Skip to main content
SpringerLink
Log in

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To assess the value of qualitative and quantitative MRI radiomics features for noninvasive prediction of immuno-oncologic characteristics and outcomes of hepatocellular carcinoma (HCC).

Methods

This retrospective, IRB-approved study included 48 patients with HCC (M/F 35/13, mean age 60y) who underwent hepatic resection or transplant within 4 months of abdominal MRI. Qualitative imaging traits, quantitative nontexture related and texture features were assessed in index lesions on contrast-enhanced T1-weighted and diffusion-weighted images. The association of imaging features with immunoprofiling and genomics features was assessed using binary logistic regression and correlation analyses. Binary logistic regression analysis was also employed to analyse the association of radiomics, histopathologic and genomics features with radiological early recurrence of HCC at 12 months.

Results

Qualitative (r = − 0.41–0.40, p < 0.042) and quantitative (r = − 0.52–0.45, p < 0.049) radiomics features correlated with immunohistochemical cell type markers for T-cells (CD3), macrophages (CD68) and endothelial cells (CD31). Radiomics features also correlated with expression of immunotherapy targets PD-L1 at protein level (r = 0.41–0.47, p < 0.029) as well as PD1 and CTLA4 at mRNA expression level (r = − 0.48–0.47, p < 0.037). Finally, radiomics features, including tumour size, showed significant diagnostic performance for assessment of early HCC recurrence (AUC 0.76–0.80, p < 0.043), while immunoprofiling and genomic features did not (p = 0.098–0929).

Conclusions

MRI radiomics features may serve as noninvasive predictors of HCC immuno-oncological characteristics and tumour recurrence and may aid in treatment stratification of HCC patients. These results need prospective validation.

Key Points

• MRI radiomics features showed significant associations with immunophenotyping and genomics characteristics of hepatocellular carcinoma.

• Radiomics features, including tumour size, showed significant associations with early hepatocellular carcinoma recurrence after resection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CE-MRI:

Contrast-enhanced MRI

CT:

Computed tomography

DWI:

Diffusion-weighted imaging

FDR:

False discovery rate

HCC:

Hepatocellular carcinoma

MICSSS:

Multiplexed immunohistochemical consecutive staining on single slide

MRI:

Magnetic resonance imaging

OR:

Odds ratio

References

  1. Forner A, Reig M, Bruix J (2018) Hepatocellular carcinoma. Lancet. https://doi.org/10.1016/S0140-6736(18)30010-2

  2. Marrero JA, Kulik LM, Sirlin C et al (2018) Diagnosis, staging and management of hepatocellular carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. https://doi.org/10.1002/hep.29913

  3. Llovet JM, Ricci S, Mazzaferro V et al (2008) Sorafenib in advanced hepatocellular carcinoma. N Engl J Med 359:378–390

    Article  CAS  Google Scholar 

  4. El-Khoueiry AB, Sangro B, Yau T et al (2017) Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet 389:2492–2502

    Article  CAS  Google Scholar 

  5. Chen DS, Mellman I (2017) Elements of cancer immunity and the cancer-immune set point. Nature 541:321–330

    Article  CAS  Google Scholar 

  6. Gnjatic S, Bronte V, Brunet LR et al (2017) Identifying baseline immune-related biomarkers to predict clinical outcome of immunotherapy. J Immunother Cancer 5:44

    Article  Google Scholar 

  7. Remark R, Merghoub T, Grabe N et al (2016) In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide. Sci Immunol 1:aaf6925

    Article  Google Scholar 

  8. Hoshida Y, Toffanin S, Lachenmayer A, Villanueva A, Minguez B, Llovet JM (2010) Molecular classification and novel targets in hepatocellular carcinoma: recent advancements. Semin Liver Dis 30:35–51

    Article  CAS  Google Scholar 

  9. Khemlina G, Ikeda S, Kurzrock R (2017) The biology of hepatocellular carcinoma: implications for genomic and immune therapies. Mol Cancer 16:149

    Article  Google Scholar 

  10. Clark T, Maximin S, Meier J, Pokharel S, Bhargava P (2015) Hepatocellular carcinoma: review of epidemiology, screening, imaging diagnosis, response assessment, and treatment. Curr Probl Diagn Radiol 44:479–486

    Article  Google Scholar 

  11. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  12. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  13. Lewis S, Hectors S, Taouli B (2020) Radiomics of hepatocellular carcinoma. Abdom Radiol (NY). https://doi.org/10.1007/s00261-019-02378-5

  14. Hectors SJ, Wagner M, Bane O et al (2017) Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging. Sci Rep 7:2452

    Article  Google Scholar 

  15. Taouli B, Hoshida Y, Kakite S et al (2017) Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results. Eur Radiol 27:4472–4481

    Article  Google Scholar 

  16. American College of Radiology (ACR) (2018) CT/MRI LI-RADS® v2018. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/LI-RADS/CT-MRI-LI-RADS-v2018

  17. Haralick RM, Shanmungam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC 3:610–621

  18. Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22:81–91

    Article  CAS  Google Scholar 

  19. Edmondson HA, Steiner PE (1954) Primary carcinoma of the liver: a study of 100 cases among 48,900 necropsies. Cancer 7:462–503

    Article  CAS  Google Scholar 

  20. Tan PS, Nakagawa S, Goossens N et al (2016) Clinicopathological indices to predict hepatocellular carcinoma molecular classification. Liver Int 36:108–118

    Article  CAS  Google Scholar 

  21. Hoshida Y, Nijman SM, Kobayashi M et al (2009) Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res 69:7385–7392

    Article  CAS  Google Scholar 

  22. Goossens N, Sun X, Hoshida Y (2015) Molecular classification of hepatocellular carcinoma: potential therapeutic implications. Hepatol Oncol 2:371–379

    Article  Google Scholar 

  23. Villanueva A, Hoshida Y, Battiston C et al (2011) Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. Gastroenterology 140:1501–1512 e1502

    Article  CAS  Google Scholar 

  24. Yamashita T, Forgues M, Wang W et al (2008) EpCAM and alpha-fetoprotein expression defines novel prognostic subtypes of hepatocellular carcinoma. Cancer Res 68:1451–1461

    Article  CAS  Google Scholar 

  25. Di Tommaso L, Franchi G, Park YN et al (2007) Diagnostic value of HSP70, glypican 3, and glutamine synthetase in hepatocellular nodules in cirrhosis. Hepatology 45:725–734

    Article  Google Scholar 

  26. Llovet JM, Chen Y, Wurmbach E et al (2006) A molecular signature to discriminate dysplastic nodules from early hepatocellular carcinoma in HCV cirrhosis. Gastroenterology 131:1758–1767

    Article  CAS  Google Scholar 

  27. Hagel M, Miduturu C, Sheets M et al (2015) First selective small molecule inhibitor of FGFR4 for the treatment of hepatocellular carcinomas with an activated FGFR4 signaling pathway. Cancer Discov 5:424–437

    Article  CAS  Google Scholar 

  28. Horwitz E, Stein I, Andreozzi M et al (2014) Human and mouse VEGFA-amplified hepatocellular carcinomas are highly sensitive to sorafenib treatment. Cancer Discov 4:730–743

    Article  CAS  Google Scholar 

  29. Poon RT, Fan ST, Ng IO, Lo CM, Liu CL, Wong J (2000) Different risk factors and prognosis for early and late intrahepatic recurrence after resection of hepatocellular carcinoma. Cancer 89:500–507

    Article  CAS  Google Scholar 

  30. Kong LQ, Zhu XD, Xu HX et al (2013) The clinical significance of the CD163+ and CD68+ macrophages in patients with hepatocellular carcinoma. PLoS One 8:e59771

    Article  CAS  Google Scholar 

  31. Wang L, Wang FS (2019) Clinical immunology and immunotherapy for hepatocellular carcinoma: current progress and challenges. Hepatol Int. https://doi.org/10.1007/s12072-019-09967-y

  32. Zhou W, Zhang L, Wang K et al (2017) Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging 45:1476–1484

    Article  Google Scholar 

  33. Zimmerman MA, Ghobrial RM, Tong MJ et al (2008) Recurrence of hepatocellular carcinoma following liver transplantation: a review of preoperative and postoperative prognostic indicators. Arch Surg 143:182–188 discussion 188

    Article  Google Scholar 

  34. Renzulli M, Brocchi S, Cucchetti A et al (2016) Can current preoperative imaging be used to detect microvascular invasion of hepatocellular carcinoma? Radiology 279:432–442

    Article  Google Scholar 

  35. Peng J, Zhang J, Zhang Q, Xu Y, Zhou J, Liu L (2018) A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol 24:121–127

    Article  Google Scholar 

  36. Llovet JM, Schwartz M, Mazzaferro V (2005) Resection and liver transplantation for hepatocellular carcinoma. Semin Liver Dis 25:181–200

    Article  Google Scholar 

  37. Akateh C, Black SM, Conteh L et al (2019) Neoadjuvant and adjuvant treatment strategies for hepatocellular carcinoma. World J Gastroenterol 25:3704–3721

    Article  Google Scholar 

  38. Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of Radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158

    Article  Google Scholar 

  39. Chen S, Feng S, Wei J et al (2019) Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol 29:4177–4187

    Article  Google Scholar 

  40. Feng ST, Jia Y, Liao B et al (2019) Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol 29:4648–4659

    Article  Google Scholar 

Download references

Funding

This study has received funding from the Research Seed Grant no. RSD1608 from the Radiological Society of North America, and grant U01 CA172320 from the National Cancer Institute and the International Liver Cancer Association.

Author information

Author notes

  1. Yujin Hoshida and Bachir Taouli share senior authorship.

Authors and Affiliations

  1. Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA

    Stefanie J. Hectors, Sara Lewis, Cecilia Besa, Michael J. King, Daniela Said & Bachir Taouli

  2. Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA

    Stefanie J. Hectors, Sara Lewis, Michael J. King, Daniela Said & Bachir Taouli

  3. Department of Radiology, Weill Cornell Medicine, 515 E 71st Street, New York, NY, 10021, USA

    Stefanie J. Hectors

  4. Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Avenida Libertador Bernardo O’Higgins 340, 8331150, Santiago, Chile

    Cecilia Besa

  5. Department of Radiology, Universidad de los Andes, Avenida Plaza 2501, 7620157, Las Condes, Chile

    Daniela Said

  6. Department of Pathology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA

    Juan Putra, Stephen Ward & Swan Thung

  7. Liver Tumor Translational Research Program, Harold C. Simmons Comprehensive Cancer Center, Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA

    Takaaki Higashi & Yujin Hoshida

  8. Division of Hematology and Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA

    Shen Yao & Ilaria Laface

  9. Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA

    Myron Schwartz

  10. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA

    Sacha Gnjatic

  11. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA

    Miriam Merad

Authors
  1. Stefanie J. Hectors
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sara Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cecilia Besa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael J. King
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniela Said
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Juan Putra
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stephen Ward
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Takaaki Higashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Swan Thung
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shen Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ilaria Laface
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Myron Schwartz
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sacha Gnjatic
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Miriam Merad
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Yujin Hoshida
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Bachir Taouli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bachir Taouli.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Bachir Taouli.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• observational

• performed at one institution

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(DOCX 6958 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hectors, S.J., Lewis, S., Besa, C. et al. MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma. Eur Radiol 30, 3759–3769 (2020). https://doi.org/10.1007/s00330-020-06675-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-06675-2

Keywords

Search

Navigation