[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119402-en":3,"doc-seo-119402-105":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},119402,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Application of Machine Learning in Hepatocellular Carcinoma - Review Summary","Hepatocellular carcinoma remains a highly lethal solid tumor despite progress in diagnostic and therapeutic strategies, with challenging long-term outcomes and limited survival. This review explains why machine learning is well suited for tumor research, highlighting its strength in modeling high-dimensional data and complex nonlinear patterns beyond conventional regression approaches. It organizes current evidence on machine learning for HCC risk prediction, diagnosis, treatment selection, and prognosis evaluation, aiming to support clinical decision-making and future research directions.","Chin J Clin Res, May 2025, Vol.38, No.5   \nCite as: Niu RY, Wang X, Liu DJ, Li CZ. Application of machine learning in hepatocellular carcinoma [J] . Chin JClin Res, 2025, 38(5):677-680 .  \nDOI: 10.13429/j.cnki.cjcr.2025.05.005  \nApplication of machine learning in hepatocellular carcinoma  \nNIU Riyu, WANG Xin, LIU Dingjing, LI Chengzhong  \nDepartment of Infectious Diseases, Shanghai Changhai Hospital, First Affiliated Hospital of Naval Medical University,  \nShanghai 223200, China  \nCorresponding author: LI Chengzhong, [E-mail: Leo_lee66@126.com](E-mail: Leo_lee66@126.com)  \nAbstract: Hepatocellular carcinoma（HCC）is a common solid tumor. In recent years, although significant researches have been focused on diagnostic and therapeutic strategies for HCC, the overall prognosis for patients remains challenging. Machine learning（ML） , as a core technology of artificial intelligence, has been increasingly applied in the field of tumor research. Compared to traditional regression models, ML models excel at handling high-dimensional data and complex nonlinear relationships, making it an ideal tool for HCC research. This review summarizes the application of ML in the risk prediction, diagnosis, treatment selection, and prognosis evaluation of HCC , aiming to provide references and insights for clinical practice and future research.  \nKeywords: Hepatocellular carcinoma；Artificial intelligence；Machine learning；Risk prediction；Diagnosis；Treatment selection； Prognostic evaluation  \nAccording to the Global Cancer Statistics 2022, liver cancer ranks as the sixth most common malignancy in terms of incidence, while its mortality rate ranks third [1] . According to data from the National Cancer Center of China, approximately 370,000 people were diagnosed with liver cancer and around 320,000 died from it in China in 2022 [2] . These statistics underscore the severity of liver cancer as a significant public health challenge. Hepatocellular carcinoma (HCC) is the most common pathological type of liver cancer, accounting for 80% to 90% of cases [3-4] . Due to the subtle symptoms of HCC, its high recurrence rate after surgery, and the widespread occurrence of drug resistance, the overall 5-year survival rate for patients is less than 20%[5] .  \nArtificial intelligence (AI) is a broad field aimed at endowing computers with the ability to simulate and surpass human intelligence. Machine learning (ML), an important branch of AI, enables computers to make predictions or decisions by analyzing data without the need for explicit programming instructions. Traditional regression models typically rely on linear assumptions and fewer variables. In contrast, ML models excel at handling high-dimensional data and complex nonlinear relationships. In recent years, ML has gradually been applied in medical research, achieving significant progress in cancer research [6-7] . This review summarizes the applications of ML in the risk prediction, diagnosis, treatment selection, and prognosis assessment of HCC, aiming to provide references and insights for clinical practice and future research.  \n1. ML for HCC Risk Prediction  \nRisk prediction for HCC is a critical component of chronic liver disease management. Identifying high-risk patients and providing early interventions can not only improve patient survival rates but also reduce the medical  \nburden and improve overall public health. Risk factors for HCC include hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, and metabolic associated fatty liver disease is gradually becoming an important risk factor [8] . Kucukakcali et al. [9] used the extreme gradient boosting machine algorithm to analyze gene expression data from HBV-related HCC patients and HBV-infected individuals without HCC, identifying RNF26, FLJ10233, ACBD6, RBM12, PFAS, H3C11, and GKP5 as characteristic genes associated with the development of HCC in HBV-infected individuals. Minami et al. [10] developed and validated a random survival forest ","cbCaiobIVNs3T27O","https://ap.wps.com/l/cbCaiobIVNs3T27O","pdf",1432828,1,8,"English","en",105,"# Introduction\n## Burden and clinical challenges of HCC\n## Role of AI and machine learning\n# ML for HCC Risk Prediction\n## Risk factors and predictive models\n# ML for HCC Diagnosis\n## Imaging-based early detection approaches\n# ML for Treatment Selection\n## Model-guided therapy decisions\n# ML for Prognosis Evaluation\n## Survival and outcome prediction","[{\"question\":\"Why is machine learning useful for hepatocellular carcinoma research compared with traditional regression models?\",\"answer\":\"Machine learning models handle high-dimensional data and complex nonlinear relationships, avoiding linear assumptions commonly used in traditional regression approaches.\"},{\"question\":\"Which areas does the review cover for using machine learning in HCC?\",\"answer\":\"The review summarizes applications in risk prediction, diagnosis, treatment selection, and prognosis evaluation for hepatocellular carcinoma.\"},{\"question\":\"What is the clinical importance of early HCC diagnosis addressed in the document?\",\"answer\":\"Early diagnosis improves patient survival, but many patients are diagnosed at advanced stages because early symptoms are subtle.\"}]","Application of Machine Learning in Hepatocellular Carcinoma - 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