Skip to main content

State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations

Abdominal Radiology volume 45, pages 342–353 (2020)Cite this article

Abstract

Radiomics is a new field in medical imaging with the potential of changing medical practice. Radiomics is characterized by the extraction of several quantitative imaging features which are not visible to the naked eye from conventional imaging modalities, and its correlation with specific relevant clinical endpoints, such as pathology, therapeutic response, and survival. Several studies have evaluated the use of radiomics in patients with hepatocellular carcinoma (HCC) with encouraging results, particularly in the pretreatment prediction of tumor biological characteristics, risk of recurrence, and survival. In spite of this, there are limitations and challenges to be overcome before the implementation of radiomics into clinical routine. In this article, we will review the concepts of radiomics and their current potential applications in patients with HCC. It is important that the multidisciplinary team involved in the treatment of patients with HCC be aware of the basic principles, benefits, and limitations of radiomics in order to achieve a balanced interpretation of the results toward a personalized medicine.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3

References

  1. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359-86.

    CAS  PubMed  Google Scholar 

  2. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108.

    PubMed  Google Scholar 

  3. Llovet JM, Bustamante J, Castells A, Vilana R, Ayuso MeC, Sala M, et al. Natural history of untreated nonsurgical hepatocellular carcinoma: rationale for the design and evaluation of therapeutic trials. Hepatology. 1999;29(1):62-7.

    CAS  PubMed  Google Scholar 

  4. Mittal S, El-Serag HB. Epidemiology of hepatocellular carcinoma: consider the population. J Clin Gastroenterol. 2013;47 Suppl:S2-6.

    PubMed  PubMed Central  Google Scholar 

  5. Venook AP, Papandreou C, Furuse J, de Guevara LL. The incidence and epidemiology of hepatocellular carcinoma: a global and regional perspective. Oncologist. 2010;15 Suppl 4:5-13.

    PubMed  Google Scholar 

  6. Cruite I, Tang A, Sirlin CB. Imaging-based diagnostic systems for hepatocellular carcinoma. AJR Am J Roentgenol. 2013;201(1):41-55.

    PubMed  Google Scholar 

  7. Horvat N, Monti S, Oliveira BC, Rocha CCT, Giancipoli RG, Mannelli L. State of the art in magnetic resonance imaging of hepatocellular carcinoma. Radiol Oncol. 2018;52(4):353-64.

    PubMed  PubMed Central  Google Scholar 

  8. Horvat N, Nikolovski I, Long N, Gerst S, Zheng J, Pak LM, et al. Imaging features of hepatocellular carcinoma compared to intrahepatic cholangiocarcinoma and combined tumor on MRI using liver imaging and data system (LI-RADS) version 2014. Abdom Radiol (NY). 2018;43(1):169-78.

    PubMed  PubMed Central  Google Scholar 

  9. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563-77.

    PubMed  Google Scholar 

  10. Horvat N, Bates DDB, Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. Abdom Radiol (NY). 2019.

  11. Zheng BH, Liu LZ, Zhang ZZ, Shi JY, Dong LQ, Tian LY, et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer. 2018;18(1):1148.

    PubMed  PubMed Central  Google Scholar 

  12. Zhou Y, He L, Huang Y, Chen S, Wu P, Ye W, et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY). 2017;42(6):1695-704.

    PubMed  Google Scholar 

  13. Zhou W, Zhang L, Wang K, Chen S, Wang G, Liu Z, et al. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging. 2017;45(5):1476-84.

    PubMed  Google Scholar 

  14. Wu M, Tan H, Gao F, Hai J, Ning P, Chen J, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol. 2018.

  15. Peng J, Zhang J, Zhang Q, Xu Y, Zhou J, Liu L. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol. 2018;24(3):121-7.

    PubMed  PubMed Central  Google Scholar 

  16. Perrin T, Midya A, Yamashita R, Chakraborty J, Saidon T, Jarnagin WR, et al. Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging. Abdom Radiol (NY). 2018;43(12):3271-8.

    PubMed  PubMed Central  Google Scholar 

  17. Cai W, He B, Hu M, Zhang W, Xiao D, Yu H, et al. A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma. Surg Oncol. 2019;28:78-85.

