[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126767-en":3,"doc-seo-126767-105":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},126767,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma","Viral and non-viral hepatocellular carcinoma (HCC) is becoming increasingly prevalent, and late diagnosis remains a key driver of HCC-related mortality. While traditional diagnostic approaches are established standards, they face constraints that delay confirmation of disease progression. This review contrasts conventional HCC diagnostics with machine learning and deep learning methods, summarizing biomedical data sources and database resources and outlining how AI supports diagnosis using histopathology, radiology, biomarkers, and electronic health records.","Received 19 January 2024, accepted 5 February 2024, date of publication 23 February 2024, date of current version 14 March 2024. Digital Object Identifier 10.1109/ACCESS.2024.3369491  \nMachine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma  \nARUN ASIF1, FAHEEM AHMED2, ZEESHAN3, JAVED ALI KHAN4, EMAN ALLOGMANI5, NORA EL RASHIDY6, SOBIA MANZOOR7, AND MUHAMMAD SHAHID ANWAR8  \n1Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12202, USA  \n2Department of Mechatronics Engineering, Jeju National University, Jeju-si 63243, Republic of Korea  \n3Department of Electronics Engineering, Gachon University, Seongnam-si, Gyeonggi-do 13120, South Korea  \n4Department of Computer Science, School of Physics, Engineering, and Computer Science, University of Hertfordshire, AL10 9AB Hatfield, U.K.  \n5Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia  \n6Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kaferelshikh University, Kafr El-Shaikh 33516, Egypt  \n7Department of Healthcare Biotechnology, Atta-Ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad 44000, Pakistan  \n8Department of AI and Software, Gachon University, Seongnam-si 13120, South Korea  \nCorresponding authors: Sobia Manzoor ([drsobia.manzoor@asab.nust.edu.pk](drsobia.manzoor@asab.nust.edu.pk)) and Muhammad Shahid Anwar ([shahidanwar786@gachon.ac.kr](shahidanwar786@gachon.ac.kr))  \nABSTRACT Viral and non-viral hepatocellular carcinoma (HCC) is becoming predominant in developing countries. A major issue linked to HCC-related mortality rate is the late diagnosis of cancer development. Although traditional approaches to diagnosing HCC have become gold-standard, there remain several limitations due to which the confirmation of cancer progression takes a longer period. The recent emergence of artificial intelligence tools with the capacity to analyze biomedical datasets is assisting traditional diagnostic approaches for early diagnosis with certainty. Here we present a review of traditional HCC diagnostic approaches versus the use of artificial intelligence (Machine Learning and Deep Learning) for HCC diagnosis. The overview of the cancer-related databases along with the use of AI in histopathology, radiology, biomarker, and electronic health records (EHRs) based HCC diagnosis is given.  \nINDEX TERMS Hepatocellular carcinoma (HCC), viral cancers, artificial intelligence, cancer diagnosis, traditional cancer diagnostic.  \nI. INTRODUCTION  \nApproximately, 80% of the hepatocellular carcinoma are caused by chronic viral infections including Hepatitis C Virus (HCV) and Hepatis B Virus (HBV) [1] . World Health Organization (WHO) aims to reduce the CHV incidence by 90% and mortality by 65% before 2030 according to the strategy on viral hepatitis prepared in 2016 . Globally, two million deaths are caused due to hepatocellular carcinoma (HCC)  \n[2] . Hepatitis C virus and hepatitis B virus are the major cause of chronic liver disease leading to HCC development especially in developing countries [3], [4], [5] . As early diagnosis of liver inflammation causing fibrosis and cirrhosis is crucial for better treatment outcomes [6] . The current  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Inês Domingues  .  \ninvasive method, biopsy, for liver fibrosis analysis is complicated and present the challenges of bleeding, severe pain, observer variability, sampling errors and increased chances of infection [7] . Although, another method of ultrasound elastography is emerging but it has limitations with measuring stiffness of liver tissue owing to interference offactors such as tissue inflammation, hepatic vein congestion, obesity, meal, etc., which lead to misinterpretation of acquired data [8] . Comparatively, there i","cbCaiqXmQ5lgWzb3","https://ap.wps.com/l/cbCaiqXmQ5lgWzb3","pdf",2378267,1,15,"English","en",105,"# Introduction\n## Limitations of current diagnostic approaches\n## Need for less invasive diagnosis\n# Traditional statistical and diagnostic models\n## Regression-based approaches\n## Challenges in fibrosis assessment\n# Artificial intelligence for HCC diagnosis\n## Machine learning and deep learning overview\n## Applications across data modalities","[{\"question\":\"Why is early diagnosis critical in hepatocellular carcinoma (HCC)?\",\"answer\":\"Late diagnosis increases HCC-related mortality. Early identification is important for timely assessment of disease development and progression.\"},{\"question\":\"What limitations affect traditional HCC diagnostic methods?\",\"answer\":\"Conventional methods can require invasive procedures like biopsy, which brings risks such as bleeding, pain, sampling errors, and infection, and can also face observer variability. Other approaches, such as ultrasound elastography, may be affected by factors like inflammation, congestion, obesity, and meals.\"},{\"question\":\"How does machine learning improve HCC diagnosis compared with traditional approaches?\",\"answer\":\"Machine learning models can capture non-linear and complex interactions in large datasets, helping improve prediction for diagnosis, staging, and prognosis. The review highlights AI’s use across histopathology, radiology, biomarkers, and electronic health records.\"}]","Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma | PDF",1785934670,38,{"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-based-diagnostic-paradigm-in-viral-and-non-viral-hepatocellular-carcinoma","",{"@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-based-diagnostic-paradigm-in-viral-and-non-viral-hepatocellular-carcinoma/126767/",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","2026-08-05",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},"Why is early diagnosis critical in hepatocellular carcinoma (HCC)?","Question",{"text":75,"@type":76},"Late diagnosis increases HCC-related mortality. Early identification is important for timely assessment of disease development and progression.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations affect traditional HCC diagnostic methods?",{"text":80,"@type":76},"Conventional methods can require invasive procedures like biopsy, which brings risks such as bleeding, pain, sampling errors, and infection, and can also face observer variability. Other approaches, such as ultrasound elastography, may be affected by factors like inflammation, congestion, obesity, and meals.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning improve HCC diagnosis compared with traditional approaches?",{"text":84,"@type":76},"Machine learning models can capture non-linear and complex interactions in large datasets, helping improve prediction for diagnosis, staging, and prognosis. The review highlights AI’s use across histopathology, radiology, biomarkers, and electronic health records.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]