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Research advances in radiomics in the biological behavior of hepatocellular carcinoma
LUO Xi  PENG Shouyong  HUANG Xiaoya  WEN Canping  YAO Hongyan  WU Zichen  WANG Jun 

Cite this article as: LUO X, PENG S Y, HUANG X Y, et al. Research advances in radiomics in the biological behavior of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(9): 215-222. DOI:10.12015/issn.1674-8034.2025.09.033.


[Abstract] Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the digestive system, characterized by a high mortality rate and poor prognosis. The therapeutic efficacy and prognosis of HCC patients are closely related to the biological behavior of the tumor, which is primarily influenced by histopathological features, microvascular metastatic patterns, and molecular protein expression, among other factors. Traditionally, the prediction of these factors has largely relied on postoperative pathological analysis, making preoperative assessment often difficult to conduct efficiently. With the advancement of radiomics and artificial intelligence technologies, it is now possible to effectively predict factors related to the biological behavior of HCC preoperatively by extracting high-throughput tumor imaging features. Although several reviews have summarized the use of radiomics to assess the biological behaviors of HCC, most of them evaluate only a single factor and lack a systematic, comprehensive synthesis and in-depth analysis. This article reviews the use of radiomics in evaluating histopathological features, microvascular infiltration patterns, and molecular protein expression related to the biological behavior of HCC. It provides an in-depth analysis and summary of the current research status and limitations in these areas, revealing that most studies to date are small-sample, single-center, single-modal, retrospective studies and lack standardized guidelines and consensus. Future research should focus on large-sample, prospective, multi-modal, multi-center studies, deeply optimizing radiomics algorithms and integrating insights from other disciplines such as biology, pathology, and genomics to uncover richer and deeper information. The aim is to provide effective guidance for imaging and clinical practitioners to accurately assess HCC patients preoperatively and formulate optimal treatment decisions, ultimately helping patients benefit from diagnosis and treatment and improve outcomes.
[Keywords] hepatocellular carcinoma；radiomics；biological behavior；microvascular invasion；vessels encapsulating tumor clusters；molecular protein expression；magnetic resonance imaging；computed tomography

LUO Xi1, 2   PENG Shouyong1, 2   HUANG Xiaoya1, 2   WEN Canping1, 2   YAO Hongyan1   WU Zichen1   WANG Jun1, 2*  

1 The First Clinical Medical School of Gannan Medical University, Ganzhou 341000, China

2 Department of Medical Imaging, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China

Corresponding author: WANG J, E-mail: Wangguoshou911@126.com

Conflicts of interest   None.

Received  2025-06-03
Accepted  2025-08-25
DOI: 10.12015/issn.1674-8034.2025.09.033
Cite this article as: LUO X, PENG S Y, HUANG X Y, et al. Research advances in radiomics in the biological behavior of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(9): 215-222. DOI:10.12015/issn.1674-8034.2025.09.033.

