Share：
Share this content in WeChat
X
[Chinese] [PDF] 46 1
• Reviews •
Research progress on microvascular invasion of hepatocellular carcinoma based on multi-parametric MRI radiomics and extracellular volume
CHANG Xinyue  XIA Jikai  CAO Xinshan 

Cite this article as CHANG X Y, XIA J K, CAO X S. Research progress on microvascular invasion of hepatocellular carcinoma based on multi-parametric MRI radiomics and extracellular volume[J]. Chin J Magn Reson Imaging, 2026, 17(8): 212-218, 234. DOI:10.12015/issn.1674-8034.2026.08.025.


[Abstract] Hepatocellular carcinoma (HCC) is a highly aggressive and recurrent primary liver malignancy, with microvascular invasion (MVI) serving as an independent prognostic factor for patient outcomes. Non-invasive preoperative prediction of MVI using imaging techniques is crucial for developing personalized treatment strategies. In recent years, radiomics based on multi-parametric magnetic resonance imaging (mpMRI) and extracellular volume (ECV) quantification have shown promising applications in characterizing tumor heterogeneity and the tumor microenvironment, respectively. However, existing literature predominantly focuses on individual technological advances, lacking comprehensive summaries of the theoretical foundations and cutting-edge findings regarding their combined use. This article systematically reviews the latest research progress in applying mpMRI-based radiomics and ECV for HCC MVI assessment, emphasizing advancements in mpMRI radiomics, the principles of ECV quantification, its pathological association with MVI, and the theoretical framework for integrating both approaches in non-invasive preoperative evaluation of HCC MVI. It further analyzes key challenges in current studies: including inconsistent label quality, cross-modal registration errors, and limited model interpretability, and proposes corresponding optimization strategies. The aim is to provide actionable theoretical guidance and methodological insights for precise preoperative risk stratification in HCC and future multimodal integration research.
[Keywords] hepatocellular carcinoma；microvascular invasion；extracellular volume；radiomics；magnetic resonance imaging；multi-parametric

CHANG Xinyue1   XIA Jikai2   CAO Xinshan1*  

1 Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou 256603, China

2 Department of Medical Imaging, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264000, China

Corresponding author: CAO X S, E-mail: byfycxs@126.com

Conflicts of interest   None.

Received  2026-02-10
Accepted  2026-07-24
DOI: 10.12015/issn.1674-8034.2026.08.025
Cite this article as CHANG X Y, XIA J K, CAO X S. Research progress on microvascular invasion of hepatocellular carcinoma based on multi-parametric MRI radiomics and extracellular volume[J]. Chin J Magn Reson Imaging, 2026, 17(8): 212-218, 234. DOI:10.12015/issn.1674-8034.2026.08.025.

