Abstract
The diagnosis of hepatocellular carcinoma relies largely on non-invasive imaging, and is well suited for radiomics analysis. Radiomics is an emerging method for quantification of tumor heterogeneity by mathematically analyzing the spatial distribution and relationships of gray levels in medical images. The published studies on radiomics analysis of HCC provide encouraging data demonstrating potential utility for prediction of tumor biology, molecular profiles, post-therapy response, and outcome. The combination of radiomics data and clinical/laboratory information provides added value in many studies. Radiomics is a multi-step process that requires optimization and standardization, the development of semi-automated or automated segmentation methods, robust data quality control, and refinement of algorithms and modeling approaches for high-throughput data analysis. While radiomics remains largely in the research setting, the strong associations of predictive models and nomograms with certain pathologic, molecular, and immune markers with tumor aggressiveness and patient outcomes, provide great potential for clinical applications to inform optimized treatment strategies and patient prognosis.
Similar content being viewed by others
References
Ryerson, A.B., et al., Annual Report to the Nation on the Status of Cancer, 1975-2012, featuring the increasing incidence of liver cancer. Cancer, 2016. 122(9): p. 1312-37.
Cartier, V. and C. Aube, Diagnosis of hepatocellular carcinoma. Diagn Interv Imaging, 2014. 95(7-8): p. 709-19.
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
Llovet, J.M., et al., Sorafenib in advanced hepatocellular carcinoma. N Engl J Med, 2008. 359(4): p. 378-90.
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.
Villanueva, A., et al., New strategies in hepatocellular carcinoma: genomic prognostic markers. Clin Cancer Res, 2010. 16(19): p. 4688-94.
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.
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.
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.
Yip, S.S. and H.J. Aerts, Applications and limitations of radiomics. Phys Med Biol, 2016. 61(13): p. R150-66.
Lambin, P., et al., Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017. 14(12): p. 749-762.
Nougaret, S., et al., Radiomics: an Introductory Guide to What It May Foretell. Curr Oncol Rep, 2019. 21(8): p. 70.
Zwanenburg, A., et al., Imaging biomarker standardisation initiative. arXiv, 2016. 1612.07003.
Shan, Q.Y., et al., CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging, 2019. 19(1): p. 11.
Ahn, S.J., et al., Hepatocellular carcinoma: preoperative gadoxetic acid-enhanced MR imaging can predict early recurrence after curative resection using image features and texture analysis. Abdom Radiol (NY), 2019. 44(2): p. 539-548.
Kim, S., et al., Radiomics on Gadoxetic Acid-Enhanced Magnetic Resonance Imaging for Prediction of Postoperative Early and Late Recurrence of Single Hepatocellular Carcinoma. Clin Cancer Res, 2019. 25(13): p. 3847-3855.
Yang, F., et al., Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol, 2018. 7(3): p. 445-458.
Just, N., Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer, 2014. 111(12): p. 2205-13.
Haralick RM, S.K., Dinstein I, Textural Features for Image Classification. IEEE Trans SystMan Cybern SMC, 1973. 3: p. 610-621.
Davnall, F., et al., Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging, 2012. 3(6): p. 573-89.
Parekh, V. and M.A. Jacobs, Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev, 2016. 1(2): p. 207-226.
Rizzo, S., et al., Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp, 2018. 2(1): p. 36.
Parmar, C., et al., Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep, 2015. 5: p. 13087.
Varghese, B.A., et al., Texture Analysis of Imaging: What Radiologists Need to Know. AJR Am J Roentgenol, 2019. 212(3): p. 520-528.
Dobbin, K.K. and R.M. Simon, Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genomics, 2011. 4: p. 31.
Fransvea, E., et al., HCC heterogeneity: molecular pathogenesis and clinical implications. Cell Oncol, 2009. 31(3): p. 227-33.
Friemel, J., et al., Intratumor heterogeneity in hepatocellular carcinoma. Clin Cancer Res, 2015. 21(8): p. 1951-61.
Lin, D.C., et al., Genomic and Epigenomic Heterogeneity of Hepatocellular Carcinoma. Cancer Res, 2017. 77(9): p. 2255-2265.
Zhu, S. and Y. Hoshida, Molecular heterogeneity in hepatocellular carcinoma. Hepat Oncol, 2018. 5(1): p. HEP10.
Goossens, N., X. Sun, and Y. Hoshida, Molecular classification of hepatocellular carcinoma: potential therapeutic implications. Hepat Oncol, 2015. 2(4): p. 371-379.
Hoshida, Y., et al., Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res, 2009. 69(18): p. 7385-92.
Sia, D., et al., Identification of an Immune-specific Class of Hepatocellular Carcinoma, Based on Molecular Features. Gastroenterology, 2017. 153(3): p. 812-826.
Liu, J., H. Dang, and X.W. Wang, The significance of intertumor and intratumor heterogeneity in liver cancer. Exp Mol Med, 2018. 50(1): p. e416.
Martins-Filho, S.N., et al., Histological Grading of Hepatocellular Carcinoma-A Systematic Review of Literature. Front Med (Lausanne), 2017. 4: p. 193.
Moriya, T., et al., 3D analysis of apparent diffusion coefficient histograms in hepatocellular carcinoma: correlation with histological grade. Cancer Imaging, 2017. 17(1): p. 1.
Xu, Y.S., et al., Whole-lesion histogram analysis metrics of the apparent diffusion coefficient: a correlation study with histological grade of hepatocellular carcinoma. Abdom Radiol (NY), 2019. 44(9): p. 3089-3098.
Wu, M., et al., Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol, 2019. 29(6): p. 2802-2811.
Zhou, W., et al., Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging, 2017. 45(5): p. 1476-1484.
Oh, J., et al., Hepatocellular Carcinoma: Texture Analysis of Preoperative Computed Tomography Images Can Provide Markers of Tumor Grade and Disease-Free Survival. Korean J Radiol, 2019. 20(4): p. 569-579.
Gouw, A.S., et al., Markers for microvascular invasion in hepatocellular carcinoma: where do we stand? Liver Transpl, 2011. 17 Suppl 2: p. S72-80.
Roayaie, S., et al., A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology, 2009. 137(3): p. 850-5.
Lim, K.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. Ann Surg, 2011. 254(1): p. 108-13.
Mazzaferro, V., et al., Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. Lancet Oncol, 2009. 10(1): p. 35-43.
Banerjee, S., et al., A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology, 2015. 62(3): p. 792-800.
Renzulli, M., et al., Can Current Preoperative Imaging Be Used to Detect Microvascular Invasion of Hepatocellular Carcinoma? Radiology, 2016. 279(2): p. 432-42.
Peng, J., et al., A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol, 2018. 24(3): p. 121-127.
Ma, X., et al., Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT. Eur Radiol, 2019. 29(7): p. 3595-3605.
Bakr, S., et al., Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study. J Med Imaging (Bellingham), 2017. 4(4): p. 041303.
Xu, X., et al., Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol, 2019. 70(6): p. 1133-1144.
Zheng, B.H., et al., Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer, 2018. 18(1): p. 1148.
Zhu, Y.J., 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. Oncol Lett, 2019. 18(1): p. 720-732.
Huang, Y.Q., et al., Value of MR histogram analyses for prediction of microvascular invasion of hepatocellular carcinoma. Medicine (Baltimore), 2016. 95(26): p. e4034.
Li, H., et al., Preoperative histogram analysis of intravoxel incoherent motion (IVIM) for predicting microvascular invasion in patients with single hepatocellular carcinoma. Eur J Radiol, 2018. 105: p. 65-71.
Hu, H.T., et al., Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol, 2019. 29(6): p. 2890-2901.
Luo, Y., et al., Clinicopathological and prognostic significance of high Ki-67 labeling index in hepatocellular carcinoma patients: a meta-analysis. Int J Clin Exp Med, 2015. 8(7): p. 10235-47.
Hu, X.X., et al., Whole-tumor MRI histogram analyses of hepatocellular carcinoma: Correlations with Ki-67 labeling index. J Magn Reson Imaging, 2017. 46(2): p. 383-392.
Li, Y., et al., Texture analysis of multi-phase MRI images to detect expression of Ki67 in hepatocellular carcinoma. Clin Radiol, 2019.
Kim, H., et al., Human hepatocellular carcinomas with “Stemness”-related marker expression: keratin 19 expression and a poor prognosis. Hepatology, 2011. 54(5): p. 1707-17.
Tsuchiya, K., et al., Expression of keratin 19 is related to high recurrence of hepatocellular carcinoma after radiofrequency ablation. Oncology, 2011. 80(3-4): p. 278-88.
Wang, H.Q., et al., Magnetic resonance texture analysis for the identification of cytokeratin 19-positive hepatocellular carcinoma. Eur J Radiol, 2019. 117: p. 164-170.
Yao, Z., et al., Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images. BMC Cancer, 2018. 18(1): p. 1089.
Cariani, E., et al., Immunological and molecular correlates of disease recurrence after liver resection for hepatocellular carcinoma. PLoS One, 2012. 7(3): p. e32493.
Segal, E., et al., Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol, 2007. 25(6): p. 675-80.
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.
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.
Gyorffy, B., et al., Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. Oncogene, 2005. 24(51): p. 7542-51.
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.
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.
Hectors, S.J., et al., Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging. Sci Rep, 2017. 7(1): p. 2452.
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.
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.
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.
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.
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.
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.
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.
Wu, L.F., et al., Pre-TACE kurtosis of ADCtotal derived from histogram analysis for diffusion-weighted imaging is the best independent predictor of prognosis in hepatocellular carcinoma. Eur Radiol, 2019. 29(1): p. 213-223.
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.
Reiner, C.S., et al., Histogram Analysis of CT Perfusion of Hepatocellular Carcinoma for Predicting Response to Transarterial Radioembolization: Value of Tumor Heterogeneity Assessment. Cardiovasc Intervent Radiol, 2016. 39(3): p. 400-8.
Riaz, A., et al., Radiologic-pathologic correlation of hepatocellular carcinoma treated with internal radiation using yttrium-90 microspheres. Hepatology, 2009. 49(4): p. 1185-93.
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.
Ma, X., et al., Histogram analysis of apparent diffusion coefficient predicts response to radiofrequency ablation in hepatocellular carcinoma. Chin J Cancer Res, 2019. 31(2): p. 366-374.
Yuan, C., et al., Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram. Cancer Imaging, 2019. 19(1): p. 21.
Cozzi, L., et al., Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer, 2017. 17(1): p. 829.
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.
Shah, S.A., et al., Recurrence after liver resection for hepatocellular carcinoma: risk factors, treatment, and outcomes. Surgery, 2007. 141(3): p. 330-9.
Guo, D., et al., Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. Eur J Radiol, 2019. 117: p. 33-40.
Hui, T.C.H., et al., Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study. Clin Radiol, 2018. 73(12): p. 1056 e11-1056 e16.
Brenet Defour, L., et al., Hepatocellular carcinoma: CT texture analysis as a predictor of survival after surgical resection. Eur Radiol, 2019. 29(3): p. 1231-1239.
Chen, S., et al., Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: A retrospective pilot study. Eur J Radiol, 2017. 90: p. 198-204.
Zhang, J., et al., Texture Analysis Based on Preoperative Magnetic Resonance Imaging (MRI) and Conventional MRI Features for Predicting the Early Recurrence of Single Hepatocellular Carcinoma after Hepatectomy. Acad Radiol, 2018.
Zhang, W., et al., Prognostic value of preoperative computed tomography in HBV-related hepatocellular carcinoma patients after curative resection. Onco Targets Ther, 2019. 12: p. 3791-3804.
Akai, H., et al., Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagn Interv Imaging, 2018. 99(10): p. 643-651.
Zhang, Z., et al., Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction. Cancer Imaging, 2019. 19(1): p. 22.
Zhou, Y., et al., CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY), 2017. 42(6): p. 1695-1704.
Braman, N.M., et al., Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res, 2017. 19(1): p. 57.
Prasanna, P., et al., Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur Radiol, 2017. 27(10): p. 4188-4197.
Shafiq-Ul-Hassan, M., et al., Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys, 2017. 44(3): p. 1050-1062.
Traverso, A., et al., Repeatability and Reproducibility of Radiomic Features: A Systematic Review. Int J Radiat Oncol Biol Phys, 2018. 102(4): p. 1143-1158.
Qiu, Q., et al., Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability. Quant Imaging Med Surg, 2019. 9(3): p. 453-464.
Sala, E., et al., Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol, 2017. 72(1): p. 3-10.
Parekh, V.S. and M.A. Jacobs, Deep learning and radiomics in precision medicine. Expert Rev Precis Med Drug Dev, 2019. 4(2): p. 59-72.
Paul, R., et al., Towards deep radiomics: nodule malignancy prediction using CNNs on feature images. Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosi, 2019.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lewis, S., Hectors, S. & Taouli, B. Radiomics of hepatocellular carcinoma. Abdom Radiol 46, 111–123 (2021). https://doi.org/10.1007/s00261-019-02378-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00261-019-02378-5