[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-458721-105":59,"doc-detail-458721-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","network-analysis-of-multidimensional-symptom-experience-among-postoperative-esophageal-cancer-survivors","Network analysis of multidimensional symptom experience among postoperative esophageal cancer survivors","","Postoperative esophageal cancer survivors face a considerable symptom burden that substantially undermines quality of life. This cross-sectional study examined symptom clusters and the interrelationships among symptoms in postoperative patients to identify core symptoms. Using principal component analysis and Gaussian graphical network models, the study recruited 460 survivors in Shandong from February 2023 to February 2024, revealing three symptom clusters and a stable symptom network.",{"@graph":69,"@context":122},[70,84,105],{"@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/network-analysis-of-multidimensional-symptom-experience-among-postoperative-esophageal-cancer-survivors/458721/",{"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/network-analysis-of-multidimensional-symptom-experience-among-postoperative-esophageal-cancer-survivors/458721.png","ImageObject",300,407,{"name":92,"@type":93},"Franzy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the main purpose of this study?","Question",{"text":112,"@type":113},"To identify symptom clusters and characterize the interrelationships among postoperative symptoms in esophageal cancer survivors, with the goal of determining core symptoms.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were symptom clusters and symptom networks analyzed?",{"text":117,"@type":113},"Principal component analysis (PCA) was used to identify symptom clusters, and Gaussian graphical network models were applied to estimate the network structure and interconnections.",{"name":119,"@type":110,"acceptedAnswer":120},"Which symptom emerged as the most influential core symptom?",{"text":121,"@type":113},"Fatigue showed the highest strength centrality, making it the most prominent core symptom in the network.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},458721,1791354341,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"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,"preview_page_count":4},2336478945519,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Wang et al. Health and Quality of Life Outcomes (2026) 24:5  \n[https://doi.org/10.1186/s12955-025-02459-8](https://doi.org/10.1186/s12955-025-02459-8)  \nHealth and Quality of Life Outcomes  \nRESEARCH Open Access  \nNetwork analysis of multidimensional symptom experience among postoperative esophageal cancer survivors  \nYanfei Wang 1†, Yingtao Meng2†, Xiaotong Li3, Fang Zhang4, Wenya Su 1, Ruixue Han5, Junyi Peng 1, Miao Zhang5, Shengfen Li6, Ge Wang4 and Meimei Shang2*  \nAbstract  \nBackground Postoperative symptom burden is considerable and markedly undermines the quality of life of esophageal cancer (EC) survivors. This study aimed to examine symptom clusters and the interrelationships among symptoms in postoperative EC survivors, with the goal of identifying core symptoms.  \nMethods A cross-sectional study was conducted using the European Cancer Life Questionnaire and the EC-Specific Supplementary Questionnaire. EC survivors were recruited in Shandong between February 2023 and February 2024. Principal component analysis (PCA) was utilized to identify symptom clusters, while Gaussian graphical network models were used to estimate the network structure.  \nResults A total of 460 EC survivors were included in the study, revealing three distinct symptom clusters: the refluxdysphagia cluster, the respiratory-related symptom cluster, and the recovery-fatigue cluster. The final network model demonstrated interconnections among these symptoms.“Fatigue”(FA) exhibited the highest strength centrality, identifying it as the most prominent core symptom in the network.“Emotional functioning”(EF),“Fatigue”(FA), and“cognitive functioning”(CF) ranked highest in terms of bridge strengths. Additionally, the model showed excellent network stability.  \nConclusions EC survivors experienced significant postoperative symptom burden, with symptom network analysis revealing the complex interrelations among postoperative symptoms. This approach also identified core symptoms that play a crucial role in the network. Fatigue emerged as the most influential core symptom, highlighting the significance of targeted interventions to mitigate negative symptom interactions and improve quality of life. Keywords Esophageal cancer, Surgery, Multidimensional symptoms, Network analysis  \n†Yanfei Wang andYingtao Meng contributed equally to this work.  \n*Correspondence: Meimei Shang [smmmlove@sina.com](smmmlove@sina.com)  \n1School of Nursing, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China  \n2Nursing Department, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China  \n3Ordos central hospital, Ordos, China  \n4Esophageal Surgical Department, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China  \n5School of Nursing, Shandong University of Traditional Chinese Medicine, Jinan, China  \n6Qingzhou Wangfu Health Center, Weifang, China  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/.  \nWang et al. Health and Quality of Life Outcomes (2026) 24:5  \nIntr","cbCaiuN4aNXRG1LA","https://ap.wps.com/l/cbCaiuN4aNXRG1LA","pdf",2701931,11,"English","# Abstract\n# Introduction\n# Methods\n## Study design and participants\n## Analytical approach (PCA and network models)\n# Results\n## Symptom clusters\n## Network structure and centrality\n## Stability","[{\"question\":\"What was the main purpose of this study?\",\"answer\":\"To identify symptom clusters and characterize the interrelationships among postoperative symptoms in esophageal cancer survivors, with the goal of determining core symptoms.\"},{\"question\":\"How were symptom clusters and symptom networks analyzed?\",\"answer\":\"Principal component analysis (PCA) was used to identify symptom clusters, and Gaussian graphical network models were applied to estimate the network structure and interconnections.\"},{\"question\":\"Which symptom emerged as the most influential core symptom?\",\"answer\":\"Fatigue showed the highest strength centrality, making it the most prominent core symptom in the network.\"}]","Network analysis of multidimensional symptom experience among postoperative esophageal cancer survivors | PDF",1790754202,28]