[1]
LV K, CAO X, DU P, et al. Radiomics for the detection of microvascular invasion in hepatocellular carcinoma[J]. World J Gastroenterol, 2022, 28(20): 2176-2183. DOI: 10.3748/wjg.v28.i20.2176.
[2]
HUANG Z H, HUANG W R, JIANG L, et al. Decision fusion model for predicting microvascular invasion in hepatocellular carcinoma based on multi-MR habitat imaging and machine-learning classifiers[J]. Acad Radiol, 2025, 32(4): 1971-1980. DOI: 10.1016/j.acra.2024.10.007.
[3]
RHEE H, PARK Y N, CHOI J Y. Advances in understanding hepatocellular carcinoma vasculature: implications for diagnosis, prognostication, and treatment[J]. Korean J Radiol, 2024, 25(10): 887-901. DOI: 10.3348/kjr.2024.0307.
[4]
JIANG B, FEI X, FAN X W, et al. A nomogram based on contrast-enhanced ultrasound to predict the microvascular invasion in hepatocellular carcinoma[J]. Ultrasound Med Biol, 2023, 49(7): 1561-1568. DOI: 10.1016/j.ultrasmedbio.2023.02.020.
[5]
ZHENG W X, HUANG G, WANG L L, et al. Research progress of extracellular volume in imaging[J]. J Clin Radiol, 2024, 43(7): 1226-1229.
[6]
ZHANG R, YAO Y T, GAO H W, et al. Mechanisms of angiogenesis in tumour[J/OL]. Front Oncol, 2024, 14: 1359069 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/38590656/. DOI: 10.3389/fonc.2024.1359069.
[7]
REED M S, OCHOA M, TICHAUER K M, et al. Mapping estimates of vascular permeability with a clinical indocyanine green fluorescence imaging system in experimental pancreatic adenocarcinoma tumors[J/OL]. J Biomed Opt, 2023, 28(7): 076001 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/37457627/. DOI: 10.1117/1.JBO.28.7.076001.
[8]
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[9]
KUMAR V, GU Y H, BASU S, et al. Radiomics: the process and the challenges[J]. Magn Reson Imaging, 2012, 30(9): 1234-1248. DOI: 10.1016/j.mri.2012.06.010.
[10]
ZHANG Y P, ZHANG X Y, CHENG Y T, et al. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling[J/OL]. Mil Med Res, 2023, 10(1): 22 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/37189155/. DOI: 10.1186/s40779-023-00458-8.
[11]
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[12]
RENZULLI M, MOTTOLA M, COPPOLA F, et al. Automatically extracted machine learning features from preoperative CT to early predict microvascular invasion in HCC: the role of the zone of transition (ZOT)[J/OL]. Cancers, 2022, 14(7): 1816 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/35406589/. DOI: 10.3390/cancers14071816.
[13]
YANG L H, CHEN M L, CHEN S, et al. The predictive value of MRI radiomics integrated with inflammatory factors for microvascular invasion in hepatocellular carcinoma[J]. Radiol Pract, 2024, 39(5): 585-590. DOI: 10.13609/j.cnki.1000-0313.2024.05.004.
[14]
COOLENS C, DRISCOLL B, FOLTZ W, et al. Comparison of voxel-wise tumor perfusion changes measured with dynamic contrast-enhanced (DCE) MRI and volumetric DCE CT in patients with metastatic brain cancer treated with radiosurgery[J]. Tomography, 2016, 2(4): 325-333. DOI: 10.18383/j.tom.2016.00178.
[15]
YANG J W, DONG X, WANG G L, et al. Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma[J]. Abdom Radiol (NY), 2023, 48(2): 554-566. DOI: 10.1007/s00261-022-03740-w.
[16]
SUN B Y, GU P Y, GUAN R Y, et al. Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma[J/OL]. World J Surg Oncol, 2022, 20(1): 189 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/35676669/. DOI: 10.1186/s12957-022-02645-8.
[17]
SONG D J, WANG Y Y, WANG W T, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters[J]. J Cancer Res Clin Oncol, 2021, 147(12): 3757-3767. DOI: 10.1007/s00432-021-03617-3.
[18]
WANG L L, ZHANG F C, XU H X, et al. Advances in imaging techniques for tumor microenvironment evaluation in hepatocellular carcinoma[J/OL]. World J Gastroenterol, 2025, 31(10): 103454 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/40093677/. DOI: 10.3748/wjg.v31.i10.103454.
