% of all cancer deaths and ranking fourth in terms of mortality rate [3] . Patients with early gastric cancer undergoing radical surgery and subsequent chemotherapy have a 5-year survival rate of 90% after surgery [4] . However, more than half of STAD patients are initially diagnosed at an advanced stage, and the 5-year overall survival (OS) rate of STAD is less than 30%[5] . Tumor microenvironment (TME) denotes the non-cancerous cells and components presented in the tumor, including molecules produced and released by them. As an important component of TME, tumor-infiltrating immune cells (TIIC) are associated with the promotion or inhibition of tumor growth [6, 7] . Therefore, early detection and appropriate treatment are important ways to reduce the mortality of STAD patients, while understanding the degree of immune cell infiltration indifferent subtypes of gastric cancer is helpful for the administration of relevant immunotherapy.  \nThe spatial characteristics of different tissues in histopathological images play an important role in the diagnosis and prognosis of cancer [8–10] . Traditionally, pathologistshave identified and distinguished different pathological types of STAD by visu","cbCaigjD8MjVgQrM","https://ap.wps.com/l/cbCaigjD8MjVgQrM","pdf",2187448,13,"English","# Abstract\n# Introduction\n## Gastric adenocarcinoma burden and clinical challenge\n## Tumor microenvironment and immune infiltration\n## Limits of traditional pathology review\n## Rationale for deep learning and CLAM\n## Related deep-learning applications in pathology","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To build a deep learning model that predicts the pathological subtype of gastric adenocarcinoma from whole-slide images and supports prognosis evaluation.\"},{\"question\":\"How were the datasets prepared for training and validation?\",\"answer\":\"356 gastric adenocarcinoma histopathology images were downloaded and split into training, validation, and test sets in an 8:1:1 ratio. An additional 80 H\\u0026E-stained WSIs were collected for external validation.\"},{\"question\":\"Which model approach is used to process the WSIs and learn features?\",\"answer\":\"The CLAM (Clustering-constrained Attention Multiple Instance Learning) tool cuts WSIs and uses attention-based weak supervision with instance-level clustering to learn diagnostically valuable regions and refine the feature space.\"}]","Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm | PDF",1790036409,33]