Artificial intelligence for enhancing decision-making in multidisciplinary tumor boards for HCC in China

Abstract

Hepatocellular carcinoma (HCC) exhibits high incidence and mortality rates in China, posing a significant public health burden. Established risk factors, including hepatitis B virus, hepatitis C virus, aflatoxin B1 exposure, alcohol consumption, and smoking, shape the unique epidemiological profile of HCC in China and exacerbate its marked tumor heterogeneity. This complexity leads to highly intricate prognostic assessment, management strategies, and predictive approaches across diverse patient populations. The updated “Guidelines for Diagnosis and Treatment of Primary Liver Cancer (2024 Edition)” reflect significant advancements in screening, diagnosis, staging, treatment, and follow-up, with particular emphasis on management strategies tailored to the Chinese context. The multidisciplinary tumor board (MDTB) serves as a cornerstone of modern oncology care. By integrating expertise from diverse medical specialties, the MDTB is crucial for developing individualized treatment plans for complex HCC cases. However, current MDTB practice faces significant challenges, primarily stemming from the rapid evolution of treatment options and the swift advancement of emerging technologies, particularly artificial intelligence (AI). This necessitates continuous learning among MDTB members to effectively integrate cutting-edge therapies and tools. This review focuses on the disease characteristics of HCC in China and the unmet needs within its clinical management. It delves into how AI technologies can enhance the capabilities of MDTBs, aiming to elucidate the transformative potential and persisting challenges of AI-driven multidisciplinary care models for HCC in China.

Keywords

Artificial intelligence / multidisciplinary tumor boards / hepatocellular carcinoma / deep learning / convolutional neural network

References

[1] Qin Y,Li J.Liver cancer in China: the analysis of mortality and burden of disease trends from 2008 to 2021.BMC Cancer2024;24:594 PMCID:PMC11097423

[2] Shen C,Li M.Hepatitis virus and hepatocellular carcinoma: recent advances.Cancers2023;15:533 PMCID:PMC9856776

[3] Oncology Society of Chinese Medical Association. [Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2024 edition)].Zhonghua Zhong Liu Za Zhi2024;46:805-43

[4] Sharma S.Benefits or concerns of AI: a multistakeholder responsibility.Futures2024;157:103328

[5] Yüksel N,Sezer HK.Review of artificial intelligence applications in engineering design perspective.Eng Appl Artif Intell2023;118:105697

[6] Sarker IH.Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions.SN Comput Sci2021;2:420 PMCID:PMC8372231

[7] Mienye ID,Obaido G,Ilono P.Deep convolutional neural networks in medical image analysis: a review.Information2025;16:195

[8] Nguyen-Tat TB,Nam PT.Evaluating pre-processing and deep learning methods in medical imaging: combined effectiveness across multiple modalities.Alex Eng J2025;119:558-86

[9] Xia TY,Meng XP.Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model.Radiology2023;307:e222729

[10] Bae JS,Kim H,Lee JY.Deep learning-aided ¹H-MR spectroscopy for differentiating between patients with and without hepatocellular carcinoma.Magn Reson Med Sci2025;mp.2025-0064

[11] Hou J,Vogel A.Comparative evaluation of multimarker algorithms for early-stage HCC detection in multicenter prospective studies.JHEP Rep2025;7:101263 PMCID:PMC11782856

[12] Potter LN,Dempsey W,Nahum-Shani I.Integrating intensive longitudinal data (ILD) to inform the development of dynamic theories of behavior change and intervention design: a case study of scientific and practical considerations.Prev Sci2023;24:1659-71 PMCID:PMC10576833

[13] Hao S,Wang J.Dynamic modeling for multivariate functional and longitudinal data.J Econometrics2024;239:105573

[14] Diao S,Hu W.Weakly supervised framework for cancer region detection of hepatocellular carcinoma in whole-slide pathologic images based on multiscale attention convolutional neural network.Am J Pathol2022;192:553-63

[15] Banerjee T,Kour P.A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection.Sci Rep2025;15:29908 PMCID:PMC12354780

