[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-329992-105":59,"doc-detail-329992-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","risk-stratification-of-papillary-thyroid-cancers-using-multidimensional-machine-learning","Risk stratification of papillary thyroid cancers using multidimensional machine learning","","Papillary thyroid cancer (PTC) is a common endocrine malignancy with different risk levels, yet preoperative risk assessment remains difficult worldwide. This study reports a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) built from multidimensional features, including clinical indicators, immune indices, genetic mutation information, and high-throughput proteomics. Using 558 patients, the model achieved strong discrimination across discovery, retrospective, and prospective external validations.",{"@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/risk-stratification-of-papillary-thyroid-cancers-using-multidimensional-machine-learning/329992/",{"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/risk-stratification-of-papillary-thyroid-cancers-using-multidimensional-machine-learning/329992.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is PRAC-PTC and how does it work?","Question",{"text":112,"@type":113},"PRAC-PTC is a preoperative risk assessment classifier for papillary thyroid cancer that integrates clinical indicators, immune indices, genetic feature information, and high-throughput proteomics with machine learning.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the patient datasets organized and validated?",{"text":117,"@type":113},"Among 558 patients, the authors used a discovery set, a retrospective test set, and a prospective test set. The model was externally validated by blinded analyses in both retrospective and prospective cohorts from independent clinical centers.",{"name":119,"@type":110,"acceptedAnswer":120},"What impact did PRAC-PTC have on clinicians’ risk prediction?",{"text":121,"@type":113},"Clinicians’ preoperative predictive risk effectiveness improved with PRAC-PTC assistance, with reported accuracy values of 84.4% (retrospective) and 83.5% (prospective) in the test sets.","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},329992,1790543936,{"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":29,"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},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","’Tumour Marker Prognostic Study  \nRisk stratification of papillary thyroid cancers using multidimensional machine learning  \nYuanhui Li, PhDa , Fan Wu, MMa , Weigang Ge, BSb , Yu Zhang, MDa , Yifan Hu, PhDb , Lingqian Zhao, MMc , Wanglong Gou, PhDd , Jingjing Shi, MMa , Yeqin Ni, MMa , Lu Li, MMe,f,g , Wenxin Fu, BSb , Xiangfeng Lin, MMh , Yunxian Yu, PhDi , Zhijiang Han, MDj , Chuanghua Chen, MMk , Rujun Xu, BMl , Shirong Zhang, MDm , Li Zhou, MDa , Gang Pan, MMa , You Peng, MMa , Linlin Mao, MDa , Tianhan Zhou, MMc , Jusheng Zheng, PhDd , Haitao Zheng, MMh,* , Yaoting Sun, PhDe,f,g,* , Tiannan Guo, PhDe,f,g,* , Dingcun Luo, BMa,c,*  \nBackground: Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics.  \nMaterials and Methods: The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration) . Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system.  \nResults: The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0 .925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0 .799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4%(95% CI: 82.9–84.4) and 83.5%(95% CI: 82 .2–84.2) in the retrospective and prospective test sets, respectively.  \nConclusion: This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.  \nKey words machine learning, papillary thyroid cancer, proteomics, risk stratification  \nIntroduction  \nPapillary thyroid cancer (PTC) is one of the most common endocrine malignancies [1,2] . Although the prognosis of PTC is favourable, it tends to have a high rate of recurrence (10–35%), which may lead to treatment failure and death[3] . PTCs can be divided into different risk levels. In recent  \nyears, it has been believed that some relatively inert PTCs can be managed with active surveillance without immediate surgery. However, 15–30% of invasive PTCs have a poor prognosis and require active surgical treatment [1,4] . At present, achieving an accurate preoperative risk assessment of PTC to avoid excessive or insufficient treatment remains challenging.  \naDepartment of Oncological Surgery, jDepartment of Radiology, kDepartment of Ultrasonography, lDepartment of Pathology, mCentre of Translational Medicine, Hangzhou First People ’s Hospital, fKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, gResearch Centre for Industries of the Future, Westlake University, eWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, iDepartment of Epidemiology and Health Stat","cbCaihjxxsTonUM0","https://ap.wps.com/l/cbCaihjxxsTonUM0","pdf",2210059,13,"English","# Background\n# Materials and Methods\n## Patient cohorts and datasets\n## Biomarkers and measurements\n# Results\n# Conclusion","[{\"question\":\"What is PRAC-PTC and how does it work?\",\"answer\":\"PRAC-PTC is a preoperative risk assessment classifier for papillary thyroid cancer that integrates clinical indicators, immune indices, genetic feature information, and high-throughput proteomics with machine learning.\"},{\"question\":\"How were the patient datasets organized and validated?\",\"answer\":\"Among 558 patients, the authors used a discovery set, a retrospective test set, and a prospective test set. The model was externally validated by blinded analyses in both retrospective and prospective cohorts from independent clinical centers.\"},{\"question\":\"What impact did PRAC-PTC have on clinicians’ risk prediction?\",\"answer\":\"Clinicians’ preoperative predictive risk effectiveness improved with PRAC-PTC assistance, with reported accuracy values of 84.4% (retrospective) and 83.5% (prospective) in the test sets.\"}]","Risk stratification of papillary thyroid cancers using multidimensional machine learning | PDF",1789986482,33]