Realizing the Promise of Artificial Intelligence in Hepatocellular Carcinoma through Opportunities and Recommendations for Responsible Translation


  • Tamer A. Addissouky Department of Biochemistry, Science Faculty, Menoufia University, Menoufia; Al-Hadi University College, Baghdad, Iraq; MLS ASCP, United States; MLS Ministry of Health, Alexandria, Egypt
  • Majeed M. A. Ali Al-Hadi University College, Baghdad, Iraq
  • Ibrahim El Tantawy El Sayed Department of Biochemistry, Science Faculty, Menoufia University, Menoufia, Egypt
  • Mahmood Hasen Shuhata Alubiady Al-Hadi University College, Baghdad, Iraq



Artificial Intelligence, Deep Learning, Imaging, Machine Learning, Hepatocellular carcinoma


This study aims to provide an overview of the current state-of-the-art applications of artificial intelligence (AI) and machine learning in the management of hepatocellular carcinoma (HCC), and to explore future directions for continued progress in this emerging field.  This study is a comprehensive literature review that synthesizes recent findings and advancements in the application of AI and machine learning techniques across various aspects of HCC care, including screening and early detection, diagnosis and staging, prognostic modeling, treatment planning, interventional guidance, and monitoring of treatment response. The review draws upon a wide range of published research studies, focusing on the integration of AI and machine learning with diverse data sources, such as medical imaging, clinical data, genomics, and other multimodal information.  The results demonstrate that AI-based systems have shown promise in improving the accuracy and efficiency of HCC screening, diagnosis, and tumor characterization compared to traditional methods. Machine learning models integrating clinical, imaging, and genomic data have outperformed conventional staging systems in predicting survival and recurrence risk. AI-based recommendation systems have the potential to optimize personalized therapy selection, while augmented reality techniques can guide interventional procedures in real-time. Moreover, longitudinal application of AI may enhance the assessment of treatment response and recurrence monitoring. Despite these promising findings, the review highlights the need for rigorous multicenter prospective validation studies, standardized multimodal datasets, and thoughtful consideration of ethical implications before widespread clinical implementation of AI technologies in HCC management.

Author Biography

Tamer A. Addissouky , Department of Biochemistry, Science Faculty, Menoufia University, Menoufia; Al-Hadi University College, Baghdad, Iraq; MLS ASCP, United States; MLS Ministry of Health, Alexandria

Al-Hadi University College, Baghdad, Iraq. Department of Biochemistry, Science Faculty, Menoufia University, Menoufia, Egypt MLS ministry of health, Alexandria, Egypt. MLS ASCP, United States Corresponding Author: Tamer A. Addissouky, Al-HADI University College, Baghdad. Iraq. - Department of Biochemistry, Science Faculty, Menoufia University, Egypt. - MLS ministry of health, Alexandria, Egypt.  - MLS, ASCP, USA.    Email:;;      


B. Foglia, C. Turato, and S. Cannito, “Hepatocellular Carcinoma: Latest Research in Pathogenesis, Detection and Treatment,” Int J Mol Sci, vol. 24, no. 15, p. 12224, Jul. 2023, doi: 10.3390/ijms241512224.

T. A. Addissouky et al., “Latest advances in hepatocellular carcinoma management and prevention through advanced technologies,” Egyptian Liver Journal, vol. 14, no. 1, p. 2, Jan. 2024, doi: 10.1186/s43066-023-00306-3.

T. A. Addissouky et al., “Preclinical Promise and Clinical Challenges for Innovative Therapies Targeting Liver Fibrogenesis,” Archives of Gastroenterology Research, vol. 4, no. 1, pp. 14–23, Nov. 2023, doi: 10.33696/Gastroenterology.4.044.

T. A. Addissouky, Y. Wang, F. A. K. Megahed, A. E. El Agroudy, I. E. T. El Sayed, and A. M. A. K. El-Torgoman, “Novel biomarkers assist in detection of liver fibrosis in HCV patients,” Egyptian Liver Journal, vol. 11, no. 1, p. 86, Dec. 2021, doi: 10.1186/s43066-021-00156-x.