[1]
SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
Department of Medical Administration, National Health Commission of the People's Republic of China. Guideline for diagnosis and treatment of primary liver cancer (2024 edition)[J]. Chin J Magn Reson Imaging, 2024, 15(6): 1-18. DOI: 10.12015/issn.1674-8034.2024.06.001.
[3]
BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[4]
HAN B F, ZHENG R S, ZENG H M, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024, 4(1): 47-53. DOI: 10.1016/j.jncc.2024.01.006.
[5]
NEVOLA R, RUOCCO R, CRISCUOLO L, et al. Predictors of early and late hepatocellular carcinoma recurrence[J]. World J Gastroenterol, 2023, 29(8): 1243-1260. DOI: 10.3748/wjg.v29.i8.1243.
[6]
HWANG S Y, DANPANICHKUL P, AGOPIAN V, et al. Hepatocellular carcinoma: updates on epidemiology, surveillance, diagnosis and treatment[J]. Clin Mol Hepatol, 2025, 31(Suppl): S228-S254. DOI: 10.3350/cmh.2024.0824.
[7]
ZHANG Y F, CHEN J J, YANG C, et al. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging-based habitat imaging[J]. Eur Radiol, 2024, 34(5): 3215-3225. DOI: 10.1007/s00330-023-10339-2.
[8]
QU Q, LIU Z X, LU M T, et al. Preoperative gadoxetic acid-enhanced MRI features for evaluation of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma: creating nomograms for risk assessment[J]. J Magn Reson Imaging, 2024, 60(3): 1094-1110. DOI: 10.1002/jmri.29187.
[9]
LIU G X, SHEN Z H, CHONG H H, et al. Three-dimensional multifrequency MR elastography for microvascular invasion and prognosis assessment in hepatocellular carcinoma[J]. J Magn Reson Imaging, 2024, 60(6): 2626-2640. DOI: 10.1002/jmri.29276.
[10]
ZHOU H Y, CHENG J M, CHEN T W, et al. A systematic review and meta-analysis of MRI radiomics for predicting microvascular invasion in patients with hepatocellular carcinoma[J/OL]. Curr Med Imaging, 2024, 20: e15734056256824 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/38389371/. DOI: 10.2174/0115734056256824231204073534.
[11]
HAN X, SHAN L F, XU R, et al. Assessing MRI-based artificial intelligence models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis[J/OL]. Acad Radiol, 2025: S1076-6332(25)00608-7 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/40670226/. DOI: 10.1016/j.acra.2025.06.030.
[12]
LEE S W, JEONG S Y, KIM S J. Diagnostic performance of FDG PET/CT radiomics in predicting microvascular invasion in hepatocellular carcinoma compared to conventional metabolic parameters: a systematic review and meta-analysis[J/OL]. Ann Nucl Med, 2025[2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/40581726/ DOI: 10.1007/s12149-025-02075-y.
[13]
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[J]. Liver Cancer, 2021, 10(5): 461-472. DOI: 10.1159/000517992.
[14]
MAO B, ZHANG L Z, NING P G, et al. Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics[J]. Eur Radiol, 2020, 30(12): 6924-6932. DOI: 10.1007/s00330-020-07056-5.
[15]
WU C Y, DU X Y, ZHANG Y, et al. Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma[J]. J Cancer Res Clin Oncol, 2023, 149(16): 15103-15112. DOI: 10.1007/s00432-023-05327-4.
[16]
HAN Y E, CHO Y, KIM M J, et al. Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study[J]. Abdom Radiol (NY), 2023, 48(1): 244-256. DOI: 10.1007/s00261-022-03679-y.
[17]
YANG Y, ZHANG S, CUI C, et al. Multiphase MRI-based radiomics for predicting histological grade of hepatocellular carcinoma[J]. J Magn Reson Imaging, 2024, 60(5): 2117-2127. DOI: 10.1002/jmri.29289.
[18]
LIU H F, WANG M, WANG Q, et al. Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma[J/OL]. Insights Imaging, 2024, 15(1): 97 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/38536542/. DOI: 10.1186/s13244-024-01623-w.
[19]
MAO Y F, WANG J C, ZHU Y, et al. Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma[J]. Hepatobiliary Surg Nutr, 2022, 11(1): 13-24. DOI: 10.21037/hbsn-19-870.
