[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120532-en":3,"doc-seo-120532-105":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},120532,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting early recurrence of colorectal cancer liver metastases - an integrative approach using radiomics and machine learning","Colorectal cancer liver metastases occur in up to half of patients, and recurrence within one year after hepatectomy remains frequent, with reported rates around 60–70%. This study integrates imaging radiomics feature extraction with machine-learning classification to forecast intrahepatic recurrence during the first postoperative year. Multiple models were built, systematically evaluated, and validated on a test set to determine effectiveness. The best-performing approach combines imaging and clinical information, supporting early-risk stratification and improved diagnostic decision support.","TYPE Original Research PUBLISHED 14 November 2025 DOI 10.3389/fonc.2025.1613093  \nOPEN ACCESS  \nEDITED BY  \nArka Bhowmik,  \nMemorial Sloan Kettering Cancer Center, United States  \nREVIEWED BY  \nLisheng Wang,  \nShanghai Jiao Tong University, China Long Wu,  \nAffiliated Hospital of Guizhou Medical University, China  \nYen Cho Huang,  \nChang Gung Memorial Hospital, Taiwan  \n*CORRESPONDENCE  \nXiaobin Feng  \n [fengxiaobin200708@aliyun.com](fengxiaobin200708@aliyun.com)[ ](fengxiaobin200708@aliyun.com)Chunkang Yang  \n [chunkang129@fjmu.edu.cn](chunkang129@fjmu.edu.cn)  \nRECEIVED 16 April 2025  \nACCEPTED 29 August 2025  \nPUBLISHED 14 November 2025  \nCITATION  \nLin Y, Huang Y, Liu Z, Feng X and Yang C (2025) Predicting early recurrence of colorectal cancer liver metastases: an integrative approach using radiomicsand machine learning.  \nFront. Oncol. 15:1613093 .  \ndoi: 10.3389/fonc.2025.1613093  \nCOPYRIGHT  \n© 2025 Lin, Huang, Liu, Feng and Yang. This isan 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.  \nPredicting early recurrence of colorectal cancer liver metastases: an integrative approach using radiomicsand machine learning  \nYanzong Lin 1,2, Yunxia Huang 3, Zhaohui Liu 2, Xiaobin Feng 4* and Chunkang Yang 1*  \n1 Department of Colorectal Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China, 2 Department of General Surgery, First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China, 3 Department of Radiation Oncology, First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China, 4 Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Institute for Precision Medicine, Key Laboratory of Digital Intelligence Hepatology (Ministry of Education), Tsinghua University, Beijing, China  \nBackground: The overall incidence of liver metastasis in colorectal cancer is as high as 50%, and surgery remains the only potentially curative approach for the metastatic disease. The recurrence rate of liver metastases within one year after surgery is still 60%-70% in clinical practice. Whether we can accurately predict the early recurrence of patients after surgery is one of the most important considerations in formulating the overall treatment strategy.  \nMethods: In this study, we combined radiomics feature extraction with machine learning classification methods to develop a novel strategy for predicting intrahepatic metastases based on imaging radiomics and machine learning. We constructed and systematically evaluated multiple machine learning models to assess their performance . By validating these models on a test set, we determined the effectiveness of each predictive model and selected the one with the highest predictive accuracy.  \nResults: The integration of radio mics and machine learning methods demonstrated significant potential in predicting intrahepatic recurrence within one year after surgery in patients with colorectal cancer liver metastases. The Gradient Boosting, LightGBM, and Random Forest models all achieved classification accuracies (ACC) exceeding 65% across all classification tasks. Notably, the Random Forest model exhibited the best performance; while its classification accuracy was 65.52% in the imaging-only group, it increased to 75.86% when both imaging and clinical information were combined, with an area under the receiver operating characteristic curve (AUC) of 70.83%, indicating strong predictive capability. These findings suggest that these models have potential application value in supporting the diagnostic work of clinical radiologists, potentially helping to reduc","cbCailKPcCcYuI76","https://ap.wps.com/l/cbCailKPcCcYuI76","pdf",4379713,1,11,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusions\n# Keywords","[{\"question\":\"What clinical problem does this study address?\",\"answer\":\"It focuses on predicting early intrahepatic recurrence within one year after surgery in patients with colorectal cancer liver metastases, where recurrence risk is still high.\"},{\"question\":\"How do the researchers build the prediction models?\",\"answer\":\"They extract radiomics features from imaging and combine them with machine-learning classification, constructing and evaluating multiple models and validating them on a test set.\"},{\"question\":\"Which modeling strategy performed best and what improvement was observed?\",\"answer\":\"The Random Forest model showed the best performance; accuracy was 65.52% with imaging-only and increased to 75.86% when imaging and clinical information were combined (AUC 70.83%).\"}]","Predicting early recurrence of colorectal cancer liver metastases - an integrative approach using radiomics and machine learning | PDF",1785730534,28,{"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},"predicting-early-recurrence-of-colorectal-cancer-liver-metastases-an-integrative-approach-using-radiomics-and-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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-early-recurrence-of-colorectal-cancer-liver-metastases-an-integrative-approach-using-radiomics-and-machine-learning/120532/",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","2026-08-03",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 clinical problem does this study address?","Question",{"text":75,"@type":76},"It focuses on predicting early intrahepatic recurrence within one year after surgery in patients with colorectal cancer liver metastases, where recurrence risk is still high.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the researchers build the prediction models?",{"text":80,"@type":76},"They extract radiomics features from imaging and combine them with machine-learning classification, constructing and evaluating multiple models and validating them on a test set.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling strategy performed best and what improvement was observed?",{"text":84,"@type":76},"The Random Forest model showed the best performance; 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