T. A. Addissouky, A. E. El Agroudy, and A. A. Eltorgman, “Efficiency of alternative markers to assess liver fibrosis levels in viral hepatitis B patients.,” Biomedical Research, vol. 30, no. 2, 2019, doi: 10.35841/biomedicalresearch.30-19-107.

J. Albarrak and H. Al-Shamsi, “Current Status of Management of Hepatocellular Carcinoma in The Gulf Region: Challenges and Recommendations,” Cancers (Basel), vol. 15, no. 7, p. 2001, Mar. 2023, doi: 10.3390/cancers15072001.

S. S. Elbalka, A. Abdallah, and I. H. Metwally, “Hepatocellular carcinoma associated other primaries: common types and prognosis,” Egyptian Liver Journal, vol. 13, no. 1, p. 5, Feb. 2023, doi: 10.1186/s43066-023-00241-3.

H. Innes and P. Nahon, “Statistical perspectives on using hepatocellular carcinoma risk models to inform surveillance decisions,” J Hepatol, vol. 79, no. 5, pp. 1332–1337, Nov. 2023, doi: 10.1016/j.jhep.2023.05.005.

T. A. Addissouky, A. E. El-Agroudy, A. Moneim, A. K. El-Torgoman, and I. E. El-Sayed, “Efficacy of Biomarkers in Detecting Fibrosis Levels of Liver Diseases,” World Journal of Medical Sciences, vol. 16, no. 1, pp. 11–18, 2019, doi: 10.5829/idosi.wjms.2019.11.18.

C. Larrain, A. Torres-Hernandez, and D. B. Hewitt, “Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma,” Livers, vol. 4, no. 1, pp. 36–50, Jan. 2024, doi: 10.3390/livers4010004.

J. Calderaro, T. P. Seraphin, T. Luedde, and T. G. Simon, “Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma,” J Hepatol, vol. 76, no. 6, pp. 1348–1361, Jun. 2022, doi: 10.1016/j.jhep.2022.01.014.

S. Feng et al., “Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma,” J Clin Transl Hepatol, vol. 000, no. 000, pp. 000–000, May 2023, doi: 10.14218/JCTH.2022.00077S.

B. Schmauch et al., “Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery,” J Pathol Inform, p. 100360, Dec. 2023, doi: 10.1016/j.jpi.2023.100360.

A. E. El Agroudy, M. S. Elghareb, T. A. Addissouky, E. H. Elshahat, and E. H. Hafez, “Serum hyaluronic acid as non invasive biomarker to predict liver fibrosis in viral hepatitis patients.,” Journal of Bioscience and Applied Research, vol. 2, no. 5, pp. 326–333, May 2016, doi: 10.21608/jbaar.2016.108377.

A. E. El Agroudy, M. S. Elghareb, T. A. Addissouky, E. H. Elshahat, and E. H. Hafez, “Biochemical study of some non invasive markers in liver fibrosis patients.,” Journal of Bioscience and Applied Research, vol. 2, no. 5, pp. 319–325, May 2016, doi: 10.21608/jbaar.2016.108375.

X. Shen et al., “Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework,” Front Genet, vol. 14, Mar. 2023, doi: 10.3389/fgene.2023.1004481.

T. A. Addissouky, A. E. El Agroudy, and A. A. Khalil, “Developing a Novel Non-invasive Serum-based Diagnostic Test for Early Detection of Colorectal Cancer,” Am J Clin Pathol, vol. 160, no. Supplement_1, pp. S17–S17, Nov. 2023, doi: 10.1093/ajcp/aqad150.037.

A. Mansur et al., “The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities,” Cancers (Basel), vol. 15, no. 11, p. 2928, May 2023, doi: 10.3390/cancers15112928.

T. A. Addissouky et al., “Can Vaccines Stop Cancer Before It Starts? Assessing the Promise of Prophylactic Immunization Against High-Risk Preneoplastic Lesions,” J Cell Immunol, vol. 5, no. 4, pp. 127–140, Nov. 2023, doi: 10.33696/immunology.5.178.

A. Bakrania, N. Joshi, X. Zhao, G. Zheng, and M. Bhat, “Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases,” Pharmacol Res, vol. 189, p. 106706, Mar. 2023, doi: 10.1016/j.phrs.2023.106706.

