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Research progress on MRI radiomics in the progression, recurrence and prognosis prediction of hepatocellular carcinoma
GAO Kaihua  WU Hui 

Cite this article as: GAO K H, WU H. Research progress on MRI radiomics in the progression, recurrence and prognosis prediction of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(12): 181-186. DOI:10.12015/issn.1674-8034.2023.12.033.


[Abstract] Primary liver cancer ranks third among the world's leading causes of cancer death, with hepatocellular carcinoma being the most common. In most cases, the cancer is advanced when hepatocellular carcinoma is diagnosed, so the prognosis for hepatocellular carcinoma is usually poor. MRI radiomics technology can combine imaging information and clinical risk factors to build a combination model for preoperative prediction and postoperative evaluation of hepatocellular carcinoma patients. This article reviews the application potential of MRI radiomics in microvascular invasion, prediction of recurrence, efficacy assessment and survival analysis in patients with hepatocellular carcinoma, and shows that MRI radiomics models have good predictive ability, guide patients and clinicians to choose the appropriate treatment plan to improve the survival rate of patients, and hope to provide more new ideas and new directions for future scientific research.
[Keywords] hepatocellular carcinoma；microvascular invasion；magnetic resonance imaging；radiomics；prognosis

GAO Kaihua   WU Hui*  

Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010050, China

Corresponding author: WU H, E-mail: terrywuhui@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Inner Mongolia Autonomous Region (No. 2021MS08026); General Program of Inner Mongolia Medical University (No. YKD2021MS045).
Received  2023-09-19
Accepted  2023-11-29
DOI: 10.12015/issn.1674-8034.2023.12.033
Cite this article as: GAO K H, WU H. Research progress on MRI radiomics in the progression, recurrence and prognosis prediction of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(12): 181-186. DOI:10.12015/issn.1674-8034.2023.12.033.