[19]
LI X, HUANG W, HOLMES J H. Dynamic contrast-enhanced (DCE) MRI[J]. Magn Reson Imaging Clin N Am, 2024, 32(1): 47-61. DOI: 10.1016/j.mric.2023.09.001.
[20]
WANG Y Z, TIAN X X, TIAN J H, et al. Is gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced magnetic resonance imaging an accurate diagnostic method for hepatocellular carcinoma a systematic review with meta-analysis[J]. Curr Med Imaging, 2022, 18(6): 633-647. DOI: 10.2174/1573405617666210917124404.
[21]
MURAKAMI T, SOFUE K, HORI M. Diagnosis of hepatocellular carcinoma using Gd-EOB-DTPA MR imaging[J]. Magn Reson Med Sci, 2022, 21(1): 168-181. DOI: 10.2463/mrms.rev.2021-0031.
[22]
QU C M, WANG Q, LI C F, et al. A radiomics model based on Gd-EOB-DTPA-enhanced MRI for the prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm[J/OL]. Front Oncol, 2022, 12: 831795 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/35664790/. DOI: 10.3389/fonc.2022.831795.
[23]
XU X S, LUO Y, LIANG S Q, et al. Predicting microvascular invasion in hepatocellular carcinoma using Delta radiomics model based on Gd-EOB-DTPA enhanced MRI[J]. Chin J Magn Reson Imaging, 2025, 16(11): 123-128. DOI: 10.12015/issn.1674-8034.2025.11.018.
[24]
ZENG Y L, WU H Q, ZHU Y Q, et al. MRI-based intra-tumoral ecological diversity features and temporal characteristics for predicting microvascular invasion in hepatocellular carcinoma[J/OL]. Front Oncol, 2025, 15: 1510071 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/40098699/. DOI: 10.3389/fonc.2025.1510071.
[25]
ZHU Z, YU Y X, LU J, et al. Clinical application value of predicting microvascular invasion in hepatocellular carcinoma using intratumoral and peritumoral radiomics models: a multicenter study[J]. Chin J Magn Reson Imaging, 2024, 15(8): 132-138. DOI: 10.12015/issn.1674-8034.2024.08.020.
[26]
TIAN L, LIU S B, ZHOU H, et al. DWI-derived sequences: application in the evaluation of liver fibrosis[J/OL]. Curr Med Imaging, 2024, 20: e15734056326012 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/39838678/. DOI: 10.2174/0115734056326012241031074233.
[27]
VIJITHANANDA S M, JAYATILAKE M L, HEWAVITHANA B, et al. Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques[J/OL]. Biomed Eng Online, 2022, 21(1): 52 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/35915448/. DOI: 10.1186/s12938-022-01022-6.
[28]
PALOMBO M, BODINI B, GRUSSU F, et al. ESR Essentials: diffusion-weighted MRI: practice recommendations by the European Society for Magnetic Resonance in Medicine and Biology[J]. Eur Radiol, 2026, 36(3): 2198-2208. DOI: 10.1007/s00330-025-12033-x.
[29]
HWANG S H, RHEE H. Radiologic features of hepatocellular carcinoma related to prognosis[J]. J Liver Cancer, 2023, 23(1): 143-156. DOI: 10.17998/jlc.2023.02.16.
[30]
ZHAO J K, LI X B, ZHANG K, et al. Prediction of microvascular invasion of hepatocellular carcinoma with preoperative diffusion-weighted imaging: A comparison of mean and minimum apparent diffusion coefficient values[J/OL]. Medicine, 2017, 96(33): e7754 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/28816952/. DOI: 10.1097/md.0000000000007754.
[31]
SUROV A, PECH M, OMARI J, et al. Diffusion-weighted imaging reflects tumor grading and microvascular invasion in hepatocellular carcinoma[J]. Liver Cancer, 2021, 10(1): 10-24. DOI: 10.1159/000511384.
[32]
LIU B E, ZENG Q Y, HUANG J B, et al. IVIM using convolutional neural networks predicts microvascular invasion in HCC[J]. Eur Radiol, 2022, 32(10): 7185-7195. DOI: 10.1007/s00330-022-08927-9.
[33]
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.
[34]
HONG S B, CHOI S H, KIM S Y, et al. MRI features for predicting microvascular invasion of hepatocellular carcinoma: a systematic review and meta-analysis[J]. Liver Cancer, 2021, 10(2): 94-106. DOI: 10.1159/000513704.
