[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83409-en":3,"doc-seo-83409-105":29,"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":20,"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":13,"seo_description":14,"update_tm":27,"read_time":28},83409,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Towards Precision Therapy in Hepatocellular Carcinoma A Clinical-Reasoning LLM for Risk Stratification and Treatment","Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality worldwide, while existing staging and guideline frameworks provide coarse categories that miss within-stage heterogeneity and contextual signals in electronic medical records (EMRs). HCC-STAR is a clinically aligned large language model that reads routine EMR narratives and outputs risk-score staging, guideline-consistent treatment rankings with evidence-based rationales, and individualized survival estimates. Trained on ~30,000 cases expanded into EMR-style narratives, it achieves state-of-the-art performance across a multi-center cohort and improves decision quality and speed in clinician evaluations.","arXiv :2607 .08602v 1 [ cs .AI] 9 Jul 2026  \nTowards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment  \nGuidance  \nPeng Cui 1,* Jitao Wang2,* Siyan Xue3,* Yao Huang4,* Haoming Xia2,* Dong Li2 Dengxiang Liu5 Weilin Wang6 Liping Liu7,8 Leida Zhang9 Yunfu Cui 10 Tao Peng 11 Daolin Ji 12 Haitao Zhao 13 Wei Zhang 14 Xiaojuan Wang2 Weijie Ma 15,16 Zongren Ding 17 Jinlong Li5 Yuan Ding6 Jiajing Zhao7,8 Zhiyu Chen9 Chengkun Yang 11 Ziyue Huang 13 Jiaqi Liu2 Fusheng Liu 15,16 Yang Zhou 18 Xiaojuan Wang5 Zhongquan Sun6 Shiyun Bao7,8 Xiaojun Wang9 Ming Yang2 Guangxin Li 19 Bin Shu2 Yong Liao2 Hongxuan Li2 Yao Tang2 Shizhong Yang2 Yongyi Zeng 17 Yufeng Yuan 15,16 Yinpeng Dong4,† Jihui Hao20,† Jun Zhu 1,† Jiahong Dong2,†  \n1Department of Computer Science & Technology, Tsinghua University, Beijing, China  \n2Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology (Ministry of Education), School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China  \n3 School of Biomedical Engineering, Tsinghua University, Beijing, China  \n4College of AI, Tsinghua University, Beijing, China  \n5Hebei Provincial Key Laboratory of Portal Hypertension & Cirrhosis, Xingtai People’s Hospital, Xingtai, China  \n6Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China  \n7Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University), Shenzhen, China  \n8The Second Clinical Medical College, Jinan University, Shenzhen, China  \n9Department of Hepatobiliary Surgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China  \n10Department of Hepatopancreatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China  \n11Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China  \n12Department of Hepatopancreatobiliary Surgery, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China  \n13Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China  \n14Department of Hepatobiliary Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin, China  \n15Department of Hepatobiliary & Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China  \n16Clinical Medicine Research Center for Minimally Invasive Procedure, Wuhan University, Wuhan, China  \n17Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China  \n18Biological Information Biobank, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China  \n19Department of Radiotherapy, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China  \n20Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin, China  \n* These authors contributed equally to this work. †Corresponding authors: [dongyinpeng@tsinghua.edu.cn](dongyinpeng@tsinghua.edu.cn), [haojihui@tjmuch.com](haojihui@tjmuch.com),  \n[dcszj@tsinghua.edu.cn](dcszj@tsinghua.edu.cn), [dongjiahong@mail.tsinghua.edu.cn](dongjiahong@mail.tsinghua.edu.cn)  \n[Preprint. Under review](Preprint. Under review","cbCaiqcEuNAMttzl","https://ap.wps.com/l/cbCaiqcEuNAMttzl","pdf",10745366,1,47,"English","en",105,"# Abstract\n## HCC challenge and motivation\n## HCC-STAR model outputs\n## Data construction and training framework\n## Multi-center evaluation results\n## Survival analysis and clinical trustworthiness","[{\"question\":\"What problem does HCC-STAR target in hepatocellular carcinoma care?\",\"answer\":\"It addresses the limitations of coarse staging and guideline systems by capturing within-stage heterogeneity and leveraging contextual information contained in EMR narratives for more precise risk and treatment decisions.\"},{\"question\":\"What outputs does HCC-STAR generate from routine EMR narratives?\",\"answer\":\"It produces a risk-score-based staging, a ranked list of guideline-consistent treatments with evidence-based rationales, and individualized survival estimates.\"},{\"question\":\"How was HCC-STAR trained and evaluated?\",\"answer\":\"It was trained using approximately 30,000 HCC cases expanded into EMR-style narrative data, then evaluated on a multi-center cohort of 6,668 patients from 12 hospitals in China, showing improved performance versus clinical guidelines and competing models.\"}]",1784187364,118,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"towards-precision-therapy-in-hepatocellular-carcinoma-a-clinical-reasoning-llm-for-risk-stratification-and-treatment","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/towards-precision-therapy-in-hepatocellular-carcinoma-a-clinical-reasoning-llm-for-risk-stratification-and-treatment/83409/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does HCC-STAR target in hepatocellular carcinoma care?","Question",{"text":75,"@type":76},"It addresses the limitations of coarse staging and guideline systems by capturing within-stage heterogeneity and leveraging contextual information contained in EMR narratives for more precise risk and treatment decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What outputs does HCC-STAR generate from routine EMR narratives?",{"text":80,"@type":76},"It produces a risk-score-based staging, a ranked list of guideline-consistent treatments with evidence-based rationales, and individualized survival estimates.",{"name":82,"@type":73,"acceptedAnswer":83},"How was HCC-STAR trained and evaluated?",{"text":84,"@type":76},"It was trained using approximately 30,000 HCC cases expanded into EMR-style narrative data, then evaluated on a multi-center cohort of 6,668 patients from 12 hospitals in China, showing improved performance versus clinical guidelines and competing 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