[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]
KULIK L, EL-SERAG H B. Epidemiology and management of hepatocellular carcinoma[J/OL]. Gastroenterology, 2019, 156(2): 477-491.e1 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/30367835. DOI: 10.1053/j.gastro.2018.08.065.
[3]
KHEMLINA G, IKEDA S, KURZROCK R. The biology of Hepatocellular carcinoma: implications for genomic and immune therapies[J/OL]. Mol Cancer, 2017, 16(1): 149 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/28854942. DOI: 10.1186/s12943-017-0712-x.
[4]
KUREBAYASHI Y, OJIMA H, TSUJIKAWA H, et al. Landscape of immune microenvironment in hepatocellular carcinoma and its additional impact on histological and molecular classification[J]. Hepatology, 2018, 68(3): 1025-1041. DOI: 10.1002/hep.29904.
[5]
LI M S, YIN Z Y, HU B, et al. MR elastography-based shear strain mapping for assessment of microvascular invasion in hepatocellular carcinoma[J]. Eur Radiol, 2022, 32(7): 5024-5032. DOI: 10.1007/s00330-022-08578-w.
[6]
JI G W, ZHU F P, XU Q, et al. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: a multi-institutional study[J]. EBioMedicine, 2019, 50: 156-165. DOI: 10.1016/j.ebiom.2019.10.057.
[7]
RODRÍGUEZ-PERÁLVAREZ M, LUONG T V, ANDREANA L, et al. A systematic review of microvascular invasion in hepatocellular carcinoma: diagnostic and prognostic variability[J]. Ann Surg Oncol, 2013, 20(1): 325-339. DOI: 10.1245/s10434-012-2513-1.
[8]
LIM K C, CHOW P K, ALLEN J C, et al. Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria[J]. Ann Surg, 2011, 254(1): 108-113. DOI: 10.1097/SLA.0b013e31821ad884.
[9]
LI Y C, ZHANG Y, FANG Q, et al. Radiomics analysis of[18F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma[J]. Eur J Nucl Med Mol Imaging, 2021, 48(8): 2599-2614. DOI: 10.1007/s00259-020-05119-9.
[10]
HU F, ZHANG Y H, LI M, et al. Preoperative prediction of microvascular invasion risk grades in hepatocellular carcinoma based on tumor and peritumor dual-region radiomics signatures[J/OL]. Front Oncol, 2022, 12: 853336 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/35392229. DOI: 10.3389/fonc.2022.853336.
[11]
LÉVI-STRAUSS T, TORTORICI B, LOPEZ O, et al. Radiomics, a promising new discipline: example of hepatocellular carcinoma[J/OL]. Diagnostics, 2023, 13(7): 1303 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/37046521. DOI: 10.3390/diagnostics13071303.
[12]
ZHU Y J, FENG B, WANG S, et al. Model-based three-dimensional texture analysis of contrast-enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J]. Oncol Lett, 2019, 18(1): 720-732. DOI: 10.3892/ol.2019.10378.
[13]
LU X Y, ZHANG J Y, ZHANG T, et al. Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI[J/OL]. BMC Med Imaging, 2022, 22(1): 157 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/36057576. DOI: 10.1186/s12880-022-00855-w.
[14]
MENG X P, WANG Y C, ZHOU J Y, et al. Comparison of MRI and CT for the prediction of microvascular invasion in solitary hepatocellular carcinoma based on a non-radiomics and radiomics method: which imaging modality is better?[J]. J Magn Reson Imaging, 2021, 54(2): 526-536. DOI: 10.1002/jmri.27575.
[15]
LIU X F, WANG Q H, YANG H A, et al. A nomogram model for predicting microvascular invasion of hepatocellular carcinoma based on multi-sequences MRI radiomics score and clinical-pathology-imaging parameters[J]. Radiol Pract, 2023, 38(8): 1018-1025. DOI: 10.13609/j.cnki.1000-0313.2023.08.011.
[16]
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 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/33747956. DOI: 10.3389/fonc.2021.633596.
[17]
ZHANG R, XU L, WEN X, et al. A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J]. Quant Imaging Med Surg, 2019, 9(9): 1503-1515. DOI: 10.21037/qims.2019.09.07.
[18]
LIU J P, CHENG D L, LIAO Y T, et al. Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma[J]. Quant Imaging Med Surg, 2023, 13(6): 3948-3961. DOI: 10.21037/qims-22-1011.
[19]
DAI H J, LU M H, HUANG B S, et al. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging[J]. Quant Imaging Med Surg, 2021, 11(5): 1836-1853. DOI: 10.21037/qims-20-218.