T. A. Addissouky and A. A. Khalil, “Detecting Lung Cancer Stages Earlier By Appropriate Markers Rather Than Biopsy And Other Techniques,” Am J Clin Pathol, vol. 154, no. Supplement_1, pp. S146–S147, Oct. 2020, doi: 10.1093/ajcp/aqaa161.320.

Q. Wei, N. Tan, S. Xiong, W. Luo, H. Xia, and B. Luo, “Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis,” Cancers (Basel), vol. 15, no. 23, p. 5701, Dec. 2023, doi: 10.3390/cancers15235701.

S. Singh, S. Hoque, A. Zekry, and A. Sowmya, “Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review,” J Med Syst, vol. 47, no. 1, p. 73, Jul. 2023, doi: 10.1007/s10916-023-01968-7.

C. Hsieh et al., “Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma,” Radiology, vol. 309, no. 2, Nov. 2023, doi: 10.1148/radiol.222891.

C. Zhang et al., “Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment,” J Hematol Oncol, vol. 16, no. 1, p. 114, Nov. 2023, doi: 10.1186/s13045-023-01514-5.

H.-X. Gu, X.-S. Huang, J.-X. Xu, P. Zhu, J.-F. Xu, and S.-F. Fan, “Diagnostic Value of MRI Features in Dual-phenotype Hepatocellular Carcinoma: A Preliminary Study,” J Digit Imaging, vol. 36, no. 6, pp. 2554–2566, Dec. 2023, doi: 10.1007/s10278-023-00888-9.

S. M. Hosseiniyan Khatibi et al., “Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches,” Sci Rep, vol. 13, no. 1, p. 3840, Mar. 2023, doi: 10.1038/s41598-023-30720-x.

M. Sato et al., “Development of a transformer model for predicting the prognosis of patients with hepatocellular carcinoma after radiofrequency ablation,” Hepatol Int, vol. 18, no. 1, pp. 131–137, Feb. 2024, doi: 10.1007/s12072-023-10585-y.

C. D. Christou and G. Tsoulfas, “Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!,” World J Gastroenterol, vol. 29, no. 48, pp. 6168–6178, Dec. 2023, doi: 10.3748/wjg.v29.i48.6168.

R. Najjar, “Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging,” Diagnostics, vol. 13, no. 17, p. 2760, Aug. 2023, doi: 10.3390/diagnostics13172760.

I. Skalidis et al., “Cardiology in the digital era: from artificial intelligence to Metaverse, paving the way for future advancements,” Future Cardiol, vol. 19, no. 16, pp. 755–758, Dec. 2023, doi: 10.2217/fca-2023-0106.

C. Lanza et al., “Robotics in Interventional Radiology: Review of Current and Future Applications,” Technol Cancer Res Treat, vol. 22, p. 153303382311520, Jan. 2023, doi: 10.1177/15330338231152084.

A. A. Gumbs et al., “Surgomics and the Artificial intelligence, Radiomics, Genomics, Oncopathomics and Surgomics (AiRGOS) Project,” Artificial Intelligence Surgery, vol. 3, no. 3, pp. 180–5, Sep. 2023, doi: 10.20517/ais.2023.24.

P. Basthi Mohan, R. Lochan, and S. Shetty, “Biomarker in Hepatocellular Carcinoma,” Indian J Surg Oncol, Jan. 2024, doi: 10.1007/s13193-023-01858-x.

Y. Han, J. Akhtar, G. Liu, C. Li, and G. Wang, “Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning,” Comput Struct Biotechnol J, vol. 21, pp. 3478–3489, 2023, doi: 10.1016/j.csbj.2023.07.002.

R. Qin, T. Jin, and F. Xu, “Biomarkers predicting the efficacy of immune checkpoint inhibitors in hepatocellular carcinoma,” Front Immunol, vol. 14, Dec. 2023, doi: 10.3389/fimmu.2023.1326097.

A. Jaffe, T. H. Taddei, E. G. Giannini, Y. C. Ilagan?Ying, M. Colombo, and M. Strazzabosco, “Holistic management of hepatocellular carcinoma: The hepatologist’s comprehensive playbook,” Liver International, vol. 42, no. 12, pp. 2607–2619, Dec. 2022, doi: 10.1111/liv.15432.