    PubMed  Google Scholar 

  18. Zheng J, Chakraborty J, Chapman WC, Gerst S, Gonen M, Pak LM, et al. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma Using Quantitative Image Analysis. J Am Coll Surg. 2017;225(6):778-88.e1.

    PubMed  PubMed Central  Google Scholar 

  19. Chen S, Feng S, Wei J, Liu F, Li B, Li X, 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.

  20. Kim J, Choi SJ, Lee SH, Lee HY, Park H. 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):1026-34.

    PubMed  Google Scholar 

  21. Kim S, Shin J, Kim DY, Choi GH, Kim MJ, Choi JY. Radiomics on Gadoxetic Acid-Enhanced Magnetic Resonance Imaging for Prediction of Postoperative Early and Late Recurrence of Single Hepatocellular Carcinoma. Clin Cancer Res. 2019.

  22. Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019.

  23. Bakr S, Echegaray S, Shah R, Kamaya A, Louie J, Napel 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):041303.

    Google Scholar 

  24. Feng ST, Jia Y, Liao B, Huang B, Zhou Q, Li X, et al. Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol. 2019.

  25. Kiryu S, Akai H, Nojima M, Hasegawa K, Shinkawa H, Kokudo N, et al. Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator. Sci Rep. 2017;7(1):12689.

    PubMed  PubMed Central  Google Scholar 

  26. Akai H, Yasaka K, Kunimatsu A, Nojima M, Kokudo T, Kokudo N, et al. Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagn Interv Imaging. 2018;99(10):643-51.

    CAS  PubMed  Google Scholar 

  27. Hui TCH, Chuah TK, Low HM, Tan CH. Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study. Clin Radiol. 2018;73(12):1056.e11-.e16.

    PubMed  Google Scholar 

  28. Shan QY, Hu HT, Feng ST, Peng ZP, Chen SL, Zhou Q, 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):11.

    PubMed  PubMed Central  Google Scholar 

  29. Wu J, Liu A, Cui J, Chen A, Song Q, Xie L. Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images. BMC Med Imaging. 2019;19(1):23.

    PubMed  PubMed Central  Google Scholar 

  30. Guo D, Gu D, Wang H, Wei J, Wang Z, Hao X, et al. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. Eur J Radiol. 2019;117:33-40.

    PubMed  Google Scholar 

  31. Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol. 2017;90(1070):20160665.

    PubMed  PubMed Central  Google Scholar 

  32. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics: a review publication of the Radiological Society of North America, Inc. 2017;37(5):1483-503.

    PubMed  Google Scholar 

  33. Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, et al. Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung cancer (Amsterdam, Netherlands). 2018;115:34-41.

    Google Scholar 

  34. Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36.

    PubMed  PubMed Central  Google Scholar 

  35. Horvat N, Bates DDB, Petkovska IJAR. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. 2019.

    PubMed  Google Scholar 

  36. Bashir U, Siddique MM, McLean E, Goh V, Cook GJ. Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges. AJR American journal of roentgenology. 2016;207(3):534-43.

    PubMed  Google Scholar 

  37. Kolossváry M, Karády J, Szilveszter B, Kitslaar P, Hoffmann U, Merkely B, et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign. Circ Cardiovasc Imaging. 2017;10(12).

  38. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ. Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep. 2015;5:13087.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017;77(21):e104-e7.

    PubMed  PubMed Central  Google Scholar 

  40. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts H. Machine Learning methods for Quantitative Radiomic Biomarkers. Scientific reports. 2015;5:13087.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321-57.

    Google Scholar 

  42. Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A. 2015;112(46):E6265-73.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Lim KC, Chow PK, Allen JC, Chia GS, Lim M, Cheow PC, 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):108-13.

    PubMed  Google Scholar 

  44. Mazzaferro V, Llovet JM, Miceli R, Bhoori S, Schiavo M, Mariani L, 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):35-43.

    PubMed  Google Scholar 

  45. Imamura H, Matsuyama Y, Tanaka E, Ohkubo T, Hasegawa K, Miyagawa S, et al. Risk factors contributing to early and late phase intrahepatic recurrence of hepatocellular carcinoma after hepatectomy. J Hepatol. 2003;38(2):200-7.