[20]
HU M J, YU Y X, FAN Y F, et al. The predictive value of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid enhanced MRI imaging features combined with quantitative parameters for the pathologic grading of hepatocellular carcinoma[J]. Natl Med J China, 2020, 100(17): 1299-1304. DOI: 10.3760/cma.j.cn112137-20191021-02281.
[21]
MIRANDA J, HORVAT N, FONSECA G M, et al. Current status and future perspectives of radiomics in hepatocellular carcinoma[J]. World J Gastroenterol, 2023, 29(1): 43-60. DOI: 10.3748/wjg.v29.i1.43.
[22]
FENG S T, JIA Y M, LIAO B, et al. Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI[J]. Eur Radiol, 2019, 29(9): 4648-4659. DOI: 10.1007/s00330-018-5935-8.
[23]
ZHANG S, XU G Z, DUAN C F, et al. Radiomics analysis of MR imaging with Gd-EOB-DTPA for preoperative prediction of microvascular invasion in hepatocellular carcinoma: investigation and comparison of different hepatobiliary phase delay times[J/OL]. Biomed Res Int, 2021, 2021: 6685723 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/33506029. DOI: 10.1155/2021/6685723.
[24]
YANG L, GU D S, WEI J W, et al. A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J]. Liver Cancer, 2019, 8(5): 373-386. DOI: 10.1159/000494099.
[25]
TIAN Y Q, HUA H, PENG Q Q, et al. Preoperative evaluation of Gd-EOB-DTPA-enhanced MRI radiomics-based nomogram in small solitary hepatocellular carcinoma (≤3 cm) with microvascular invasion: a two-center study[J]. J Magn Reson Imaging, 2022, 56(5): 1459-1472. DOI: 10.1002/jmri.28157.
[26]
NEBBIA G, ZHANG Q, AREFAN D, et al. Pre-operative microvascular invasion prediction using multi-parametric liver MRI radiomics[J]. J Digit Imaging, 2020, 33(6): 1376-1386. DOI: 10.1007/s10278-020-00353-x.
[27]
BRUIX J, REIG M, SHERMAN M. Evidence-based diagnosis, staging, and treatment of patients with hepatocellular carcinoma[J]. Gastroenterology, 2016, 150(4): 835-853. DOI: 10.1053/j.gastro.2015.12.041.
[28]
LI W C, SHEN H R, HAN L Z, et al. A multiparametric fusion radiomics signature based on contrast-enhanced MRI for predicting early recurrence of hepatocellular carcinoma[J/OL]. J Oncol, 2022, 2022: 3704987 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/36213823. DOI: 10.1155/2022/3704987.
[29]
WEN L T, WENG S P, YAN C, et al. A radiomics nomogram for preoperative prediction of early recurrence of small hepatocellular carcinoma after surgical resection or radiofrequency ablation[J/OL]. Front Oncol, 2021, 11: 657039 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/34026632. DOI: 10.3389/fonc.2021.657039.
[30]
WANG L Y, MA X H, FENG B, et al. Multi-sequence MR-based radiomics signature for predicting early recurrence in solitary hepatocellular carcinoma ≤5 cm[J/OL]. Front Oncol, 2022, 12: 899404 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/35756618. DOI: 10.3389/fonc.2022.899404.
[31]
CHONG H H, GONG Y D, PAN X P, et al. Peritumoral dilation radiomics of gadoxetate disodium-enhanced MRI excellently predicts early recurrence of hepatocellular carcinoma without macrovascular invasion after hepatectomy[J]. J Hepatocell Carcinoma, 2021, 8: 545-563. DOI: 10.2147/JHC.S309570.
[32]
ZHAO Y, WU J J, ZHANG Q H, et al. Radiomics analysis based on multiparametric MRI for predicting early recurrence in hepatocellular carcinoma after partial hepatectomy[J]. J Magn Reson Imaging, 2021, 53(4): 1066-1079. DOI: 10.1002/jmri.27424.
[33]
KIM S, SHIN J, KIM D Y, et al. Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma[J]. Clin Cancer Res, 2019, 25(13): 3847-3855. DOI: 10.1158/1078-0432.CCR-18-2861.
[34]
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.
[35]
ZHANG Z, JIANG H Y, CHEN J, et al. Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction[J/OL]. Cancer Imaging, 2019, 19(1): 22 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/31088553. DOI: 10.1186/s40644-019-0209-5.
[36]
SONG W L, YU X L, GUO D J, et al. MRI-based radiomics: associations with the recurrence-free survival of patients with hepatocellular carcinoma treated with conventional transcatheter arterial chemoembolization[J]. J Magn Reson Imaging, 2020, 52(2): 461-473. DOI: 10.1002/jmri.26977.