[16] Prajumwongs P,Thanasukarn V.Serum peptide biomarkers by MALDI-TOF MS coupled with machine learning for diagnosis and classification of hepato-pancreato-biliary cancers.Sci Rep2025;15:29169 PMCID:PMC12335492

[17] Zhang G,Yan C,Luo J.A novel liver cancer diagnosis method based on patient similarity network and DenseGCN.Sci Rep2022;12:6797 PMCID:PMC9043215

[18] Sucularli C.Machine learning-based identification of diagnostic and prognostic mitotic cell cycle genes in hepatocellular carcinoma.PLoS One2025;20:e0331118 PMCID:PMC12393733

[19] Xie DY,Ren ZG,Fan J.A review of 2022 Chinese clinical guidelines on the management of hepatocellular carcinoma: updates and insights.Hepatobiliary Surg Nutr2023;12:216-28 PMCID:PMC10129899

[20] Xu X,Zhu Z.A comprehensive review on synergy of multi-modal data and AI technologies in medical diagnosis.Bioengineering2024;11:219 PMCID:PMC10967767

[21] Yang T,Xu XF,Wu H.Management of hepatocellular carcinoma in China: seeking common grounds while reserving differences.Clin Mol Hepatol2023;29:342-4 PMCID:PMC10121302

[22] Xu Y,Li H.Survey of hepatitis B virus infection for liver cancer screening in China: a population-based, cross-sectional study.Chin Med J2024;137:1414-20 PMCID:PMC11188860

[23] Zhang CH,Zhang S,Gao Q.Changing epidemiology of hepatocellular carcinoma in Asia.Liver Int2022;42:2029-41

[24] Bialecki ES.Diagnosis of hepatocellular carcinoma.HPB2005;7:26-34 PMCID:PMC2023919

[25] Wang X,Yang N.Evaluation of the combined application of AFP, AFP-L3%, and DCP for hepatocellular carcinoma diagnosis: a meta-analysis.Biomed Res Int2020;2020:5087643 PMCID:PMC7519464

[26] Tu X,Huang Y,Yang M.An overview of large AI models and their applications.Vis Intell2024;2:65

[27] Alqahtani T,Alrashed M.The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research.Res Social Adm Pharm2023;19:1236-42

[28] Yu X.Application of the multi-omics liquid biopsy method M2P-HCC in early liver cancer screening for high-risk individuals with hepatitis B-related liver cancer.Diagnostics2023;13:2484 PMCID:PMC10417463

[29] Zhou J,Wang Z.Guidelines for the diagnosis and treatment of hepatocellular carcinoma (2019 Edition).Liver Cancer2020;9:682-720 PMCID:PMC7768108

[30] El Atifi W, El Rhazouani O, Khan FM, Sekkat H. Optimizing ensemble machine learning models for accurate liver disease prediction in healthcare.PLoS One2025;20:e0330899 PMCID:PMC12393716

[31] Sang H,Lee M.Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.Sci Rep2024;14:14966 PMCID:PMC11213851

[32] Abdelhamed W.Integrating artificial intelligence into multidisciplinary evaluations of HCC: opportunities and challenges.Hepatoma Res2025;11:8

[33] Seven İ,Arslan H.Predicting hepatocellular carcinoma survival with artificial intelligence.Sci Rep2025;15:6226 PMCID:PMC11842547

[34] Zhang ZM,Liu G.Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.Sci Rep2024;14:5274 PMCID:PMC10912761

[35] Chartampilas E,Georgopoulou V,Hatzidakis A.Current imaging diagnosis of hepatocellular carcinoma.Cancers2022;14:3997 PMCID:PMC9406360

[36] Vengateswaran HT,You HW,Bhavar GB.Hepatocellular carcinoma imaging: exploring traditional techniques and emerging innovations for early intervention.Med Nov Technol Devices2024;24:100327

[37] Tu J,Wang X.Current status and new directions for hepatocellular carcinoma diagnosis.Liver Res2024;8:218-36 PMCID:PMC11771281

