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REVIEW ARTICLE

The application and challenges of artificial intelligence in imaging-based precision treatment of hepatocellular carcinoma

Haijun He1 ,  Mei Zhang2 ,  Rengui Chen1 ,  Tong Chen1 ,  Jing Zheng1 ,  Mingquan Luo1 ,  Yang Jing3*
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1 Department of Radiology, People’s Hospital of Nanbu County, Nanchong City, Sichuan, China
2 School Office, Chengdu Open University, Chengdu, Sichuan, China
3 Huiying Medical Technology Co., Ltd., Beijing, China
EJMO, 025180163 https://doi.org/10.36922/EJMO025180163
Received: 30 April 2025 | Revised: 13 June 2025 | Accepted: 4 July 2025 | Published online: 5 August 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related deaths worldwide. Early diagnosis and effective treatment of HCC remain major global health challenges and place a significant burden on healthcare systems. In recent years, significant advancements in radiomics and deep learning (DL) technologies have been made in the field of medical imaging, offering new possibilities for the diagnosis, treatment, and prognostic assessment of HCC. This review summarizes the current applications of radiomics and DL in precision medicine for HCC, with a focus on their roles in diagnosis, pathological grading, prediction of microvascular invasion, post-operative recurrence, and treatment efficacy assessment. By analyzing existing studies, this paper demonstrates both the potential and current limitations of these technologies in HCC management and outlines directions for future development.

Keywords
Radiomics
Deep learning
Hepatocellular carcinoma
Precision medicine
Prognosis
Applications and challenges
Funding
The study was supported by the Nanchong Bureau of Science and Technology (24YFZJZC0061).
Conflict of interest
The authors declare no competing interests.
References
  1. Philips CA, Rajesh S, Nair DC, Ahamed R, Abduljaleel JK, Augustine P. Hepatocellular carcinoma in 2021: An exhaustive update. Cureus. 2021;13(11):e19274. doi: 10.7759/cureus.19274

 

  1. Brown ZJ, Tsilimigras DI, Ruff SM, et al. Management of hepatocellular carcinoma: A review. JAMA Surg. 2023;158(4):410-420. doi: 10.1001/jamasurg.2022.7989

 

  1. Wang Z, Qin H, Liu S, Sheng J, Zhang X. Precision diagnosis of hepatocellular carcinoma. Chin Med J (Engl). 2023 May 20;136(10):1155-1165. doi: 10.1097/CM9.0000000000002641

 

  1. Bo Z, Song J, He Q, et al. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med. 2024;173:108337. doi: 10.1016/j.compbiomed.2024.108337

 

  1. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-510. doi: 10.1038/s41568-018-0016-5

 

  1. Xu HL, Gong TT, Song XJ, et al. Artificial intelligence performance in image-based cancer identification: Umbrella review of systematic reviews. J Med Internet Res. 2025;27:e53567. doi: 10.2196/53567

 

  1. Chatzipanagiotou OP, Loukas C, Vailas M, et al. Artificial intelligence in hepatocellular carcinoma diagnosis: A comprehensive review of current literature. J Gastroenterol Hepatol. 2024 Oct;39(10):1994-2005. doi: 10.1111/jgh.16663

 

  1. Hong EK, Ham J, Roh B, et al. Diagnostic accuracy and clinical value of a domain-specific multimodal generative AI model for chest radiograph report generation. Radiology. 2025;314(3):e241476. doi: 10.1148/radiol.241476

 

  1. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: Machine learning and the future of medicine. J Intern Med. 2018;284(6):603-619. doi: 10.1111/joim.12822

 

  1. Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol. 2023;78(6):1216-1233. doi: 10.1016/j.jhep.2023.01.006

 

  1. Mao Y, Xu N, Wu Y, et al. Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images. Cell Rep Med. 2025;6(3):101988. doi: 10.1016/j.xcrm.2025.101988

 

  1. Nardone V, Reginelli A, Rubini D, et al. Delta radiomics: An updated systematic review. Radiol Med. 2024;129(8):1197-1214. doi: 10.1007/s11547-024-01853-4

 

  1. Li S, Dai Y, Chen J, Yan F, Yang Y. MRI-based habitat imaging in cancer treatment: Current technology, applications, and challenges. Cancer Imaging. 2024;24(1):107. doi: 10.1186/s40644-024-00758-9

 

  1. Alirr OI, Rahni AA, Golkar E. An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning. Int J Comput Assist Radiol Surg. 2018;13(8):1169-1176. doi: 10.1007/s11548-018-1801-z

 

  1. Zang L, Liang W, Ke H, Chen F, Shen C. Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet. Sci Rep. 2023;13(1):12779. doi: 10.1038/s41598-023-39240-0

 