[37]
MAKARY M S, KHANDPUR U, CLOYD J M, et al. Locoregional therapy approaches for hepatocellular carcinoma: recent advances and management strategies[J/OL]. Cancers, 2020, 12(7): 1914 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/32679897. DOI: 10.3390/cancers12071914.
[38]
FANG S J, LAI L Q, ZHU J Y, et al. A radiomics signature-based nomogram to predict the progression-free survival of patients with hepatocellular carcinoma after transcatheter arterial chemoembolization plus radiofrequency ablation[J/OL]. Front Mol Biosci, 2021, 8: 662366 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/34532340. DOI: 10.3389/fmolb.2021.662366.
[39]
LUO J P, HUANG Z M, WANG M R, et al. Prognostic role of multiparameter MRI and radiomics in progression of advanced unresectable hepatocellular carcinoma following combined transcatheter arterial chemoembolization and lenvatinib therapy[J/OL]. BMC Gastroenterol, 2022, 22(1): 108 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/35260095. DOI: 10.1186/s12876-022-02129-9.
[40]
WANG X H, LONG L H, CUI Y, et al. MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma[J]. Br J Cancer, 2020, 122(7): 978-985. DOI: 10.1038/s41416-019-0706-0.
[41]
ZHAO Y, WANG N, WU J J, et al. Radiomics analysis based on contrast-enhanced MRI for prediction of therapeutic response to transarterial chemoembolization in hepatocellular carcinoma[J/OL]. Front Oncol, 2021, 11: 582788 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/33868988. DOI: 10.3389/fonc.2021.582788.
[42]
SUN Y J, BAI H L, XIA W, et al. Predicting the outcome of transcatheter arterial embolization therapy for unresectable hepatocellular carcinoma based on radiomics of preoperative multiparameter MRI[J]. J Magn Reson Imaging, 2020, 52(4): 1083-1090. DOI: 10.1002/jmri.27143.
[43]
ZHANG L, CAI P Q, HOU J Y, et al. Radiomics model based on gadoxetic acid disodium-enhanced MR imaging to predict hepatocellular carcinoma recurrence after curative ablation[J]. Cancer Manag Res, 2021, 13: 2785-2796. DOI: 10.2147/CMAR.S300627.
[44]
HE Y, HU B, ZHU C Z, et al. A novel multimodal radiomics model for predicting prognosis of resected hepatocellular carcinoma[J/OL]. Front Oncol, 2022, 12: 745258 [2023-09-24]. https://pubmed.ncbi.nlm.nih.gov/35321432. DOI: 10.3389/fonc.2022.745258.
[45]
ZHANG Z, CHEN J, JIANG H Y, et al. Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection[J/OL]. Ann Transl Med, 2020, 8(14): 870 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/32793714. DOI: 10.21037/atm-20-3041.
[46]
RIZZO S, BOTTA F, RAIMONDI S, et al. Radiomics: the facts and the challenges of image analysis[J/OL]. Eur Radiol Exp, 2018, 2(1): 36 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/30426318. DOI: 10.1186/s41747-018-0068-z.
[47]
LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
[48]
LEE H, PALM J, GRIMES S M, et al. The Cancer Genome Atlas Clinical Explorer: a web and mobile interface for identifying clinical-genomic driver associations[J/OL]. Genome Med, 2015, 7: 112 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/26507825. DOI: 10.1186/s13073-015-0226-3.
[49]
SALA E, MEMA E, HIMOTO Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging[J]. Clin Radiol, 2017, 72(1): 3-10. DOI: 10.1016/j.crad.2016.09.013.
[50]
VAN TIMMEREN J E, CESTER D, TANADINI-LANG S, et al. Radiomics in medical imaging- "how-to" guide and critical reflection[J/OL]. Insights Imaging, 2020, 11(1): 91 [2023-09-24]. https://www.ncbi.nlm.nih.gov/pubmed/32785796. DOI: 10.1186/s13244-020-00887-2.
[51]
CARBONELL G, KENNEDY P, BANE O, et al. Precision of MRI radiomics features in the liver and hepatocellular carcinoma[J]. Eur Radiol, 2022, 32(3): 2030-2040. DOI: 10.1007/s00330-021-08282-1.
[52]
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.
[53]
YAO S, YE Z, WEI Y, et al. Radiomics in hepatocellular carcinoma: a state-of-the-art review[J]. World J Gastrointest Oncol, 2021, 13(11): 1599-1615. DOI: 10.4251/wjgo.v13.i11.1599.

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