Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Abdominal Radiology
Published in: Abdominal Radiology 8/2021

01-08-2021 | Hepatocellular Carcinoma | Special Section: HCC treatment

Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response

Authors: Amir A. Borhani, Roberta Catania, Yuri S. Velichko, Stefanie Hectors, Bachir Taouli, Sara Lewis

Published in: Abdominal Radiology | Issue 8/2021

Login to get access

Abstract

Radiomics refers to the process of conversion of conventional medical images into quantifiable data (“features”) which can be further mined to reveal complex patterns and relationships between the voxels in the image. These high throughput features can potentially reflect the histology of biologic tissues at macroscopic and microscopic levels. Several studies have investigated radiomics of hepatocellular carcinoma (HCC) before and after treatment. HCC is a heterogeneous disease with diverse phenotypical and genotypical landscape. Due to this inherent heterogeneity, HCC lesions can manifest variable aggressiveness with different response to treatment options, including the newer targeted therapies. Hence, radiomics can be used as a potential tool to enable patient selection for therapies and to predict response to treatments and outcome. Additionally, radiomics may serve as a tool for earlier and more efficient assessment of response to treatment. Radiomics, radiogenomics, and radio-immunoprofiling and their potential roles in management of patients with HCC will be discussed and critically reviewed in this article.
Literature
1.
Gillies, R.J., P.E. Kinahan, and H. Hricak, Radiomics: Images Are More than Pictures, They Are Data. Radiology, 2016. 278(2): p. 563-77.PubMed
2.
Khemlina, G., S. Ikeda, and R. Kurzrock, The biology of Hepatocellular carcinoma: implications for genomic and immune therapies. Mol Cancer, 2017. 16(1): p. 149.PubMedPubMedCentralCrossRef
3.
Kurebayashi, Y., et al., Landscape of immune microenvironment in hepatocellular carcinoma and its additional impact on histological and molecular classification. Hepatology, 2018. 68(3): p. 1025-1041.PubMedCrossRef
4.
5.
Cariani, E., et al., Immunological and molecular correlates of disease recurrence after liver resection for hepatocellular carcinoma. PLoS One, 2012. 7(3): p. e32493.PubMedPubMedCentralCrossRef
6.
Lewis, S., S. Hectors, and B. Taouli, Radiomics of hepatocellular carcinoma. Abdom Radiol (NY), 2020.
7.
Miranda Magalhaes Santos, J.M., et al., State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations. Abdom Radiol (NY), 2020. 45(2): p. 342–353.
8.
9.
10.
Apte, A.P., et al., Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research. Med Phys, 2018.
11.
Nioche, C., et al., LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. Cancer Res, 2018. 78(16): p. 4786-4789.PubMedCrossRef
12.
13.
Chlebus, G., et al., Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific Reports, 2018. 8(1): p. 15497.PubMedPubMedCentralCrossRef
14.
Fave, X., et al., Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer. Translational Cancer Research, 2016. 5(4): p. 349-363.CrossRef
15.
Shafiq-Ul-Hassan, M., et al., Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Medical physics, 2017. 44(3): p. 1050-1062.PubMedCrossRef
16.
Velichko, Y.S., et al., Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis. Academic Radiology, 2020.
17.
Lehmann, T.M., C. Gonner, and K. Spitzer, Addendum: B-spline interpolation in medical image processing. IEEE Transactions on Medical Imaging, 2001. 20(7): p. 660-665.PubMedCrossRef
18.
19.
Zwanenburg, A., et al., Image biomarker standardisation initiative-feature definitions. arXiv preprint arXiv:1612.07003, 2016.
20.
Haralick, R.M., K. Shanmugam, and I. Dinstein, Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 1973. SMC-3(6): p. 610–621.
21.
Parekh, V. and M.A. Jacobs, Radiomics: a new application from established techniques. Expert review of precision medicine and drug development, 2016. 1(2): p. 207-226.PubMedPubMedCentralCrossRef
22.
23.
Di Giovanni, P., et al., The biological correlates of macroscopic breast tumour structure measured using fractal analysis in patients undergoing neoadjuvant chemotherapy. Breast Cancer Research and Treatment, 2012. 133(3): p. 1199-1206.PubMedCrossRef
24.
Balboa, R.M. and N.M. Grzywacz, Power spectra and distribution of contrasts of natural images from different habitats. Vision Research, 2003. 43(24): p. 2527-2537.PubMedCrossRef
25.
Baish, J.W. and R.K. Jain, Fractals and Cancer. Cancer Research, 2000. 60(14): p. 3683-3688.PubMed
26.
Wang, X.-H., et al., MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma. British Journal of Cancer, 2020. 122(7): p. 978-985.PubMedPubMedCentralCrossRef
27.
Zwanenburg, A., et al., The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology, 2020. 295(2): p. 328-338.PubMedCrossRef
28.
29.
Tibshirani, R., Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 1996. 58(1): p. 267–288.
30.
Clark, K., et al., The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 2013. 26(6): p. 1045-1057.PubMedPubMedCentralCrossRef
31.
Llovet, J.M., et al., Molecular therapies and precision medicine for hepatocellular carcinoma. Nat Rev Clin Oncol, 2018. 15(10): p. 599-616.PubMedCrossRef
32.
Goossens, N., X. Sun, and Y. Hoshida, Molecular classification of hepatocellular carcinoma: potential therapeutic implications. Hepat Oncol, 2015. 2(4): p. 371-379.PubMedPubMedCentralCrossRef
33.
Friemel, J., et al., Intratumor heterogeneity in hepatocellular carcinoma. Clin Cancer Res, 2015. 21(8): p. 1951-61.PubMedCrossRef
34.
35.
Segal, E., et al., Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol, 2007. 25(6): p. 675-80.PubMedCrossRef
36.
Taouli, B., et al., Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results. Eur Radiol, 2017. 27(11): p. 4472-4481.PubMedPubMedCentralCrossRef
37.
Hectors, S.J., et al., MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma. Eur Radiol, 2020. 30(7): p. 3759-3769.PubMedPubMedCentralCrossRef
38.
Furlan, A., et al., A radiogenomic analysis of hepatocellular carcinoma: association between fractional allelic imbalance rate index and the liver imaging reporting and data system (LI-RADS) categories and features. Br J Radiol, 2018. 91(1086): p. 20170962.PubMedPubMedCentralCrossRef
39.
Kuo, M.D., et al., Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol, 2007. 18(7): p. 821-31.PubMedCrossRef
40.
Wakabayashi, T., et al., Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int, 2019. 13(5): p. 546-559.PubMedCrossRef
41.
Xia, W., et al., Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data-a preliminary study. Phys Med Biol, 2018. 63(3): p. 035044.PubMedCrossRef
42.
Hectors, S.J., et al., Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging. Sci Rep, 2017. 7(1): p. 2452.PubMedPubMedCentralCrossRef
43.
Zhu, A.X., et al., Ramucirumab in advanced hepatocellular carcinoma in REACH-2: the true value of alpha-fetoprotein. Lancet Oncol, 2019. 20(4): p. e191.PubMedCrossRef
44.
Sia, D., et al., Identification of an Immune-specific Class of Hepatocellular Carcinoma, Based on Molecular Features. Gastroenterology, 2017. 153(3): p. 812-826.PubMed
45.
Sun, R., 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. Lancet Oncol, 2018. 19(9): p. 1180-1191.PubMedCrossRef
46.
Ruiz de Galarreta, M., et al., beta-Catenin Activation Promotes Immune Escape and Resistance to Anti-PD-1 Therapy in Hepatocellular Carcinoma. Cancer Discov, 2019. 9(8): p. 1124–1141.
47.
Gabrielson, A., et al., Intratumoral CD3 and CD8 T-cell Densities Associated with Relapse-Free Survival in HCC. Cancer Immunol Res, 2016. 4(5): p. 419-30.PubMedPubMedCentralCrossRef
48.
Sun, C., et al., The predictive value of centre tumour CD8(+) T cells in patients with hepatocellular carcinoma: comparison with Immunoscore. Oncotarget, 2015. 6(34): p. 35602-15.PubMedPubMedCentralCrossRef
49.
Yao, Q., et al., Prognostic value of immunoscore to identify mortality outcomes in adults with HBV-related primary hepatocellular carcinoma. Medicine (Baltimore), 2017. 96(17): p. e6735.CrossRef
50.
Garnelo, M., et al., Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma. Gut, 2017. 66(2): p. 342-351.PubMedCrossRef
51.
Chen, S., et al., Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol, 2019. 29(8): p. 4177-4187.PubMedCrossRef
52.
Liao, H., et al., Preoperative Radiomic Approach to Evaluate Tumor-Infiltrating CD8(+) T Cells in Hepatocellular Carcinoma Patients Using Contrast-Enhanced Computed Tomography. Ann Surg Oncol, 2019. 26(13): p. 4537-4547.PubMedCrossRef
53.
Inchingolo, R., et al., Locoregional treatments for hepatocellular carcinoma: Current evidence and future directions. World J Gastroenterol, 2019. 25(32): p. 4614-4628.PubMedPubMedCentralCrossRef
54.
Bruix, J., M. Reig, and M. Sherman, Evidence-Based Diagnosis, Staging, and Treatment of Patients With Hepatocellular Carcinoma. Gastroenterology, 2016. 150(4): p. 835-53.CrossRefPubMed
55.
Chernyak, V., et al., Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology, 2018. 289(3): p. 816-830.PubMedCrossRef
56.
Mendiratta-Lala, M., et al., Natural history of hepatocellular carcinoma after stereotactic body radiation therapy. Abdom Radiol (NY), 2020. 45(11): p. 3698-3708.CrossRef
57.
Kloth, C., et al., Evaluation of Texture Analysis Parameter for Response Prediction in Patients with Hepatocellular Carcinoma Undergoing Drug-eluting Bead Transarterial Chemoembolization (DEB-TACE) Using Biphasic Contrast-enhanced CT Image Data: Correlation with Liver Perfusion CT. Acad Radiol, 2017. 24(11): p. 1352-1363.PubMedCrossRef
58.
Yu, J.Y., et al., Value of texture analysis based on enhanced MRI for predicting an early therapeutic response to transcatheter arterial chemoembolisation combined with high-intensity focused ultrasound treatment in hepatocellular carcinoma. Clin Radiol, 2018. 73(8): p. 758 e9–758 e18.
59.
Gordic, S., et al., Prediction of hepatocellular carcinoma response to (90)Yttrium radioembolization using volumetric ADC histogram quantification: preliminary results. Cancer Imaging, 2019. 19(1): p. 29.PubMedPubMedCentralCrossRef
60.
Sun, Y., et al., Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI. J Magn Reson Imaging, 2020. 52(4): p. 1083-1090.PubMedCrossRef
61.
Reis, S.P., et al., Tumor Enhancement and Heterogeneity Are Associated With Treatment Response to Drug-Eluting Bead Chemoembolization for Hepatocellular Carcinoma. J Comput Assist Tomogr, 2017. 41(2): p. 289-293.PubMedCrossRef
62.
Park, H.J., et al., Prediction of Therapeutic Response of Hepatocellular Carcinoma to Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings. AJR Am J Roentgenol, 2017. 209(4): p. W211-W220.PubMedCrossRef
63.
Llovet, J.M., et al., Advances in targeted therapies for hepatocellular carcinoma in the genomic era. Nat Rev Clin Oncol, 2015. 12(8): p. 436.PubMedCrossRef
64.
Llovet, J.M., et al., Sorafenib in advanced hepatocellular carcinoma. N Engl J Med, 2008. 359(4): p. 378-90.PubMedCrossRef
65.
Cheng, A.L., et al., Efficacy and safety of sorafenib in patients in the Asia-Pacific region with advanced hepatocellular carcinoma: a phase III randomised, double-blind, placebo-controlled trial. Lancet Oncol, 2009. 10(1): p. 25-34.PubMedCrossRef
66.
European Association for the Study of the Liver. Electronic address, e.e.e. and L. European Association for the Study of the, EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol, 2018. 69(1): p. 182–236.
67.
[67] Finn, R.S. and A.L. Cheng, Atezolizumab and Bevacizumab in Hepatocellular Carcinoma. Reply. N Engl J Med, 2020. 383(7): p. 695.PubMed
68.
Lee, M.S., et al., Atezolizumab with or without bevacizumab in unresectable hepatocellular carcinoma (GO30140): an open-label, multicentre, phase 1b study. Lancet Oncol, 2020. 21(6): p. 808-820.PubMedCrossRef
69.
Bteich, F. and A.M. Di Bisceglie, Current and Future Systemic Therapies for Hepatocellular Carcinoma. Gastroenterol Hepatol (N Y), 2019. 15(5): p. 266-272.
70.
Mule, S., et al., Advanced Hepatocellular Carcinoma: Pretreatment Contrast-enhanced CT Texture Parameters as Predictive Biomarkers of Survival in Patients Treated with Sorafenib. Radiology, 2018. 288(2): p. 445-455.PubMedCrossRef
71.
Fu, S., et al., Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib. Oncotarget, 2017. 8(23): p. 37855-37865.PubMedCrossRef
72.
Liu, Q.P., et al., Prediction of prognostic risk factors in hepatocellular carcinoma with transarterial chemoembolization using multi-modal multi-task deep learning. EClinicalMedicine, 2020. 23: p. 100379.PubMedPubMedCentralCrossRef
73.
Meng, X.P., et al., Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization. Front Oncol, 2020. 10: p. 1196.PubMedPubMedCentralCrossRef
74.
Song, W., et al., MRI-Based Radiomics: Associations With the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization. J Magn Reson Imaging, 2020. 52(2): p. 461-473.PubMedCrossRef
75.
Wang, X.H., et al., MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma. Br J Cancer, 2020. 122(7): p. 978-985.PubMedPubMedCentralCrossRef
76.
Kim, J., et al., Predicting Survival Using Pretreatment CT for Patients With Hepatocellular Carcinoma Treated With Transarterial Chemoembolization: Comparison of Models Using Radiomics. AJR Am J Roentgenol, 2018. 211(5): p. 1026-1034.PubMedCrossRef
77.
Blanc-Durand, P., et al., Signature of survival: a (18)F-FDG PET based whole-liver radiomic analysis predicts survival after (90)Y-TARE for hepatocellular carcinoma. Oncotarget, 2018. 9(4): p. 4549-4558.PubMedCrossRef
Metadata
Title
Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response
Authors
Amir A. Borhani
Roberta Catania
Yuri S. Velichko
Stefanie Hectors
Bachir Taouli
Sara Lewis
Publication date
01-08-2021
Publisher
Springer US
Published in
Abdominal Radiology / Issue 8/2021
Print ISSN: 2366-004X
Electronic ISSN: 2366-0058
DOI
https://doi.org/10.1007/s00261-021-03085-w