  1. Chen Y, Zheng C, Hu F, et al. Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field. Comput Biol Med. 2022;150:106076. doi: 10.1016/j.compbiomed.2022.106076

 

  1. Ronot M, Purcell Y, Vilgrain V. Hepatocellular carcinoma: Current imaging modalities for diagnosis and prognosis. Dig Dis Sci. 2019;64(4):934-950. doi: 10.1007/s10620-019-05547-0

 

  1. Navin PJ, Venkatesh SK. Hepatocellular carcinoma: State of the art imaging and Recent advances. J Clin Transl Hepatol. 2019;7(1):72-85. doi: 10.14218/jcth.2018.00032

 

  1. Sangiovanni A, Prati GM, Fasani P, et al. The natural history of compensated cirrhosis due to hepatitis C virus: A 17-year cohort study of 214 patients. Hepatology. 2006;43(6):1303-1310. doi: 10.1002/hep.21176

 

  1. Mokrane FZ, Lu L, Vavasseur A, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol. 2020;30(1):558-570. doi: 10.1007/s00330-019-06347-w

 

  1. Guo L, Hao X, Chen L, et al. Early warning of hepatocellular carcinoma in cirrhotic patients by three-phase CT-based deep learning radiomics model: A retrospective, multicentre, cohort study. EClinicalMedicine. 2024;74:102718. doi: 10.1016/j.eclinm.2024.102718

 

  1. Fischer MA, Raptis DA, Donati OF, et al. MR imaging features for improved diagnosis of hepatocellular carcinoma in the non-cirrhotic liver: Multi-center evaluation. Eur J Radiol. 2015;84(10):1879-1887. doi: 10.1016/j.ejrad.2015.06.029

 

  1. Gawrieh S, Dakhoul L, Miller E, et al. Characteristics, aetiologies and trends of hepatocellular carcinoma in patients without cirrhosis: A United States multicentre study. Aliment Pharmacol Ther. 2019;50(7):809-821. doi: 10.1111/apt.15464

 

  1. Nie P, Wang N, Pang J, et al. CT-based radiomics nomogram: A potential tool for differentiating hepatocellular adenoma from hepatocellular carcinoma in the Noncirrhotic Liver. Acad Radiol. 2021;28(6):799-807. doi: 10.1016/j.acra.2020.04.027

 

  1. Ding Z, Lin K, Fu J, et al. An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver. World J Surg Oncol. 2021;19(1):181. doi: 10.1186/s12957-021-02266-7

 

  1. Zhao X, Zhou Y, Zhang Y, et al. Radiomics based on contrast-enhanced MRI in Differentiation between fat-poor angiomyolipoma and hepatocellular carcinoma in noncirrhotic liver: A multicenter analysis. Front Oncol. 2021;11:744756. doi: 10.3389/fonc.2021.744756

 

  1. Hamm CA, Wang CJ, Savic LJ, et al. Deep learning for liver tumor diagnosis part I: Development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol. 2019;29(7):3338-3347. doi: 10.1007/s00330-019-06205-9

 

  1. Fiz F, Rossi N, Langella S, et al. Radiomic analysis of intrahepatic cholangiocarcinoma: Non-invasive prediction of pathology data: A multicenter study to develop a clinical-radiomic model. Cancers (Basel). 2023;15(17):4204.doi: 10.3390/cancers15174204

 

  1. Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, staging and management of hepatocellular carcinoma: 2018 practice guidance by the American association for the study of liver diseases. Hepatology. 2018;68(2):723-750. doi: 10.1002/hep.29913

 

  1. Lewis S, Peti S, Hectors SJ, et al. Volumetric quantitative histogram analysis using diffusion-weighted magnetic resonance imaging to differentiate HCC from other primary liver cancers. Abdom Radiol (NY). 2019;44(3):912-922. doi: 10.1007/s00261-019-01906-7

 

  1. Wang X, Wang S, Yin X, Zheng Y. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med. 2022;141:105058. doi: 10.1016/j.compbiomed.2021.105058

 

  1. Liu X, Khalvati F, Namdar K, et al. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol. 2021;31(1):244-255. doi: 10.1007/s00330-020-07119-7

 

  1. Shinkawa H, Tanaka S, Kabata D, et al. The prognostic impact of tumor differentiation on recurrence and survival after resection of hepatocellular carcinoma is dependent on tumor size. Liver Cancer. 2021;10(5):461-472. doi: 10.1159/000517992

 

  1. Martins-Filho SN, Paiva C, Azevedo RS, Alves VA. Histological grading of hepatocellular carcinoma-a systematic review of literature. Front Med (Lausanne). 2017;4:193. doi: 10.3389/fmed.2017.00193

 

  1. Oishi K, Itamoto T, Amano H, et al. Clinicopathologic features of poorly differentiated hepatocellular carcinoma. J Surg Oncol. 2007;95(4):311-316. doi: 10.1002/jso.20661