[38] Romeo M,Napolitano C.Clinical applications of artificial intelligence (AI) in human cancer: is it time to update the diagnostic and predictive models in managing hepatocellular carcinoma (HCC)?.Diagnostics2025;15:252 PMCID:PMC11817573

[39] Chatzipanagiotou OP,Vailas M.Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature.J Gastroenterol Hepatol2024;39:1994-2005

[40] Liu Z,Hong Y. Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study. arXiv 2021;arXiv:2106.00090. Available from: https://doi.org/10.48550/arXiv.2106.00090 [accessed 18 Dec 2025]

[41] Ennab M.Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.Front Robot AI2024;11:1444763 PMCID:PMC11638409

[42] Tiwari A,Kuo TR.Current AI technologies in cancer diagnostics and treatment.Mol Cancer2025;24:159 PMCID:PMC12128506

[43] Yates J.New horizons at the interface of artificial intelligence and translational cancer research.Cancer Cell2025;43:708-27 PMCID:PMC12007700

[44] Richter M, Emden D, Leenings R, et al.; MBB consortium, FOR2107 consortium, PRONIA consortium. Generalizability of clinical prediction models in mental health.Mol Psychiatry2025;30:3632-9 PMCID:PMC12240828

[45] Futoma J,Panch T,Celi LA.The myth of generalisability in clinical research and machine learning in health care.Lancet Digit Health2020;2:e489-92 PMCID:PMC7444947

[46] Yang J,Clifton DA.Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening.NPJ Digit Med2022;5:69 PMCID:PMC9174159

[47] Chang Q,Zhou M.Mining multi-center heterogeneous medical data with distributed synthetic learning.Nat Commun2023;14:5510 PMCID:PMC10484909

[48] Harding-Theobald E,Maraj B.Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma.Aliment Pharmacol Ther2021;54:890-901 PMCID:PMC8435007

[49] Yao S,Wei Y,Song B.Radiomics in hepatocellular carcinoma: a state-of-the-art review.World J Gastrointest Oncol2021;13:1599-615 PMCID:PMC8603458

[50] Jiang C,Yang JJ.Radiomics in the diagnosis and treatment of hepatocellular carcinoma.Hepatobiliary Pancreat Dis Int2023;22:346-51

[51] Avery E,Aboian M.Radiomics: a primer on processing workflow and analysis.Semin Ultrasound CT MR2022;43:142-6 PMCID:PMC8961004

[52] Dong D,Liu Z.Radiomics and multiomics research. In: Liu S, editor. Artificial intelligence in medical imaging in China. Singapore: Springer Nature; 2024. pp. 63-81.

[53] Lambin P,Deist TM.Radiomics: the bridge between medical imaging and personalized medicine.Nat Rev Clin Oncol2017;14:749-62

[54] Gurzu S,Jung I.Combined hepatocellular-cholangiocarcinoma: from genesis to molecular pathways and therapeutic strategies.J Cancer Res Clin Oncol2024;150:270 PMCID:PMC11116183

[55] An L,Zhang S.Hepatocellular carcinoma and intrahepatic cholangiocarcinoma incidence between 2006 and 2015 in China: estimates based on data from 188 population-based cancer registries.Hepatobiliary Surg Nutr2023;12:45-55 PMCID:PMC9944524

[56] Mirbabaie M,Frick NRJ.Artificial intelligence in disease diagnostics: a critical review and classification on the current state of research guiding future direction.Health Technol2021;11:693-731

[57] Midya A,Srouji R.Computerized diagnosis of liver tumors from CT scans using a deep neural network approach.IEEE J Biomed Health Inform2023;27:2456-64 PMCID:PMC10245221

[58] Stollmayer R,Tóth A.Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging.World J Gastroenterol2021;27:5978-88 PMCID:PMC8475009

[59] Maniaci A,Gagliano C.The integration of radiomics and artificial intelligence in modern medicine.Life 224;14:1248 PMCID:PMC11508875

[60] Youssef A,Thakur A,Clifton D.External validation of AI models in health should be replaced with recurring local validation.Nat Med2023;29:2686-7