O. Siddique et al., “The importance of a multidisciplinary approach to hepatocellular carcinoma,” J Multidiscip Healthc, vol. Volume 10, pp. 95–100, Mar. 2017, doi: 10.2147/JMDH.S128629.

P. Fitzmorris, M. Shoreibah, B. S. Anand, and A. K. Singal, “Management of hepatocellular carcinoma,” J Cancer Res Clin Oncol, vol. 141, no. 5, pp. 861–876, May 2015, doi: 10.1007/s00432-014-1806-0.

A. D. Maharaj et al., “Monitoring quality of care in hepatocellular carcinoma: A modified Delphi consensus,” Hepatol Commun, vol. 6, no. 11, pp. 3260–3271, Nov. 2022, doi: 10.1002/hep4.2089.

T. Ito and M. H. Nguyen, “Perspectives on the Underlying Etiology of HCC and Its Effects on Treatment Outcomes,” J Hepatocell Carcinoma, vol. Volume 10, pp. 413–428, Mar. 2023, doi: 10.2147/JHC.S347959.

T. Addissouky, “Detecting Liver Fibrosis by Recent Reliable Biomarkers in Viral Hepatitis Patients,” Am J Clin Pathol, vol. 152, no. Supplement_1, pp. S85–S85, Sep. 2019, doi: 10.1093/ajcp/aqz117.000.

J. M. Schattenberg, N. Chalasani, and N. Alkhouri, “Artificial Intelligence Applications in Hepatology,” Clinical Gastroenterology and Hepatology, vol. 21, no. 8, pp. 2015–2025, Jul. 2023, doi: 10.1016/j.cgh.2023.04.007.

Q. Zhao, Y. Lan, X. Yin, and K. Wang, “Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis,” BMC Med Imaging, vol. 23, no. 1, p. 208, Dec. 2023, doi: 10.1186/s12880-023-01172-6.

F. Schön et al., “Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma,” Sci Rep, vol. 14, no. 1, p. 590, Jan. 2024, doi: 10.1038/s41598-023-50451-3.

A. Siam, A. R. Alsaify, B. Mohammad, Md. R. Biswas, H. Ali, and Z. Shah, “Multimodal deep learning for liver cancer applications: a scoping review,” Front Artif Intell, vol. 6, Oct. 2023, doi: 10.3389/frai.2023.1247195.

T. A. Addissouky, A. A. Khalil, and A. E. El Agroudy, “Assessment of potential biomarkers for early detection and management of Glomerulonephritis patients with diabetic diseases,” Am J Clin Pathol, vol. 160, no. Supplement_1, pp. S18–S19, Nov. 2023, doi: 10.1093/ajcp/aqad150.040.

T. Addissouky, M. Ali, I. E. T. El Sayed, and Y. Wang, “Revolutionary Innovations in Diabetes Research: From Biomarkers to Genomic Medicine,” Iranian journal of diabetes and obesity, Dec. 2023, doi: 10.18502/ijdo.v15i4.14556.

K. H. Lee et al., “Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study,” NPJ Digit Med, vol. 7, no. 1, p. 2, Jan. 2024, doi: 10.1038/s41746-023-00976-8.

T. A. Addissouky et al., “Shaping the Future of Cardiac Wellness: Exploring Revolutionary Approaches in Disease Management and Prevention,” Journal of Clinical Cardiology, vol. 5, no. 1, pp. 6–29, Jan. 2024, doi: 10.33696/cardiology.5.048.

Y. Liu et al., “MLIF Modulates Microglia Polarization in Ischemic Stroke by Targeting eEF1A1,” Front Pharmacol, vol. 12, Sep. 2021, doi: 10.3389/fphar.2021.725268.

T. A. Addissouky, M. M. A. Ali, I. El Tantawy El Sayed, and Y. Wang, “Recent Advances in Diagnosing and Treating Helicobacter pylori through Botanical Extracts and Advanced Technologies,” Arch Pharmacol Ther, vol. 5, no. 1, pp. 53–66, Nov. 2023, doi: 10.33696/Pharmacol.4.045.