 

  1. Okusaka T, Okada S, Ueno H, et al. Satellite lesions in patients with small hepatocellular carcinoma with reference to clinicopathologic features. Cancer. 2002;95(9):1931-1937. doi: 10.1002/cncr.10892

 

  1. Hu X, Li C, Wang Q, et al. Development and external validation of a radiomics model derived from preoperative gadoxetic acid-enhanced MRI for predicting histopathologic grade of hepatocellular carcinoma. Diagnostics (Basel). 2023;13(3):413. doi: 10.3390/diagnostics13030413

 

  1. Mao Y, Wang J, Zhu Y, et al. Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma. Hepatobiliary Surg Nutr. 2022;11(1):13-24. doi: 10.21037/hbsn-19-870

 

  1. Brancato V, Garbino N, Salvatore M, Cavaliere C. MRI-based radiomic features help identify lesions and predict histopathological grade of hepatocellular carcinoma. Diagnostics (Basel). 2022;12(5):1085. doi: 10.3390/diagnostics12051085

 

  1. Liu HF, Wang M, Wang Q, et al. Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma. Insights Imaging. 2024;15(1):97. doi: 10.1186/s13244-024-01623-w

 

  1. He T, Zou TT, Sun L, Lei AK, Xu L. Research progress of microvascular invasion in hepatocellular carcinoma. Zhonghua Gan Zang Bing Za Zhi. 2022;30(8):899-904. doi: 10.3760/cma.j.cn501113-20210428-00211

 

  1. Wang W, Guo Y, Zhong J, et al. The clinical significance of microvascular invasion in the surgical planning and postoperative sequential treatment in hepatocellular carcinoma. Sci Rep. 2021;11(1):2415. doi: 10.1038/s41598-021-82058-x

 

  1. Hu HT, Wang Z, Huang XW, et al. Ultrasound-based radiomics score: A potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol. 2019;29(6):2890-2901. doi: 10.1007/s00330-018-5797-0

 

  1. Xu X, Zhang HL, Liu QP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019;70(6):1133-1144. doi: 10.1016/j.jhep.2019.02.023

 

  1. Meng A, Zhuang Y, Huang Q, Tang L, Yang J, Gong P. Development and validation of a cross-modality tensor fusion model using multi-modality MRI radiomics features and clinical radiological characteristics for the prediction of microvascular invasion in hepatocellular carcinoma. Eur J Surg Oncol. 2025;51(1):109364. doi: 10.1016/j.ejso.2024.109364

 

  1. Song D, Wang Y, Wang W, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol. 2021;147(12):3757-3767. doi: 10.1007/s00432-021-03617-3

 

  1. Xia TY, Zhou ZH, Meng XP, et al. Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model. Radiology. 2023;307(4):e222729. doi: 10.1148/radiol.222729

 

  1. Dong Y, Zuo D, Qiu YJ, et al. Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomics features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study. Clin Hemorheol Microcirc. 2022;81(1):97-107. doi: 10.3233/CH-211363

 

  1. Jiang YQ, Cao SE, Cao S, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning. J Cancer Res Clin Oncol. 2021;147(3):821-833. doi: 10.1007/s00432-020-03366-9

 

  1. Feng ST, Jia Y, Liao B, et al. Preoperative prediction of microvascular invasion in hepatocellular cancer: A radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol. 2019;29(9):4648-4659. doi: 10.1007/s00330-018-5935-8

 

  1. Xu T, Ren L, Liao M, et al. Preoperative radiomics analysis of contrast-enhanced CT for microvascular invasion and prognosis stratification in hepatocellular carcinoma. J Hepatocell Carcinoma. 2022;9:189-201. doi: 10.2147/jhc.s356573

 

  1. Orner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet. 2018;391(10127):1301-1314. doi: 10.1016/S0140-6736(18)30010-2

 

  1. Li W, Shen H, Han L, et al. A multiparametric fusion radiomics signature based on contrast-enhanced MRI for predicting early recurrence of hepatocellular carcinoma. J Oncol. 2022;2022:3704987. doi: 10.1155/2022/3704987

 

  1. Saalfeld S, Kreher R, Hille G, et al. Prognostic role of radiomics-based body composition analysis for the 1-year survival f or hepatocellular carcinoma patients [published online ahead of print, 2023 Aug 17]. J Cachexia Sarcopenia Muscle. 2023;142301-2309. doi: 10.1002/jcsm.13315

 

  1. Ibrahim S, Roychowdhury A, Hean TK. Risk factors for intrahepatic recurrence after hepatectomy for hepatocellular carcinoma. Am J Surg. 2007;194(1):17-22. doi: 10.1016/j.amjsurg.2006.06.051

 