[35]
GAO L M, XIONG M L, CHEN X J, et al. Multi-region radiomic analysis based on multi-sequence MRI can preoperatively predict microvascular invasion in hepatocellular carcinoma[J/OL]. Front Oncol, 2022, 12: 818681 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/35574328/. DOI: 10.3389/fonc.2022.818681.
[36]
ZHANG Y, SHU Z Y, YE Q, et al. Preoperative prediction of microvascular invasion in hepatocellular carcinoma via multi-parametric MRI radiomics[J/OL]. Front Oncol, 2021, 11: 633596 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/33747956/. DOI: 10.3389/fonc.2021.633596.
[37]
BLUEMKE D A, KAWEL-BOEHM N. Can a MR imaging scanner accurately measure hematocrit to determine ECV fraction [J]. JACC Cardiovasc Imaging, 2016, 9(1): 64-66. DOI: 10.1016/j.jcmg.2015.11.009.
[38]
CUI F J, LUO Y H. Research progress of extracellular volume of abdominal lesions based on imaging[J]. Radiol Pract, 2020, 35(9): 1196-1198.
[39]
LU M, WANG C, YU J, et al. The value of T1 mapping and ECV in the diagnosis of acute pancreatitis and recurrent acute pancreatitis: a primary study[J]. Chin J Magn Reson Imaging, 2021, 12(11): 26-30. DOI: 10.12015/issn.1674-8034.2021.11.006.
[40]
SHARRACK N, BIGLANDS J D, BROADBENT D A, et al. The impact of water exchange on estimates of myocardial extracellular volume calculated using contrast enhanced T1 measurements: a preliminary analysis in patients with severe aortic stenosis[J]. Magn Reson Med, 2024, 91(4): 1637-1644. DOI: 10.1002/mrm.29956.
[41]
CAI Z P, LIU Z W, CHEN Y F, et al. Value of quantitative parameters of T1 mapping obtained from volume of interest of tumor and peritumoral combined with gadoxetic acid-enhanced MRI in evaluating microvascular invasion of hepatocellular carcinoma[J]. J Mol Imaging, 2023, 46(3): 413-420. DOI: 10.12122/j.issn.1674-4500.2023.03.05.
[42]
ZHANG Q, ZHANG Y L, LIU X. Research progress of synthetic magnetic resonance imaging technology in malignant tumors[J]. Chin J Magn Reson Imaging, 2023, 14(5): 196-202. DOI: 10.12015/issn.1674-8034.2023.05.035.
[43]
SHIYOVICH A, SINGH A, BLAIR C V, et al. Photon-counting computed tomography in cardiac imaging[J]. JACC Cardiovasc Imaging, 2026, 19(1): 94-117. DOI: 10.1016/j.jcmg.2025.07.022.
[44]
WANG Z H, WU Y, GUO R, et al. Prediction of microvascular invasion of hepatocellular carcinoma based on CT extracellular volume fraction[J]. Radiol Pract, 2025, 40(8): 995-1001. DOI: 10.13609/j.cnki.1000-0313.2025.08.008.
[45]
WEI M, FENG Q X, LIU X S. Research on evaluation of microvascular invasion in hepatocellular carcinoma based on quantitative parameters of T1 mapping[J]. J Mod Oncol, 2024, 32(11): 2059-2064. DOI: 10.3969/j.issn.1672-4992.2024.11.019.
[46]
SHEN L, LI Y Q, HUANG H Z, et al. HER2 in gastric cancer: a comprehensive analysis combining meta-analysis and DCE-MRI radiomics[J/OL]. Cancer Control, 2024, 31: 10732748241293699 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/39448273/. DOI: 10.1177/10732748241293699.
[47]
XIANG J Y, DAI Y S, ZHENG J Y, et al. Cardiac magnetic resonance-derived extracellular volume radiomics in reperfused ST-elevation myocardial infarction: long-term prognostic value and risk stratification[J]. Eur Heart J Cardiovasc Imaging, 2025, 26(8): 1376-1386. DOI: 10.1093/ehjci/jeaf140.
[48]
TANG X, ZHUANG Z J, JIANG L, et al. A preoperative CT-based multiparameter deep learning and radiomic model with extracellular volume parameter images can predict the tumor budding grade in rectal cancer patients[J]. Acad Radiol, 2025, 32(7): 4002-4012. DOI: 10.1016/j.acra.2025.02.028.
[49]
OKADA M, AOKI R, NAKAZAWA Y, et al. CT and MR imaging of hepatocellular carcinoma and liver cirrhosis[J]. Gastroenterol Insights, 2024, 15(4): 976-997. DOI: 10.3390/gastroent15040068.