T. A. Addissouky, F. Attia, K. Megahed, A. E. Elagroudy, I. El Tantawy, and E. Sayed, “Efficiency of Mixture of Olives Oil and Figs as an Antiviral Agent: a Review and Perspective,” International Journal of Medical Science and Health Research, vol. 4, no. 04, pp. 107–111, 2020, [Online]. Available:

T. A. Addissouky, A. A. Khalil, and A. E. El Agroudy, “Assessing the Efficacy of a Modified Triple Drug Regimen Supplemented with Mastic Gum in the Eradication of Helicobacter pylori Infection,” Am J Clin Pathol, vol. 160, no. Supplement_1, pp. S19–S19, Nov. 2023, doi: 10.1093/ajcp/aqad150.041.

L. Wei et al., “Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration,” Br J Radiol, vol. 96, no. 1150, Oct. 2023, doi: 10.1259/bjr.20230211.

M. Ducreux et al., “The management of hepatocellular carcinoma. Current expert opinion and recommendations derived from the 24th ESMO/World Congress on Gastrointestinal Cancer, Barcelona, 2022,” ESMO Open, vol. 8, no. 3, p. 101567, Jun. 2023, doi: 10.1016/j.esmoop.2023.101567.

T. A. Addissouky et al., “Molecular Pathways in Sepsis Pathogenesis: Recent Advances and Therapeutic Avenues,” J Cell Immunol, vol. 5, no. 6, pp. 174–183, 2023, doi: 10.33696/immunology.5.183.

T. A. Addissouky, Y. Wang, I. E. T. El Sayed, A. El Baz, M. M. A. Ali, and A. A. Khalil, “Recent trends in Helicobacter pylori management: harnessing the power of AI and other advanced approaches,” Beni Suef Univ J Basic Appl Sci, vol. 12, no. 1, p. 80, Sep. 2023, doi: 10.1186/s43088-023-00417-1.

H. Sun, H. Yang, and Y. Mao, “Personalized treatment for hepatocellular carcinoma in the era of targeted medicine and bioengineering,” Front Pharmacol, vol. 14, May 2023, doi: 10.3389/fphar.2023.1150151.

T. A. Addissouky, M. M. A. Ali, I. E. T. El Sayed, and Y. Wang, “Emerging advanced approaches for diagnosis and inhibition of liver fibrogenesis,” Egypt J Intern Med, vol. 36, no. 1, p. 19, Feb. 2024, doi: 10.1186/s43162-024-00283-y.

T. A. Addissouky, “Emerging Technologies and Advanced Biomarkers for Enhanced Toxicity Prediction and Safety Pharmacology,” Advances in Clinical Toxicology, vol. 9, no. 1, pp. 1–9, 2024, doi: 10.23880/act-16000293.

T. A. Addissouky, “Translational Insights into Molecular Mechanisms of Chemical Hepatocarcinogenesis for Improved Human Risk Assessment,” Advances in Clinical Toxicology, vol. 9, no. 1, pp. 1–8, 2024, doi: 10.23880/act-16000294.

T. A. Addissouky, “Transforming Toxicity Assessment through Microphysiology, Bioprinting, and Computational Modeling,” Advances in Clinical Toxicology, vol. 9, no. 1, pp. 1–14, 2024, doi: 10.23880/act-16000295.

I. El Sayed, T. A. Addissouky, I. El Tantawy El Sayed, M. M. A Ali, and C. Author, “Regenerating Damaged Joints: The Promise of Tissue Engineering and Nanomedicine in Lupus Arthritis,” J Clinical Orthopaedics and Trauma Care, vol. 6, no. 2, 2024, doi: 10.31579/2694-0248/083.

I. El Sayed, T. A. Addissouky, I. El Tantawy El Sayed, M. M. A Ali, and C. Author, “Conservative and Emerging Rehabilitative Approaches for Knee Osteoarthritis Management,” J Clinical Orthopaedics and Trauma Care, vol. 6, no. 2, 2024, doi: 10.31579/2694-0248/082.

A. Vigdorovits, M. M. Köteles, G.-E. Olteanu, and O. Pop, “Breaking Barriers: AI’s Influence on Pathology and Oncology in Resource-Scarce Medical Systems,” Cancers (Basel), vol. 15, no. 23, p. 5692, Dec. 2023, doi: 10.3390/cancers15235692.