Other articles of this Issue 8/2021

Abdominal Radiology 8/2021 Go to the issue

Hepatobiliary

A radiomic-based model of different contrast-enhanced CT phase for differentiate intrahepatic cholangiocarcinoma from inflammatory mass with hepatolithiasis

Pelvis

Value of bowel preparation techniques for prostate MRI: a preliminary study

Special Section : HCC Treatment

Role of the radiologist at HCC multidisciplinary conference and use of the LR-TR algorithm for improving workflow

Hepatobiliary

Radiomics-based model using gadoxetic acid disodium-enhanced MR images: associations with recurrence-free survival of patients with hepatocellular carcinoma treated by surgical resection

Hepatobiliary

Comparison of unenhanced and contrast-enhanced 3 T magnetic resonance portovenography in children with extra hepatic portal venous obstruction

Pelvis

The absolute tumor-capsule contact length in the diagnosis of extraprostatic extension of prostate cancer

ACC 2024 Congress Coverage

Heart Failure Video

Year in Review: Heart failure and cardiomyopathies

Watch now

Watch this official video from ACC.24. Dr. Biykem Bozkurt discuss last year's major advances in heart failure and cardiomyopathies.

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

16-04-2024 Type 2 Diabetes News

Incentivised to exercise

11-04-2024 Lifestyle Modifications News

New intervention for STEMI-related cardiogenic shock

10-04-2024 Myocardial Infarction News

» View more highlights on the ACC 2024 congress hub page

Image Credits