  1. Kamiyama T, Nakanishi K, Yokoo H, et al. Analysis of the risk factors for early death due to disease recurrence or progression within 1 year after hepatectomy in patients with hepatocellular carcinoma. World J Surg Oncol. 2012;10:107. doi: 10.1186/1477-7819-10-107

 

  1. Cao K, Wang X, Xu C, et al. Ultrasound-based radiomics analysis for assessing risk factors associated with early recurrence following surgical resection of hepatocellular carcinoma. Ultrasound Med Biol. 2024;50(12):1964-1972. doi: 10.1016/j.ultrasmedbio.2024.09.002

 

  1. Qian GX, Xu ZL, Li YH, et al. Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis. World J Gastroenterol. 2024;30(15):2128-2142. doi: 10.3748/wjg.v30.i15.2128

 

  1. Xu Y, Zhao J, Chen Q, et al. Contrastive learning for preoperative early recurrence prediction of hepatocellular carcinoma with liver CT image and tumor mask. Annu Int Conf IEEE Eng Med Biol Soc. 2023;2023:1-4. doi: 10.1109/embc40787.2023.10340893

 

  1. Wang F, Cheng M, Du B, Li LM, Huang WP, Gao JB. Use of radiomics containing an effective peritumoral area to predict early recurrence of solitary hepatocellular carcinoma ≤5 cm in diameter. Front Oncol. 2022;12:1032115. doi: 10.3389/fonc.2022.1032115

 

  1. Gao W, Wang W, Song D, et al. A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma. Radiol Med. 2022;127(3):259-271. doi: 10.1007/s11547-021-01445-6

 

  1. Wu C, Yu S, Zhang Y, Zhu L, Chen S, Liu Y. CT-based radiomics nomogram Improves risk stratification and prediction of early recurrence in hepatocellular carcinoma after partial hepatectomy. Front Oncol. 2022;12:896002. doi: 10.3389/fonc.2022.896002

 

  1. European Association for the Study of the Liver. Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol. 2018;69(1):182-236. doi: 10.1016/j.jhep.2018.03.019

 

  1. Xu Z, An C, Shi F, et al. Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram [published online ahead of print, 2023 Jul 27]. Eur Radiol. 2023;33:9038-9051. doi: 10.1007/s00330-023-09953-x

 

  1. Kumari R, Sahu MK, Tripathy A, Uthansingh K, Behera M. Hepatocellular carcinoma treatment: Hurdles, advances and prospects. Hepat Oncol. 2018;5(2):HEP08. doi: 10.2217/hep-2018-0002

 

  1. Fan XL, Wang YH, Chen YH, et al. Computed tomography texture analysis combined with preoperative clinical factors serve as a predictor of early efficacy of transcatheter arterial chemoembolization in hepatocellular carcinoma. Abdom Radiol (NY). 2023;48(6):2008-2018. doi: 10.1007/s00261-023-03868-3

 

  1. Zhang X, He Z, Zhang Y, Kong J. Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model. Front Pharmacol. 2024;15:1315732. doi: 10.3389/fphar.2024.1315732

 

  1. Luo J, Huang Z, Wang M, Li T, Huang J. Prognostic role of multiparameter MRI and radiomics in progression of advanced unresectable hepatocellular carcinoma following combined transcatheter arterial chemoembolization and lenvatinib therapy. BMC Gastroenterol. 2022;22(1):108. doi: 10.1186/s12876-022-02129-9

 

  1. Zhao Y, Zhang J, Wang N, et al. Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma. BMC Cancer. 2023;23(1):1026. doi: 10.1186/s12885-023-11491-0

 

  1. Kuang Y, Li R, Jia P, et al. MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm. Abdom Radiol (NY). 2021;46(8):3772-3789. doi: 10.1007/s00261-021-02992-2

 

  1. Wang L, Wu M, Zhu C, et al. Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery. Front Oncol. 2022;12:1019009. doi: 10.3389/fonc.2022.1019009

 

  1. Wang XH, Long LH, Cui Y, et al. MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma. Br J Cancer. 2020;122(7):978-985. doi: 10.1038/s41416-019-0706-0

 

  1. Zhang C, Ma LD, Zhang XL, et al. Magnetic resonance deep learning radiomic model based on distinct metastatic vascular patterns for evaluating recurrence-free survival in hepatocellular carcinoma. J Magn Reson Imaging. 2024;60(1):231-242. doi: 10.1002/jmri.29064

 

  1. Chen Y, Pasquier D, Verstappen D, Woodruff HC, Lambin P. An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy. J Cancer Res Clin Oncol. 2025;151(2):84. doi: 10.1007/s00432-025-06119-8

 

  1. Liu Q, Li J, Liu F, et al. A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy. Cancer Imaging. 2020;20(1):82. doi: 10.1186/s40644-020-00360-9
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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