[61] Khalifa M.AI in diagnostic imaging: revolutionising accuracy and efficiency.Comput Methods Programs Biomed Update2024;5:100146

[62] Vrettos K,Marias K,Klontzas ME.Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence.BJR AI2024;1:ubae011

[63] Yan T,Zhang N.The advanced development of molecular targeted therapy for hepatocellular carcinoma.Cancer Biol Med2022;19:802-17 PMCID:PMC9257319

[64] Chan YT,Wu J.Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma.Mol Cancer2024;23:189 PMCID:PMC11378508

[65] Nair M,Sharma AK.Cancer molecular markers: a guide to cancer detection and management.Semin Cancer Biol2018;52:39-55

[66] Dwivedi YK,Ismagilova E.Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy.Int J Inf Manag2021;57:101994

[67] Gao K,Wang Y,Xu T.Bi-regional machine learning radiomics based on CT noninvasively predicts LOX expression level and overall survival in hepatocellular carcinoma.Cancer Med2025;14:e71154 PMCID:PMC12340542

[68] Cheng B,Zhao Y.Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation.Eur Radiol2022;32:5869-79

[69] European Association for the Study of the Liver. EASL Clinical Practice Guidelines on the management of hepatocellular carcinoma.J Hepatol2025;82:315-74

[70] Singal AG,Yarchoan M.AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma.Hepatology2023;78:1922-65 PMCID:PMC10663390

[71] Li S,Zhou F.Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.J Appl Clin Med Phys2024;25:e14397 PMCID:PMC11302809

[72] Memeo R,Deshayes E.Optimization of the future remnant liver: review of the current strategies in Europe.Hepatobiliary Surg Nutr2021;10:350-63 PMCID:PMC8188135

[73] Kaplan DE,Thiele M.AASLD Practice Guidance on risk stratification and management of portal hypertension and varices in cirrhosis.Hepatology2024;79:1180-211

[74] Zeng X,Dong Y.Impact of three-dimensional reconstruction visualization technology on short-term and long-term outcomes after hepatectomy in patients with hepatocellular carcinoma: a propensity-score-matched and inverse probability of treatment-weighted multicenter study.Int J Surg2024;110:1663-76 PMCID:PMC10942183

[75] Zeng L,Guo P.Meta-analysis of the effects of three-dimensional visualized medical techniques hepatectomy for liver cancer with and without the treatment of sorafenib.Evid Based Complement Alternat Med2022;2022:4507673 PMCID:PMC9489363

[76] Chansangrat J.Radioembolization for hepatocellular carcinoma: updated strategies and evolving clinical applications.Hepatoma Res2024;10:49

[77] Yang Y,Qi L.Combined radiofrequency ablation or microwave ablation with transarterial chemoembolization can increase efficiency in intermediate-stage hepatocellular carcinoma without more complication: a systematic review and meta-analysis.Int J Hyperthermia2022;39:455-65

[78] Bartnik K,Bartczak T.A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.Sci Rep2024;14:14779 PMCID:PMC11208561

[79] Kokabi N,Chen B.Voxel-based dosimetry predicting treatment response and related toxicity in HCC patients treated with resin-based Y90 radioembolization: a prospective, single-arm study.Eur J Nucl Med Mol Imaging2023;50:1743-52 PMCID:PMC10119065

[80] Bibault JE.Deep learning for automated segmentation in radiotherapy: a narrative review.Br J Radiol2024;97:13-20 PMCID:PMC11027240

[81] Lee CL,Burak KW.Real-world outcomes of atezolizumab with bevacizumab treatment in hepatocellular carcinoma patients: effectiveness, esophagogastroduodenoscopy utilization and bleeding complications.Cancers2024;16:2878 PMCID:PMC11352899

[82] European Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma.J Hepatol2018;69:182-236

[83] Kinsey E.Management of hepatocellular carcinoma in 2024: the multidisciplinary paradigm in an evolving treatment landscape.Cancers2024;16:666 PMCID:PMC10854554

[84] Xu F,Chen J.Recent progress on the application of compound formulas of traditional Chinese medicine in clinical trials and basic research in vivo for chronic liver disease.J Ethnopharmacol2024;321:117514