[50]
TANG L T, ZHAO W J, LI K, et al. Assessing microvascular dysfunction and predicting long-term prognosis in patients with cardiac amyloidosis by cardiovascular magnetic resonance quantitative stress perfusion[J/OL]. J Cardiovasc Magn Reson, 2025, 27(1): 101134 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/39675481/. DOI: 10.1016/j.jocmr.2024.101134.
[51]
ZHANG Y F, WANG S T, SONG M Y, et al. MRI-based intra- and peritumoral heterogeneity in hepatocellular carcinoma for microvascular invasion prediction and prognostic risk stratification[J/OL]. Radiol Imaging Cancer, 2025, 7(6): e250066 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/41134139/. DOI: 10.1148/rycan.250066.
[52]
LI F, LIU R S, CAO J M, et al. Integrating multiphase MRI surrogates to improve microvascular invasion detection and recurrence risk stratification in HCC[J/OL]. JHEP Rep, 2026, 8(2): 101682 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/41551408/. DOI: 10.1016/j.jhepr.2025.101682.
[53]
LIANG J H, WANG M, CHEN W T, et al. Research progress of radiomics combined with biomarkers for predicting microvascular invasion in liver cancer[J]. Med J Peking Union Med Coll Hosp, 2025, 16(4): 826-835. DOI: 10.12290/xhyxzz.2025-0312.
[54]
WANG Y J, YANG Y Y, WEI B, et al. Application and research progress of extracellular volume based on MRI and CT in malignant tumor[J]. Chin J Magn Reson Imaging, 2023, 14(9): 131-135. DOI: 10.12015/issn.1674-8034.2023.09.024.
[55]
LEI Y, MATKOVIC L A, ROPER J, et al. Diffeomorphic transformer-based abdomen MRI-CT deformable image registration[J]. Med Phys, 2024, 51(9): 6176-6184. DOI: 10.1002/mp.17235.
[56]
ZHANG Z Y, CHEN M, LU K, et al. Finite element method-based hybrid MRI/CBCT generation to improve liver stereotactic body radiation therapy targets localization accuracy[J]. IEEE Trans Radiat Plasma Med Sci, 2025, 9(3): 372-381. DOI: 10.1109/trpms.2024.3466184.
[57]
VÁSQUEZ OSORIO E M, HOOGEMAN M S, MÉNDEZ ROMERO A, et al. Accurate CT/MR vessel-guided nonrigid registration of largely deformed livers[J]. Med Phys, 2012, 39(5): 2463-2477. DOI: 10.1118/1.3701779.
[58]
NGUYEN H P, JANG S Y, KIM S. A feasibility study on multimodal CT-MRI registration using segmentation aid and CoLlAGe feature extraction approach[J]. Abdom Radiol, 2026, 51(6): 3227-3236. DOI: 10.1007/s00261-025-05264-5.
[59]
HUBER A R, GONZALEZ R S, ORLOFF M S, et al. Accuracy of vascular invasion reporting in hepatocellular carcinoma before and after implementation of subspecialty surgical pathology sign-out[J]. Indian J Pathol Microbiol, 2017, 60(4): 501-504. DOI: 10.4103/IJPM.IJPM_827_16.
[60]
HU W J, CAI X D, ZHAO Y, et al. Interpretable deep learning framework based on contrast-enhanced MRI for predicting histological grade of hepatocellular carcinoma[J/OL]. Quant Imaging Med Surg, 2026, 16(1): 86 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/41522043/. DOI: 10.21037/qims-2025-269.
[61]
YOU H Y, WANG J F, MA R X, 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 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/37627833/. DOI: 10.3390/bioengineering10080948.
[62]
WANG F, LIU M, LIANG M H, et al. Interpretable stacking model integrating intra-/ peritumoral CT-radiomics and serum biomarkers for predicting microvascular invasion in HCC: a dual-center retrospective study[J/OL]. BMC Gastroenterol, 2026, 26(1): 275 [2026-02-09]. https://pubmed.ncbi.nlm.nih.gov/41882552/. DOI: 10.1186/s12876-026-04772-y.

PREV Research progress of gadoxetic acid disodium-enhanced MRI in predicting microvascular invasion of hepatocellular carcinoma
NEXT Advances in radiomics for rectal cancer immunotherapy: From treatment response assessment to prognostic prediction
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn