Research advances in radiomics combined with multi-omics in hepatocellular carcinoma

doi:10.3969/j.issn.1006-7795.2025.05.025

Abstract

Abstract: Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor in the world, accounting for 75%-85%. Due to the high incidence, strong heterogeneity and poor prognosis of HCC, early monitoring and fine assessment of high-risk populations are particularly important. In recent years, as a non-invasive method, radiomics has been widely used in the diagnosis, pathological characterization, selection of treatment methods and evaluation of curative effect of HCC. In addition, radiomics combined with next-generation sequencing technology and other multi-omics methods provide new ideas for HCC research. This paper reviews the research progress of radiomics combined with multi-omics in the field of HCC, in order to provide a new perspective for the diagnosis and treatment of HCC.

Key words: hepatocellular carcinoma (HCC), radiomics, multi-omics, radiogenomics, radioimmunomics, deep learning, personalized medicine

CLC Number:

R445

Liu Yanyi, Zhang Yanyan, Li Hongjun. Research advances in radiomics combined with multi-omics in hepatocellular carcinoma[J]. Journal of Capital Medical University, 2025, 46(5): 934-939.

References

［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: a cancer journal for clinicians, 2021, 71(3): 209-49. DOI:10.3322/caac.21660
［2］ Heimbach J K, Kulik L M, Finn R S, et al. AASLD guidelines for the treatment of hepatocellular carcinoma［J］. Hepatology, 2018, 67(1): 358-380.
［3］ Liu X Y, Elbanan M G, Luna A, et al. Radiomics in abdominopelvic solid-organ oncologic imaging: current status［J］. AJR Am J Roentgenol, 2022, 219(6): 985-995.
［4］ Liu X Y, 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?［J］. Eur Radiol, 2021, 31(1): 244-255.
［5］ 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.
［6］ Wu M H, Tan H N, Gao F, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature［J］. Eur Radiol, 2019, 29(6): 2802-2811.
［7］ Xu X, Zhang H L, Liu Q P, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma［J］. J Hepatol, 2019, 70(6): 1133-1144.
［8］ 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.
［9］ Chen M Y, Cao J S, Hu J H, et al. Clinical-radiomic analysis for pretreatment prediction of objective response to first transarterial chemoembolization in hepatocellular carcinoma［J］. Liver Cancer, 2021, 10(1): 38-51.
［10］ Kong C L, Zhao Z W, Chen W Y, et al. Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE［J］. Eur Radiol, 2021, 31(10): 7500-7511.
［11］ Zheng B H, Liu L Z, Zhang Z Z, et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients［J］. BMC Cancer, 2018, 18(1): 1148.
［12］ Segal E, Sirlin C B, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging［J］. Nat Biotechnol, 2007, 25(6): 675-680.
［13］ Peng J, Lu F Y, Huang J H, et al. Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma［J］. Front Oncol, 2022, 12: 853254.
［14］ Feng Z C, Li H L, Liu Q Y, et al. CT radiomics to predict macrotrabecular-massive subtype and immune status in hepatocellular carcinoma［J］. Radiology, 2023, 307(1): e221291.
［15］ Liao H T, Jiang H Y, Chen Y T, et al. Predicting genomic alterations of phosphatidylinositol-3 kinase signaling in hepatocellular carcinoma: a radiogenomics study based on next-generation sequencing and contrast-enhanced CT［J］. Ann Surg Oncol, 2022, 29(7): 4552-4564.
［16］ Wang Y Q, Gao B, Xia C H, et al. Development of a novel tumor microenvironment-related radiogenomics model for prognosis prediction in hepatocellular carcinoma［J］. Quant Imaging Med Surg, 2023, 13(9): 5803-5814.
［17］ Xia W, Chen Y, Zhang R, et al. Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data-a preliminary study［J］. Phys Med Biol, 2018, 63(3): 035044.
［18］ Khalili N, Kazerooni A F, Familiar A, et al. Radiomics for characterization of the glioma immune microenvironment［J］. NPJ Precis Oncol, 2023, 7(1): 59.
［19］ Liu Q Y, Zhu W W, Song F L, et al. Radio-immunomics in hepatocellular carcinoma: unraveling the tumor immune microenvironment［J］. Meta-Radiology, 2024, 2(3): 100098.
［20］ Cariani E, Pilli M, Zerbini A, et al. Immunological and molecular correlates of disease recurrence after liver resection for hepatocellular carcinoma［J］. PLoS One, 2012, 7(3): e32493.
［21］ Khemlina G, Ikeda S, Kurzrock R. The biology of hepatocellular carcinoma: implications for genomic and immune therapies［J］. Mol Cancer, 2017, 16(1): 149.
［22］ Sun R, Limkin E J, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study［J］. Lancet Oncol, 2018, 19(9): 1180-1191.
［23］ Palucka A K, Coussens L M. The basis of oncoimmunology［J］. Cell, 2016, 164(6): 1233-1247.
［24］ Galon J, Mlecnik B, Bindea G, et al. Towards the introduction of the ‘immunoscore’ in the classification of malignant tumours［J］. J Pathol, 2014, 232(2): 199-209.
［25］ Bruni D, Angell H K, Galon J. The immune contexture and immunoscore in cancer prognosis and therapeutic efficacy［J］. Nat Rev Cancer, 2020, 20(11): 662-680.
［26］ Gabrielson A, Wu Y N, Wang H K, et al. Intratumoral CD3 and CD8 T-cell densities associated with relapse-free survival in HCC［J］. Cancer Immunol Res, 2016, 4(5): 419-430.
［27］ Hectors S J, Wagner M, Bane O, et al. Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging［J］. Sci Rep, 2017, 7(1): 2452.
［28］ Chen S L, Feng S T, Wei J W, et al. Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging［J］. Eur Radiol, 2019, 29(8): 4177-4187.
［29］ Liao H T, Zhang Z, Chen J, et al. Preoperative radiomic approach to evaluate tumor-infiltrating CD8+ T cells in hepatocellular carcinoma patients using contrast-enhanced computed tomography［J］. Ann Surg Oncol, 2019, 26(13): 4537-4547.
［30］ Sangro B, Chan S L, Meyer T, et al. Diagnosis and management of toxicities of immune checkpoint inhibitors in hepatocellular carcinoma［J］. J Hepatol, 2020, 72(2): 320-341.
［31］ Hectors S J, Lewis S, Besa C, et al. MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma［J］. Eur Radiol, 2020, 30(7): 3759-3769.
［32］ 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］. Sci Rep, 2023, 13(1): 7710.
［33］ Montalbano M, Rastellini C, McGuire J T, et al. Role of glypican-3 in the growth, migration and invasion of primary hepatocytes isolated from patients with hepatocellular carcinoma［J］. Cell Oncol, 2018, 41(2): 169-184.
［34］ 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.
［35］ Liu P J, Li W Q, Qiu G B, et al. Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma［J］. Front Oncol, 2023, 13: 1142916.
［36］ Li Z J, Wang Y Y, Yu J H, et al. Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma［J］. Sci Rep, 2017, 7(1): 5467.
［37］ Gul S, Khan M S, Bibi A, et al. Deep learning techniques for liver and liver tumor segmentation: a review［J］. Comput Biol Med, 2022, 147: 105620.
［38］ McBee M P, Awan O A, Colucci A T, et al. Deep learning in radiology［J］. Acad Radiol, 2018, 25(11): 1472-1480.
［39］ Zheng X Y, Yao Z, Huang Y N, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer［J］. Nat Commun, 2020, 11(1): 1236.
［40］ Wang K, Lu X, Zhou H, et al. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study［J］. Gut, 2019, 68(4): 729-741.
［41］ Chen Y Y, Chen J, Zhang Y, et al. Preoperative prediction of cytokeratin 19 expression for hepatocellular carcinoma with deep learning radiomics based on gadoxetic acid-enhanced magnetic resonance imaging［J］. J Hepatocell Carcinoma, 2021, 8: 795-808.
［42］ He T C, Fong J N, Moore L W, et al. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer［J］. Comput Med Imaging Graph, 2021, 89: 101894.
［43］ Ding Y, Ruan S J, Wang Y B Z, et al. Novel deep learning radiomics model for preoperative evaluation of hepatocellular carcinoma differentiation based on computed tomography data［J］. Clin Transl Med, 2021, 11(11): e570.
［44］ Gao W Y, Wang W T, Song D J, et al. A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma［J］. Radiol Med, 2022, 127(3): 259-271.
［45］ Peng J, Huang J H, Huang G J, et al. Predicting the initial treatment response to transarterial chemoembolization in intermediate-stage hepatocellular carcinoma by the integration of radiomics and deep learning［J］. Front Oncol, 2021, 11: 730282.
［46］ Guo L X, 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［J］. EClinicalMedicine, 2024, 74: 102718.
［47］ Pinker K, Chin J, Melsaether A N, et al. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment［J］. Radiology, 2018, 287(3): 732-747.
［48］ Huang E P, O'Connor J P B, McShane L M, et al. Criteria for the translation of radiomics into clinically useful tests［J］. Nat Rev Clin Oncol, 2023, 20(2): 69-82.
［49］ Liu Z Q, Duan T, Zhang Y Y, et al. Radiogenomics: a key component of precision cancer medicine［J］. Br J Cancer, 2023, 129(5): 741-753.
［50］ Avanzo M, Wei L S, Stancanello J, et al. Machine and deep learning methods for radiomics［J］. Med Phys, 2020, 47(5): e185-e202.
［51］ Li J A, Tian J Y, Liu Y C, et al. Personalized analysis of human cancer multi-omics for precision oncology［J］. Comput Struct Biotechnol J, 2024, 23: 2049-2056.

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Research advances in radiomics combined with multi-omics in hepatocellular carcinoma

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