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MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

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.

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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. 1.

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

  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. 3.

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

    CAS  Article  Google Scholar 

  4. 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

    CAS  Article  Google Scholar 

  5. 5.

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

    CAS  Article  Google Scholar 

  6. 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. 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. 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

    CAS  Article  Google Scholar 

  9. 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. 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. 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. 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. 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. 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. 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. 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. 17.

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

  18. 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

    CAS  Article  Google Scholar 

  19. 19.

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

    CAS  Article  Google Scholar 

  20. 20.

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

    CAS  Article  Google Scholar 

  21. 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

    CAS  Article  Google Scholar 

  22. 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. 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

    CAS  Article  Google Scholar 

  24. 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

    CAS  Article  Google Scholar 

  25. 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. 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

    CAS  Article  Google Scholar 

  27. 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

    CAS  Article  Google Scholar 

  28. 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

    CAS  Article  Google Scholar 

  29. 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

    CAS  Article  Google Scholar 

  30. 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

    CAS  Article  Google Scholar 

  31. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 

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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.

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Correspondence to Bachir Taouli.

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The scientific guarantor of this publication is Bachir Taouli.

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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.

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No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

• observational

• performed at one institution

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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

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Keywords

  • Hepatocellular carcinoma
  • Magnetic resonance imaging
  • Correlation of data
  • Genomics
  • Immunophenotyping