[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-332446-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-332446-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","a-deep-learning-method-for-predicting-the-origins-of-cervical-lymph-node-metastatic-cancer-on-digital-pathological-images","A deep learning method for predicting the origins of cervical lymph node metastatic cancer on digital pathological images","","Metastatic cancers in cervical lymph nodes show complex morphology and are difficult for clinicians to determine their origin. A retrospective study built a deep learning framework to predict lymph node status in patients with cervical lymphadenopathy using hematoxylin and eosin (H&E) stained digital pathology slides. Training used 1,036 cervical lymph node biopsy specimens and employed a multiple-instance learning algorithm for key-region identification, with internal cross-validation and external dataset validation. The model distinguished primary lymphoma from metastatic cancer and improved generalization for preoperative assessment.",{"@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/a-deep-learning-method-for-predicting-the-origins-of-cervical-lymph-node-metastatic-cancer-on-digital-pathological-images/332446/",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/a-deep-learning-method-for-predicting-the-origins-of-cervical-lymph-node-metastatic-cancer-on-digital-pathological-images/332446.png","ImageObject",300,407,{"name":42,"@type":43},"Theodore","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-06","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the deep learning method address in cervical cancer care?","Question",{"text":63,"@type":64},"It addresses how to predict the origins and status of cervical lymph node metastatic cancer using digital pathological images, supporting clinicians before surgery.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was the model trained and what data was used?",{"text":68,"@type":64},"The retrospective study used 1,036 cervical lymph node biopsy specimens with hematoxylin and eosin (H&E) stained slides, applying a multiple-instance learning approach to identify key regions.",{"name":70,"@type":61,"acceptedAnswer":71},"Does the model distinguish between different cancer types?",{"text":72,"@type":64},"Yes. The summary states the model can differentiate primary lymphoma from metastatic cancer with high prediction accuracy.","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},332446,1790159082,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,119,122,126],{"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":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"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":112,"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,"preview_page_count":4},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","iScience  \nll  \nOPEN ACCESS  \nArticle  \nA deep learning method for predicting the origins of cervical lymph node metastatic cancer on digital pathological images  \nRunliang Zheng, Xuenian Wang, Lianghui Zhu, ..., Yonghong He, Huijuan Shi, Anjia Han  \n[heyh@sz.tsinghua.edu.cn](heyh@sz.tsinghua.edu.cn) (Y. H.)  \n[shihj@mail.sysu.edu.cn](shihj@mail.sysu.edu.cn) (H.S.) [hananjia@mail.sysu.edu.cn](hananjia@mail.sysu.edu.cn)[ ](hananjia@mail.sysu.edu.cn)(A.H.)  \nHighlights  \nA large pathological dataset of cervical lymphadenopathy was established  \nAn algorithm predicts cervical lymph node status by targeting key regions  \nOur method outperforms other methods on both internal and external datasets  \nZheng et al., iScience 27, 110645  \nSeptember 20, 2024 ª 2024 The Author(s) . Published by Elsevier Inc.  \n[https://doi.org/10.1016/](https://doi.org/10.1016/)[ ](https://doi.org/10.1016/)[j.isci.2024.110645](j.isci.2024.110645)  \niScience  \nll  \nOPEN ACCESS  \nArticle  \nA deep learning method for predicting the origins of cervical lymph node metastatic  \ncancer on digital pathological images  \nRunliang Zheng, 1,5 Xuenian Wang, 1,5 Lianghui Zhu, 1 Renao Yan, 1 Jiawen Li,1 Yani Wei,2 Fenfen Zhang,2 Hong Du,3 Linlang Guo,4 Yonghong He,1,* Huijuan Shi,2,* and Anjia Han2,6,*  \nSUMMARY  \nThe metastatic cancer of cervical lymph nodes presents complex shapes and poses significant challenges for doctors in determining its origin. We established a deep learning framework to predict the status of lymph nodes in patients with cervical lymphadenopathy (CLA) by hematoxylin and eosin (H&E) stained slides. This retrospective study utilized 1,036 cervical lymph node biopsy specimens at the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU). A multiple-instance learning algorithm designed for key region identification was applied, and cross-validation experiments were conducted in the dataset. Additionally, the model distinguished between primary lymphoma and metastatic cancer with high prediction accuracy. We also validated our model andother modelson an external dataset. Our model showed better generalization and achieved the best results on both internal and external datasets. This algorithm offers an approach for evaluating cervical lymph node status before surgery, significantly aiding physicians in preoperative diagnosis and treatment planning.  \nINTRODUCTION  \nCancers still cannot be identified in the clinic and are categorized as cancers of unknown primary (CUP) .1 Diagnosing and treating CUP is notably tricky due to their ambiguous clinical presentations and the absence of reliable biomarkers. A key characteristic of CUP is metastasis without the ability to identify the cancer’s primary location through conventional methods, introducing considerable uncertainty in the treatment and prognosis of patients. The diagnosis of CUP typically starts with detecting a metastatic tumor, indicating that the cancer has already spread from its origin to other body parts.2 Despite significant advancements in imaging and histopathological analysis by modern medicine, standard diagnostic techniques often remain unsuccessful in determining the origin of the cancer. The diagnosis of CUP typically entails a comprehensive approach comprising a detailed patient history, physical examination, blood tests, imageology studies (including computed tomography [CT], MRI, and positron emission tomography [PET scans]), and tissue analysis.3 Although these diagnostic techniques offer critical information about tumor characteristics, they are frequently time-intensive and expensive.  \nIn response to these diagnostic challenges, computational pathology has emerged as an innovative field blending computer science and pathology, witnessing rapid advancements in recent years.4 Computational pathology offers a new way to analyze microscopic images of tumor tissue through neural networks to find clues about its site of origin. Deep learning has demonstrated significant potential in ","cbCaijT6fYjq53Dp","https://ap.wps.com/l/cbCaijT6fYjq53Dp","pdf",3890498,13,"English","# Highlights\n## Method summary\n# Summary\n# Introduction","[{\"question\":\"What problem does the deep learning method address in cervical cancer care?\",\"answer\":\"It addresses how to predict the origins and status of cervical lymph node metastatic cancer using digital pathological images, supporting clinicians before surgery.\"},{\"question\":\"How was the model trained and what data was used?\",\"answer\":\"The retrospective study used 1,036 cervical lymph node biopsy specimens with hematoxylin and eosin (H\\u0026E) stained slides, applying a multiple-instance learning approach to identify key regions.\"},{\"question\":\"Does the model distinguish between different cancer types?\",\"answer\":\"Yes. The summary states the model can differentiate primary lymphoma from metastatic cancer with high prediction accuracy.\"}]","A deep learning method for predicting the origins of cervical lymph node metastatic cancer on digital pathological images | PDF",1789998237,33]