[85] Guan Y.Liver cancer: Zheng classification of Qi stagnation and blood stasis.Pharmacol Pharm2014;5:75-82

[86] Gu Z,Zhai X.Study on TCM syndrome differentiation of primary liver cancer based on the analysis of latent structural model.Evid Based Complement Alternat Med2015;2015:761565 PMCID:PMC4359838

[87] Yang SR,Luo D,Liang FX.Unlocking the potential: how acupuncture reshapes the liver-centered lipid metabolism pattern to fight obesity.J Integr Med2024;22:523-32

[88] Lei Y.Bibliometric analysis of traditional Chinese medicine in cancer treatment via immune system modulation (2015-2025).Front Immunol2025;16:1581885 PMCID:PMC12095241

[89] Zhang H,Zeng M.Investigating the metabolic level of endogenous and Exogenous Substances On The Intervention Of Traditional Chinese medicine Fuzheng Yiliu Decoction in a rat orthotopic liver cancer model.Cancer Manag Res2022;14:2785-801 PMCID:PMC9492441

[90] Zhang JX,Chen J.Xiaojianzhong decoction prevents gastric precancerous lesions in rats by inhibiting autophagy and glycolysis in gastric mucosal cells.World J Gastrointest Oncol2023;15:464-89 PMCID:PMC10052669

[91] Zhang L,Li XH,Wang HJ.Effect of modified Huqi prescription on quality of life in patients with primary liver cancer of Zhengqi deficiency and toxin-stasis binding syndrome after transcatheter arterial chemoembolization: a retrospective cohort study.J Tradit Chinese Med2019;60:306-10

[92] Yu YX,Liu ZN.Traditional Chinese medicine in the era of immune checkpoint inhibitor: theory, development, and future directions.Chin Med2023;18:59 PMCID:PMC10199665

[93] Zao X,Liang Y.The Chinese herbal KangXianYiAi formula inhibits hepatocellular carcinoma by reducing glutathione and inducing ferroptosis.Pharmacol Res Mod Chin Med2023;8:100276

[94] Ge Y,Zhang W.Effect of the self-designed peiyuan jiedu tongluo decoction combined with conventional western medicine on postoperative syndrome of liver cancer patients treated with transcatheter arterial chemoembolization.Hebei Med J2023;45:3581-3

[95] Han L,Wu W.Research progress on the therapeutic effects of effective components of traditional Chinese medicine in the treatment of gastric cancer precursors through modulation of multiple signaling pathways.Front Oncol2025;15:1555274 PMCID:PMC12127168

[96] Bilal M,Malik N.NLP for analyzing electronic health records and clinical notes in cancer research: a review.J Pain Symptom Manage2025;69:e374-94

[97] Shi Z,Hu B.A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trial.Nat Commun2025;

[98] Zhang H,Zhang K,Shen Z.CT-based deep learning radiomics model for predicting proliferative hepatocellular carcinoma: application in transarterial chemoembolization and radiofrequency ablation.BMC Med Imaging2025;25:363 PMCID:PMC12403943

[99] Liang J,Zhang J.Diagnostic performance of [¹⁸F]FAPI-04 PET/CT in suspected recurrent hepatocellular carcinoma: prospective comparison with contrast-enhanced CT/MRI.Eur J Nucl Med Mol Imaging2025;52:3951-62

[100] Rompianesi G,Ceresa CD,Troisi RI.Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases.World J Gastroenterol2022;28:108-22 PMCID:PMC8793013

[101] Magrabi F,McNair JB.Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications.Yearb Med Inform2019;28:128-34 PMCID:PMC6697499

[102] Tan S,Wu D.ChatGPT in medicine: prospects and challenges: a review article.Int J Surg2024;110:3701-6 PMCID:PMC11175750

[103] Hou H,Li J.Artificial intelligence in the clinical laboratory.Clin Chim Acta2024;559:119724

[104] Bo Z,He Q.Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma.Comput Biol Med2024;173:108337