[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121628-en":3,"doc-seo-121628-105":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},121628,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",7,"Healthcare","Prediction of Bone Metastasis in Non-Small Cell Lung Cancer Based on Machine Learning","This original research develops a machine learning algorithm to predict bone metastasis (BM) in patients with non-small cell lung cancer (NSCLC) and implements a practical web-based predictor derived from the best-performing model. NSCLC cases from the SEER database (2010–2018) and an external cohort from Nanchang University (2007–2016) are analyzed. Independent BM risk factors are identified using univariate and multivariate logistic regression. Six algorithms are compared using AUC, accuracy, sensitivity, and specificity, and the XGB model shows the strongest validation performance.","TYPE Original Research PUBLISHED 09 January 2023 DOI 10.3389/fonc.2022.1054300  \nOPEN ACCESS  \nEDITED BY  \nShyamala Guruvare,  \nManipal Academy of Higher Education, India  \nREVIEWED BY Shobitha Rao,  \nSrinivas University, India Geetha M.,  \nManipal Institute of Technology, India  \n*CORRESPONDENCE  \nJia-Ming Liu [liujiamingdr@hotmail.com](liujiamingdr@hotmail.com)[ ](liujiamingdr@hotmail.com)Zhi-Hong Zhang [13803505665@163.com](13803505665@163.com)  \n†These authors have contributed equally to this work  \nSPECIALTY SECTION  \nThis article was submitted to Cancer Epidemiology and Prevention, a section of the journal  \nFrontiers in Oncology  \nRECEIVED 26 September 2022  \nACCEPTED 21 November 2022  \nPUBLISHED 09 January 2023  \nCITATION  \nLi M-P, Liu W-C, Sun B-L, Zhong N-S, Liu Z-L, Huang S-H, Zhang Z-H and Liu J-M (2023) Prediction of bone metastasis in non-small cell lung cancer based on machine learning. Front. Oncol. 12:1054300 .  \ndoi: 10.3389/fonc.2022.1054300  \nCOPYRIGHT  \n© 2023 Li, Liu, Sun, Zhong, Liu, Huang, Zhang and Liu. 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.  \nPrediction of bone metastasis in non-small cell lung cancer based on machine learning  \nMeng-Pan Li 1,2†, Wen-Cai Liu 1,2,3†, Bo-Lin Sun 1,4, Nan-Shan Zhong 1,4, Zhi-Li Liu 1,4, Shan-Hu Huang 1,4, Zhi-Hong Zhang 1,4* and Jia-Ming Liu 1,4*  \n1 Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China, 2The First Clinical Medical College of Nanchang University, Nanchang, China, 3 Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China, 4 Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China  \nObjective: The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.  \nMethods: Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB) . Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.  \nResults: A total of 50581 NSCLC patients were included in this study, and 5087 (10 . 06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0. 808 and 0 . 841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.  \nConclusion: This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors fo","cbCainQUjuJ0KrlH","https://ap.wps.com/l/cbCainQUjuJ0KrlH","pdf",2973071,1,12,"English","en",105,"# Introduction\n## Bone metastasis in NSCLC and clinical challenge\n## Imaging limitations and need for early prediction\n# Methods\n## Data sources and cohorts\n## Risk factor screening and model building\n## Model evaluation metrics\n# Results\n## Cohort characteristics and BM incidence\n## Independent risk factors\n## Model performance in internal and external validation\n## Web predictor construction\n# Conclusion","[{\"question\":\"What is the main goal of the study on non-small cell lung cancer?\",\"answer\":\"To develop and validate a machine learning algorithm that predicts bone metastasis in NSCLC and to build a simple web predictor based on the best model.\"},{\"question\":\"How were independent risk factors for bone metastasis selected?\",\"answer\":\"Independent risk factors were screened using univariate and multivariate logistic regression.\"},{\"question\":\"Which machine learning model performed best and how was it evaluated?\",\"answer\":\"The XGB algorithm performed best in both internal and external validation, evaluated using AUC, accuracy, sensitivity, and specificity.\"}]","Prediction of Bone Metastasis in Non-Small Cell Lung Cancer Based on Machine Learning | PDF",1785805812,30,{"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},"prediction-of-bone-metastasis-in-non-small-cell-lung-cancer-based-on-machine-learning","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-bone-metastasis-in-non-small-cell-lung-cancer-based-on-machine-learning/121628/",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 is the main goal of the study on non-small cell lung cancer?","Question",{"text":75,"@type":76},"To develop and validate a machine learning algorithm that predicts bone metastasis in NSCLC and to build a simple web predictor based on the best model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were independent risk factors for bone metastasis selected?",{"text":80,"@type":76},"Independent risk factors were screened using univariate and multivariate logistic regression.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and how was it evaluated?",{"text":84,"@type":76},"The XGB algorithm performed best in both internal and external validation, evaluated using AUC, accuracy, sensitivity, and specificity.","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},[93,97,101,105,110,115,118,122,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]