[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123133-en":3,"doc-seo-123133-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123133,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Identification of Intraoperative Hypoxemia and Hypoproteinemia as Prognostic Indicators in Anastomotic Leakage Post-Radical Gastrectomy - An 8-Year Multicenter Study Utilizing Machine Learning Techniques","Complications and mortality after radical gastrectomy for gastric cancer have improved, yet anastomotic leakage (AL) remains a major driver of poor immediate and long-term outcomes. This study built machine learning models to recognize preoperative and intraoperative high-risk factors and to predict mortality in patients with AL after radical gastrectomy. Data from 906 gastric cancer patients included 36 feature variables; XGBoost, random forest, and KNN were trained, validated with k-fold crossvalidation, and tested on independent datasets.","TYPE Original Research PUBLISHED 27 November 2024 DOI 10.3389/fonc.2024.1471137  \nOPEN ACCESS  \nEDITED BY  \nPradeep Kumar Shukla,  \nUniversity of Tennessee Health Science Center (UTHSC), United States  \nREVIEWED BY  \nChristian Cotsoglou,  \nIRCCS San Gerardo dei Tintori Foundation, Italy  \nWencai Liu,  \nShanghai Jiao Tong University, China  \n*CORRESPONDENCE  \nWenyi Du  \n [2021122183@stu.njmu.edu.cn](2021122183@stu.njmu.edu.cn)[ ](2021122183@stu.njmu.edu.cn)Ning Zhou  \n [ursula1a5bos@hotmail.com](ursula1a5bos@hotmail.com)  \n†These authors have contributed equally to this work  \nRECEIVED 26 July 2024  \nACCEPTED 12 November 2024  \nPUBLISHED 27 November 2024  \nCITATION  \nLiu Y, Zhao S, Shang X, Shen W, Du W and Zhou N (2024) Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy:  \nan 8-year multicenter study utilizing machine learning techniques.  \nFront. Oncol. 14:1471137 .  \ndoi: 10.3389/fonc.2024.1471137  \nCOPYRIGHT  \n© 2024 Liu, Zhao, Shang, Shen, Du and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIdentification of intraoperative hypoxemia and hypoproteinemiaas prognostic indicators inanastomotic leakage post-radical gastrectomy: an 8-year multicenter study utilizing machine learning techniques  \nYuan Liu 1†, Songyun Zhao 2†, Xingchen Shang 1†, Wei Shen 1, Wenyi Du 1* and Ning Zhou 1*  \n1 Department of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China, 2 Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China  \nBackground: Complications and mortality rates following gastrectomy for gastric cancer have improved over recent years; however, complications such as anastomotic leakage (AL) continue to significantly impact both immediate and long-term prognoses. This study aimed to develop a machine learning model to identify preoperative and intraoperative high-risk factors and predict mortality inpatients with AL after radical gastrectomy.  \nMethods: For this investigation, 906 patients diagnosed with gastric cancer were enrolled and evaluated, with a comprehensive set of 36 feature variables collected. We employed three distinct machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN)—to develop our models. To ensure model robustness, we applied k-fold crossvalidation for internal validation of the four models and subsequently validated them using independent datasets.  \nResults: In contrast to the other machine learning models employed in this study, the XGBoost algorithm exhibited superior predictive performance in identifying mortality risk factors for patients with AL across one, three, and five-year intervals. The analysis identified several common risk factors affecting mortality rates at these intervals, including advanced age, hypoproteinemia, a history of anemia and hypertension, prolonged operative time, increased intraoperative bleeding, low intraoperative percutaneous arterial oxygen saturation (SPO2) levels, T3 and T4 tumors, tumor lymph node invasion, and tumor peripheral nerve invasion (PNI) .  \nConclusion: Among the three machine learning models examined in this study, the XGBoost algorithm exhibited superior predictive capabilities concerning the  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nprognosis of patients with AL following gastrectomy. Additionally, the use of machine learning models offers valuable assistance to clinicians in identifying crucial prognostic facto","cbCaie1XV1mPWI8D","https://ap.wps.com/l/cbCaie1XV1mPWI8D","pdf",3049837,1,14,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Keywords\n# Introduction","[{\"question\":\"What clinical problem does the study focus on?\",\"answer\":\"The study targets anastomotic leakage (AL) after radical gastrectomy for gastric cancer and its impact on mortality and long-term outcomes.\"},{\"question\":\"How were the machine learning models developed and validated?\",\"answer\":\"A cohort of 906 patients with 36 feature variables was used to train XGBoost, random forest, and KNN, with k-fold crossvalidation for internal validation and independent datasets for validation.\"},{\"question\":\"Which algorithm performed best for mortality prediction?\",\"answer\":\"XGBoost showed superior predictive performance for identifying mortality risk factors at 1-, 3-, and 5-year intervals compared with the other models.\"}]","Identification of Intraoperative Hypoxemia and Hypoproteinemia as Prognostic Indicators in Anastomotic Leakage Post-Radical Gastrectomy - An 8-Year Multicenter Study Utilizing Machine Learning Techniques | PDF",1785814789,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identification-of-intraoperative-hypoxemia-and-hypoproteinemia-as-prognostic-indicators-in-anastomotic-leakage-post-radical-gastrectomy-an-8-year-multicenter-study-utilizing-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identification-of-intraoperative-hypoxemia-and-hypoproteinemia-as-prognostic-indicators-in-anastomotic-leakage-post-radical-gastrectomy-an-8-year-multicenter-study-utilizing-machine-learning-techniques/123133/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical problem does the study focus on?","Question",{"text":75,"@type":76},"The study targets anastomotic leakage (AL) after radical gastrectomy for gastric cancer and its impact on mortality and long-term outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models developed and validated?",{"text":80,"@type":76},"A cohort of 906 patients with 36 feature variables was used to train XGBoost, random forest, and KNN, with k-fold crossvalidation for internal validation and independent datasets for validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performed best for mortality prediction?",{"text":84,"@type":76},"XGBoost showed superior predictive performance for identifying mortality risk factors at 1-, 3-, and 5-year intervals compared with the other models.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]