[20]
ZHANG Y, HE D, LIU J, et al. Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics[J]. World J Gastroenterol, 2023, 29(13): 2001-2014. DOI: 10.3748/wjg.v29.i13.2001.
[21]
FENG Z, LI H, LIU Q, et al. CT radiomics to predict macrotrabecular-massive subtype and immune status in hepatocellular carcinoma[J/OL]. Radiology, 2023, 307(1): e221291 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/36511807/. DOI: 10.1148/radiol.221291.
[22]
LI M, FAN Y, YOU H, et al. Dual-energy CT deep learning radiomics to predict macrotrabecular-massive hepatocellular carcinoma[J/OL]. Radiology, 2023, 308(2): e230255 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/37606573/. DOI: 10.1148/radiol.230255.
[23]
KANG H J, KIM H, LEE D H, et al. Gadoxetate-enhanced MRI features of proliferative hepatocellular carcinoma are prognostic after surgery[J]. Radiology, 2021, 300(3): 572-582. DOI: 10.1148/radiol.2021204352.
[24]
WANG G, DING F, CHEN K, et al. CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment[J/OL]. J Transl Med, 2024, 22(1): 683 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/39218938/. DOI: 10.1186/s12967-024-05393-3.
[25]
YAN Z Y, LIU Z X, ZHU G D, et al. Gadoxetic acid-enhanced MRI-based radiomic models for preoperative risk prediction and prognostic assessment of proliferative hepatocellular carcinoma[J]. Acad Radiol, 2025, 32(1): 157-169. DOI: 10.1016/j.acra.2024.07.040.
[26]
QU H, ZHANG S R, LI X D, et al. A deep learning model based on self-supervised learning for identifying subtypes of proliferative hepatocellular carcinoma from dynamic contrast-enhanced MRI[J/OL]. Insights Imaging, 2025, 16(1): 89 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/40244356/. DOI: 10.1186/s13244-025-01968-w.
[27]
WANG L, XU H X, WANG R, et al. Advances in multi-omics studies of microvascular invasion in hepatocellular carcinoma[J/OL]. Eur J Med Res, 2025, 30(1): 165 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/40075448/. DOI: 10.1186/s40001-025-02421-w.
[28]
LI K, ZHANG R, WEN F K, et al. Single-cell dissection of the multicellular ecosystem and molecular features underlying microvascular invasion in HCC[J]. Hepatology, 2024, 79(6): 1293-1309. DOI: 10.1097/HEP.0000000000000673.
[29]
TANG M L, ZHANG S X, YANG M, et al. Infiltrative vessel co-optive growth pattern induced by IQGAP3 overexpression promotes microvascular invasion in hepatocellular carcinoma[J]. Clin Cancer Res, 2024, 30(10): 2206-2224. DOI: 10.1158/1078-0432.CCR-23-2933.
[30]
ZHANG Z H, JIANG C, QIANG Z Y, et al. Role of microvascular invasion in early recurrence of hepatocellular carcinoma after liver resection: a literature review[J]. Asian J Surg, 2024, 47(5): 2138-2143. DOI: 10.1016/j.asjsur.2024.02.115.
[31]
HWANG Y J, BAE J S, LEE Y, et al. Classification of microvascular invasion of hepatocellular carcinoma: correlation with prognosis and magnetic resonance imaging[J]. Clin Mol Hepatol, 2023, 29(3): 733-746. DOI: 10.3350/cmh.2023.0034.
[32]
ZHOU H Y, CHENG J M, CHEN T W, et al. CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis[J/OL]. Clinics, 2023, 78: 100264 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/37562218/. DOI: 10.1016/j.clinsp.2023.100264.
[33]
ZHAO H F, FENG Z C, LI H L, et al. Influence of different region of interest sizes on CT-based radiomics model for microvascular invasion prediction in hepatocellular carcinoma[J]. Journal of Central South University (Medical Science), 2022, 47(8): 1049-1057. DOI: 10.11817/j.issn.1672-7347.2022.220027.
[34]
WANG F, ZHAN G, CHEN Q Q, et al. Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images[J]. Liver Int, 2024, 44(6): 1351-1362. DOI: 10.1111/liv.15870.
[35]
