[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126233-en":3,"doc-seo-126233-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126233,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Machine learning for predicting distant metastasis in nasopharyngeal carcinoma patients - research study","Distant metastasis is the primary driver of treatment failure and death in nasopharyngeal carcinoma (NPC). This study builds machine learning (ML) predictive models to identify risk factors for distant metastasis using patient data from Fudan University over September 2017 to June 2024. Seven ML methods were compared, with Logistic Regression achieving the strongest test performance. SHAP analysis highlighted key associated variables including targeted therapy, immunotherapy, N stage, Epstein-Barr virus, hypertension, T stage, lymphocyte count, and lactate dehydrogenase, enabling high-risk stratification and informing early interventions.","TYPE Original Research PUBLISHED 05 June 2025  \nDOI 10.3389/fimmu.2025.1580200  \nOPEN ACCESS  \nEDITED BY  \nClaudine Kieda,  \nMilitary Institute of Medicine, Poland  \nREVIEWED BY  \nCatharina Lisson,  \nUlm University Medical Center, Germany Jingwei Zhao,  \nShanghai Jiao Tong University, China  \n*CORRESPONDENCE  \nTaomin Huang  \n [taominhuang@126.com](taominhuang@126.com)[ ](taominhuang@126.com)Jingchao Yan  \n[jingchao.yan@fdeent.org](jingchao.yan@fdeent.org)  \nRECEIVED 20 February 2025  \nACCEPTED 19 May 2025  \nPUBLISHED 05 June 2025  \nCITATION  \nSun H, Zhu J, Li L, Xin X, Yan J and Huang T (2025) Machine learning for predicting distant metastasis in nasopharyngeal carcinoma patients.  \nFront. Immunol. 16:1580200 .  \ndoi: 10.3389/fimmu.2025.1580200  \nCOPYRIGHT  \n© 2025 Sun, Zhu, Li, Xin, Yan and Huang. 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.  \nMachine learning for predicting distant metastasis in nasopharyngeal carcinoma patients  \nHong Sun 1, Jijie Zhu 2, Ling Li 1, Xiu Xin 1, Jingchao Yan 1* and Taomin Huang 1*  \n1 Department of Pharmacy, Eye & ENT Hospital, Fudan University, Shanghai, China, 2Shanghai University of Medicine & Health Sciences, Shanghai, China  \nBackground: Distant metastasis is the main cause of treatment failure and death in patients with nasopharyngeal carcinoma (NPC) . The aim of this study was to explore the risk factors for distant metastasis in NPC patients using machine learning (ML) methods.  \nMethods: We collected data from NPC patients who were treated at the Eye Ear Nose Throat Hospital of Fudan University between September 2017 and June 2024. Seven ML methods were employed to construct the predictive models. By comparing the predictive performance of different ML models, the best one was selected to establish a predictive model for distant metastasis of NPC. The SHapley Additive exPlanation (SHAP) method was utilized to ascertain the ranking of feature importance and to provide explanations for the predictive model.  \nResults: A total of 1,845 NPC patients were included in this study. Among the seven models, Logistic Regression (LR) performed best in the test dataset (Area Under the ROC Curve [AUC] = 0 . 8499) . SHAP analysis indicated that the most important variables for distant metastasis in NPC patients were targeted therapy, immunotherapy, N stage, Epstein-Barr virus (EBV), hypertension, T stage, lymphocyte count (LY) and lactate dehydrogenase (LDH) level.  \nConclusion: Targeted therapy, N stage, immunotherapy, EBV, hypertension, T stage, LY and LDH level are significantly associated with the risk of distant metastasis in NPC and could be used to identify high-risk populations for distant metastasis in NPC patients. For high-risk patients, early interventions such as targeted therapy and immunotherapy might be considered to reduce the risk of distant metastasis in NPC.  \nKEYWORDS  \nnasopharyngeal carcinoma, machine learning, distant metastasis, predictive model, immunotherapy, targeted therapy  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nNasopharyngeal carcinoma (NPC), a subset of head and neck cancers, originates from the epithelial cells of the nasopharynx (1, 2). The development of NPC is associated with a variety of factors, including genetic susceptibility, infection by the Epstein-Barr virus (EBV), and environmental factors such as smoking (3–8). NPC has significant geographical differences, being prevalent in East and Southeast Asia (9). The early treatment of NPC mainly relies on radiotherapy and chemotherapy, which has a good prognosis (10). However","cbCaisFMySbVK7Re","https://ap.wps.com/l/cbCaisFMySbVK7Re","pdf",5835445,4,1,11,"English","en",105,"# Introduction\n## Rationale and clinical background\n## Current treatment challenges\n# Methods\n## Data source and cohort\n## ML models and model selection\n## Feature interpretation with SHAP\n# Results\n## Cohort size and model performance\n## SHAP-identified feature importance\n# Conclusion\n## Key risk factors and clinical implications","[{\"question\":\"What is the main objective of the study on NPC?\",\"answer\":\"To explore risk factors for distant metastasis in nasopharyngeal carcinoma and build machine learning models to predict it.\"},{\"question\":\"Which machine learning model performed best in the test dataset?\",\"answer\":\"Logistic Regression performed best, with an AUC of 0.8499 in the test dataset.\"},{\"question\":\"How were important predictors identified in the model?\",\"answer\":\"The study used the SHAP method to rank feature importance and explain the model’s predictive results.\"}]","Machine learning for predicting distant metastasis in nasopharyngeal carcinoma patients - research study | PDF",1785903968,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-for-predicting-distant-metastasis-in-nasopharyngeal-carcinoma-patients-research-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/machine-learning-for-predicting-distant-metastasis-in-nasopharyngeal-carcinoma-patients-research-study/126233/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of the study on NPC?","Question",{"text":76,"@type":77},"To explore risk factors for distant metastasis in nasopharyngeal carcinoma and build machine learning models to predict it.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning model performed best in the test dataset?",{"text":81,"@type":77},"Logistic Regression performed best, with an AUC of 0.8499 in the test dataset.",{"name":83,"@type":74,"acceptedAnswer":84},"How were important predictors identified in the model?",{"text":85,"@type":77},"The study used the SHAP method to rank feature importance and explain the model’s predictive results.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]