    PubMed  Google Scholar 

  46. Rodríguez-Perálvarez M, Luong TV, Andreana L, Meyer T, Dhillon AP, Burroughs AK. A systematic review of microvascular invasion in hepatocellular carcinoma: diagnostic and prognostic variability. Ann Surg Oncol. 2013;20(1):325-39.

    PubMed  Google Scholar 

  47. Torzilli G, Makuuchi M, Inoue K, Takayama T, Sakamoto Y, Sugawara Y, et al. No-mortality liver resection for hepatocellular carcinoma in cirrhotic and noncirrhotic patients: is there a way? A prospective analysis of our approach. Arch Surg. 1999;134(9):984-92.

    CAS  PubMed  Google Scholar 

  48. Fan ST, Lo CM, Liu CL, Lam CM, Yuen WK, Yeung C, et al. Hepatectomy for hepatocellular carcinoma: toward zero hospital deaths. Ann Surg. 1999;229(3):322-30.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Hasegawa K, Kokudo N, Imamura H, Matsuyama Y, Aoki T, Minagawa M, et al. Prognostic impact of anatomic resection for hepatocellular carcinoma. Ann Surg. 2005;242(2):252-9.

    PubMed  PubMed Central  Google Scholar 

  50. Blanc-Durand P, Van Der Gucht A, Jreige M, Nicod-Lalonde M, Silva-Monteiro M, Prior JO, et al. Signature of survival: a. Oncotarget. 2018;9(4):4549-58.

    PubMed  Google Scholar 

  51. Park HJ, Kim JH, Choi SY, Lee ES, Park SJ, Byun JY, 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):W211-W20.

    PubMed  Google Scholar 

  52. El-Khoueiry AB, Sangro B, Yau T, Crocenzi TS, Kudo M, Hsu C, et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet. 2017;389(10088):2492-502.

    CAS  PubMed  Google Scholar 

  53. Sangro B, Gomez-Martin C, de la Mata M, Iñarrairaegui M, Garralda E, Barrera P, et al. A clinical trial of CTLA-4 blockade with tremelimumab in patients with hepatocellular carcinoma and chronic hepatitis C. J Hepatol. 2013;59(1):81-8.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to express their deepest gratitude to Dr. Richard Kinh Gian Do, MD, PhD radiologist at Memorial Sloan Kettering Cancer Center for his support on this manuscript.

Funding

No funding was received.

Author information

Affiliations

  1. Department of Radiology, University of São Paulo, São Paulo, Brazil

    Joao Manoel Miranda Magalhaes Santos, Camila Carlos Tavares Rocha, Joao Vicente Horvat, Marcos Roberto Menezes & Natally Horvat

  2. Department of Radiology, Hospital Sírio-Libanês, Adma Jafet, 91, Bela Vista, São Paulo, SP, 01308-050, Brazil

    Brunna Clemente Oliveira, Jose de Arimateia Batista Araujo-Filho, Joao Vicente Horvat, Marcos Roberto Menezes & Natally Horvat

  3. Department of Radiology, Hospital Samaritano, São Paulo, Brazil

    Brunna Clemente Oliveira

  4. Research and Education Institute, Hospital Sírio-Libanês, São Paulo, Brazil

    Antonildes N. Assuncao-Jr

  5. Polytechnic School, University of São Paulo, São Paulo, Brazil

    Felipe Augusto de M. Machado

Authors
  1. Joao Manoel Miranda Magalhaes Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brunna Clemente Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose de Arimateia Batista Araujo-Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonildes N. Assuncao-Jr
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Felipe Augusto de M. Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Camila Carlos Tavares Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joao Vicente Horvat
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Marcos Roberto Menezes
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Natally Horvat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natally Horvat.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Miranda Magalhaes Santos, J.M., Clemente Oliveira, B., Araujo-Filho, J.A.B. et al. State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations. Abdom Radiol 45, 342–353 (2020). https://doi.org/10.1007/s00261-019-02299-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-019-02299-3

Keywords

  • Hepatocellular carcinoma
  • Radiomics
  • Textural analysis
  • Liver neoplasms
  • Magnetic resonance imaging
  • Computed tomography
  • Positron emission tomography