YOU H, WANG J, MA R, et al. Clinical interpretability of deep learning for predicting microvascular invasion in hepatocellular carcinoma by using attention mechanism[J/OL]. Bioengineering (Basel), 2023, 10(8): 948 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/37627833/. DOI: 10.3390/bioengineering10080948.
[36]
LI J F, SONG W X, LI J X, et al. A clinical study exploring the prediction of microvascular invasion in hepatocellular carcinoma through the use of combined enhanced CT and MRI radiomics[J/OL]. PLoS One, 2025, 20(1): e0318232 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/39874347/. DOI: 10.1371/journal.pone.0318232.
[37]
ZWANENBURG A, VALLIÈRES M, ABDALAH M A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2): 328-338. DOI: 10.1148/radiol.2020191145.
[38]
WANG M M, CAO L, WANG Y Z, et al. The prognostic value of vessels encapsulating tumor clusters (VETC) in patients with hepatocellular carcinoma: a systematic review and meta-analysis[J]. Clin Transl Oncol, 2024, 26(8): 2037-2046. DOI: 10.1007/s12094-024-03427-2.
[39]
LIU K, DENNIS C, PRINCE D S, et al. Vessels that encapsulate tumour clusters vascular pattern in hepatocellular carcinoma[J/OL]. JHEP Rep, 2023, 5(8): 100792 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/37456680/. DOI: 10.1016/j.jhepr.2023.100792.
[40]
GU M T, ZOU W J, CHEN H L, et al. Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study[J/OL]. Cancer Imaging, 2025, 25(1): 87 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/40624579/. DOI: 10.1186/s40644-025-00895-9.
[41]
YU Y X, FAN Y F, WANG X M, et al. Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma[J]. Eur Radiol, 2022, 32(2): 959-970. DOI: 10.1007/s00330-021-08250-9.
[42]
CAI C Y, WANG L C, TAO L Y, et al. Imaging-based prediction of ki-67 expression in hepatocellular carcinoma: a retrospective study[J/OL]. Cancer Med, 2025, 14(4): e70562 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/39964132/. DOI: 10.1002/cam4.70562.
[43]
ZHU Z W, WU J, GUO Y, et al. Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features[J/OL]. World J Gastrointest Oncol, 2025, 17(5): 104172 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/40487953/. DOI: 10.4251/wjgo.v17.i5.104172.
[44]
YANG Y, XIAO S L, WANG Z M, et al. Radiomic analysis based on Gd-EOB-DTPA enhanced MRI for the preoperative prediction of ki-67 expression in hepatocellular carcinoma[J]. Acad Radiol, 2024, 31(3): 859-869. DOI: 10.1016/j.acra.2023.07.019.
[45]
ZHANG L, ZHOU H, ZHANG X, et al. A radiomics nomogram for predicting cytokeratin 19-positive hepatocellular carcinoma: a two-center study[J/OL]. Front Oncol, 2023, 13: 1174069 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/37182122/. DOI: 10.3389/fonc.2023.1174069.
[46]
CHONG H H, GONG Y D, ZHANG Y F, et al. Radiomics on gadoxetate disodium-enhanced MRI: non-invasively identifying glypican 3-positive hepatocellular carcinoma and postoperative recurrence[J]. Acad Radiol, 2023, 30(1): 49-63. DOI: 10.1016/j.acra.2022.04.006.
[47]
ASCARI S, CHEN R S, VIVALDI C, et al. Advancements in immunotherapy for hepatocellular carcinoma[J]. Expert Rev Anticancer Ther, 2025, 25(2): 151-165. DOI: 10.1080/14737140.2025.2461631.
[48]
BLOOM M, PODDER S, DANG H, et al. Advances in immunotherapy in hepatocellular carcinoma[J/OL]. Int J Mol Sci, 2025, 26(5): 1936 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/40076561/. DOI: 10.3390/ijms26051936.
[49]
GONG X Q, LIU N, TAO Y Y, et al. Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma[J/OL]. Sci Rep, 2023, 13: 7710 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/37173350/. DOI: 10.1038/s41598-023-34763-y.
[50]
XIE T S, WEI Y, XU L F, et al. Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma[J/OL]. Front Oncol, 2023, 13: 1103521 [2025-05-25]. https://pubmed.ncbi.nlm.nih.gov/36937385/. DOI: 10.3389/fonc.2023.1103521.

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