[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117434-en":3,"doc-seo-117434-105":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},117434,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma - A Multicenter Cohort Study","This multicenter retrospective study evaluates magnetic resonance (MR)-guided hypofractionated radiotherapy for patients with unresectable hepatocellular carcinoma (HCC) and examines a machine learning radiomics workflow for treatment response prediction. Radiomics features were derived from gross tumor volume (GTV) and reduced using K-means clustering to form two radiomics-defined subtypes. Nine machine learning models were trained and validated via 5-fold cross-validation, with internal and external validation for a multilayer perceptron model. Results show no Grade 3/4 adverse events and significantly different survival outcomes between subtypes.","Journal of Hepatocellular Carcinoma downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nJournal of Hepatocellular Carcinoma  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nMachine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Cohort Study  \nKe Su 1–3 , *, Xin Liu 1 ,4 , *, Yue-Can Zeng 5 , *, Junnv Xu 6 , *, Han Li 2 , Heran Wang 7 , Shanshan Du 1  \n,  \nHuadong Wang 1 , Jinbo Yue8 , Yong Yin 1 , Zhenjiang Li 1  \n1Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, People’s Republic of China; 2Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, People’s Republic of China; 3Department of Radiation Oncology, National Cancer Center/ National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, People’s Republic of China; 4Department of Gynecological Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, 150081, People’s Republic of China; 5Department of Radiation Oncology, Cancer Treatment Center, The Second Affiliated Hospital of Hainan Medical University, Haikou, 570311, People’s Republic of China; 6Department of Medical Oncology, The Second Affiliated Hospital, Hainan Medical University, Haikou, Hainan Province, 570311, People’s Republic of China; 7Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, People’s Republic of China; 8Department of Abdominal Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Yong Yin; Zhenjiang Li, [Email yinyongsd@126.com](Email yinyongsd@126.com); zhenjli [1987@163.com](1987@163.com)  \n\n| Background: This study was conducted to assess the efficacy and safety of magnetic resonance (MR)-guided hypofractionated radiotherapy in patients with unresectable hepatocellular carcinoma (HCC) . Machine learning-based radiomics was utilized to predict responses in these patients.\u003Cbr>Methods: This retrospective study included 118 hCC patients who received MR-guided hypofractionated radiotherapy. The primary study endpoint was the objective response rate (ORR) . Radiomics features were based on the gross tumor volume (GTV) . K-means clustering was performed to differentiate cancer subtypes based on radiomics. Nine radiomics-utilizing machine learning models were built and validated internally through 5-fold cross-validation.\u003Cbr>Results: The ORR, median progression-free survival (mPFS), and median overall survival (mOS) were 54.4%, 21.7 months, and 40.7 months, respectively. No patient experienced Grade 3/4 adverse events. 1130 radiomics features were extracted from the GTV, of which 7 were included for further analysis. K-means clustering identified 2 subtypes based on the selected features. Subtype 1 had significantly higher response, longer mPFS, and longer mOS than Subtype 2. In both internal and external validations, the multi-layer perceptron (MLP) model demonstrated superior predictive performance for response, achieving a receiver operating characteristic-area under the curve (ROC-AUC) of 0.804 and 0.842, respectively.\u003Cbr>Conclusion: MR-guided radiotherapy was proven to be effective and safe for HCC. The machine learning radiomics model developed in this study could accurately predict the response of radiotherapy-treated inoperable HCC.\u003Cbr>Keywords: machine learning models, radiomics, radiotherapy, hepatocellular carcinoma |\n| --- |\n| Introduction\u003Cbr>Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and poses a major global health challenge.1 It accounts for app","cbCaid7z1TTiIRwo","https://ap.wps.com/l/cbCaid7z1TTiIRwo","pdf",6484003,1,15,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What patient population and treatment were evaluated in this study?\",\"answer\":\"The study included 118 patients with unresectable hepatocellular carcinoma treated with MR-guided hypofractionated radiotherapy.\"},{\"question\":\"How were radiomics features used to predict radiotherapy response?\",\"answer\":\"Radiomics features were extracted from the GTV, clustered using K-means to differentiate tumor subtypes, and used to build and validate nine machine learning models. The multilayer perceptron showed the best predictive performance.\"},{\"question\":\"What were the main outcome and safety results?\",\"answer\":\"The objective response rate was 54.4%, with median progression-free survival of 21.7 months and median overall survival of 40.7 months. No patients experienced Grade 3/4 adverse events.\"}]","Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma - A Multicenter Cohort Study | PDF",1785675860,38,{"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},"machine-learning-radiomics-for-predicting-response-to-mr-guided-radiotherapy-in-unresectable-hepatocellular-carcinoma-a-multicenter-cohort-study","",{"@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/machine-learning-radiomics-for-predicting-response-to-mr-guided-radiotherapy-in-unresectable-hepatocellular-carcinoma-a-multicenter-cohort-study/117434/",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","2026-08-02",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 patient population and treatment were evaluated in this study?","Question",{"text":75,"@type":76},"The study included 118 patients with unresectable hepatocellular carcinoma treated with MR-guided hypofractionated radiotherapy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were radiomics features used to predict radiotherapy response?",{"text":80,"@type":76},"Radiomics features were extracted from the GTV, clustered using K-means to differentiate tumor subtypes, and used to build and validate nine machine learning models. The multilayer perceptron showed the best predictive performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main outcome and safety results?",{"text":84,"@type":76},"The objective response rate was 54.4%, with median progression-free survival of 21.7 months and median overall survival of 40.7 months. 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