M. Bhat, M. Rabindranath, B. S. Chara, and D. A. Simonetto, “Artificial intelligence, machine learning, and deep learning in liver transplantation,” J Hepatol, vol. 78, no. 6, pp. 1216–1233, Jun. 2023, doi: 10.1016/j.jhep.2023.01.006.

A. Pellat, M. Barat, R. Coriat, P. Soyer, and A. Dohan, “Artificial intelligence: A review of current applications in hepatocellular carcinoma imaging,” Diagn Interv Imaging, vol. 104, no. 1, pp. 24–36, Jan. 2023, doi: 10.1016/j.diii.2022.10.001.

Y. Shen, J. Huang, L. Jia, C. Zhang, and J. Xu, “Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma,” Biochem Biophys Rep, vol. 37, p. 101587, Mar. 2024, doi: 10.1016/j.bbrep.2023.101587.

B. Lai, J. Fu, Q. Zhang, N. Deng, Q. Jiang, and J. Peng, “Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine,” Int J Oncol, vol. 63, no. 3, p. 107, Aug. 2023, doi: 10.3892/ijo.2023.5555.

F. Li, B. Wang, H. Li, L. Kong, and B. Zhu, “G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma,” BMC Cancer, vol. 24, no. 1, p. 157, Jan. 2024, doi: 10.1186/s12885-024-11887-6.

M. Chen et al., “Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI,” Insights Imaging, vol. 14, no. 1, p. 38, Feb. 2023, doi: 10.1186/s13244-023-01380-2.

L. Mou, Z. Pu, Y. Luo, R. Quan, Y. So, and H. Jiang, “Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis,” Front Immunol, vol. 14, Mar. 2023, doi: 10.3389/fimmu.2023.1036562.

J. Varghese and J. Chapiro, “ChatGPT: The transformative influence of generative AI on science and healthcare,” J Hepatol, Aug. 2023, doi: 10.1016/j.jhep.2023.07.028.

Z. Chen, P. Peng, M. Wang, X. Deng, and R. Chen, “Bioinformatics-based and multiscale convolutional neural network screening of herbal medicines for improving the prognosis of liver cancer: a novel approach,” Front Med (Lausanne), vol. 10, Aug. 2023, doi: 10.3389/fmed.2023.1218496.

M. Drezga-Kleiminger, J. Demaree-Cotton, J. Koplin, J. Savulescu, and D. Wilkinson, “Should AI allocate livers for transplant? Public attitudes and ethical considerations,” BMC Med Ethics, vol. 24, no. 1, p. 102, Nov. 2023, doi: 10.1186/s12910-023-00983-0.

K. Radiya, H. L. Joakimsen, K. Ø. Mikalsen, E. K. Aahlin, R.-O. Lindsetmo, and K. E. Mortensen, “Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review,” Eur Radiol, vol. 33, no. 10, pp. 6689–6717, May 2023, doi: 10.1007/s00330-023-09609-w.

M. Kohli, L. M. Prevedello, R. W. Filice, and J. R. Geis, “Implementing Machine Learning in Radiology Practice and Research,” American Journal of Roentgenology, vol. 208, no. 4, pp. 754–760, Apr. 2017, doi: 10.2214/AJR.16.17224.

Y. Chen, C. Yang, L. Sheng, H. Jiang, and B. Song, “The Era of Immunotherapy in Hepatocellular Carcinoma: The New Mission and Challenges of Magnetic Resonance Imaging,” Cancers (Basel), vol. 15, no. 19, p. 4677, Sep. 2023, doi: 10.3390/cancers15194677.

T. Bardol, G.-P. Pageaux, E. Assenat, and C. Alix-Panabières, “Circulating Tumor DNA Clinical Applications in Hepatocellular Carcinoma: Current Trends and Future Perspectives,” Clin Chem, vol. 70, no. 1, pp. 33–48, Jan. 2024, doi: 10.1093/clinchem/hvad168.

K. Lu et al., “HMGB2 upregulation promotes the progression of hepatocellular carcinoma cells through the activation of ZEB1/vimentin axis,” J Gastrointest Oncol, vol. 14, no. 05, pp. 2178–2191, Oct. 2023, doi: 10.21037/jgo-23-447.



2024-04-23 — Updated on 2024-04-29





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