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Can MRI features predict clinically relevant hepatocellular carcinoma genetic subtypes?

  • Hepatobiliary
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Abstract

Purpose

Recent studies in cancer genomics have revealed core drivers for hepatocellular carcinoma (HCC) pathogenesis. We aim to study whether MRI features can serve as non-invasive markers for the prediction of common genetic subtypes of HCC.

Methods

Sequencing of 447 cancer-implicated genes was performed on 43 pathology proven HCC from 42 patients, who underwent contrast-enhanced MRI followed by biopsy or resection. MRI features were retrospectively evaluated including tumor size, infiltrative tumor margin, diffusion restriction, arterial phase hyperenhancement, non-peripheral washout, enhancing capsule, peritumoral enhancement, tumor in vein, fat in mass, blood products in mass, cirrhosis and tumor heterogeneity. Fisher’s exact test was used to correlate genetic subtypes with imaging features. Prediction performance using correlated MRI features for genetic subtype and inter-reader agreement were assessed.

Results

The two most prevalent genetic mutations were TP53 (13/43, 30%) and CTNNB1 (17/43, 40%). Tumors with TP53 mutation more often demonstrated an infiltrative tumor margin on MRI (p = 0.01); inter-reader agreement was almost perfect (kappa = 0.95). The CTNNB1 mutation was associated with peritumoral enhancement on MRI (p = 0.04), inter-reader agreement was substantial (kappa = 0.74). The MRI feature of an infiltrative tumor margin correlated with the TP53 mutation with accuracy, sensitivity, and specificity of 74.4%, 61.5% and 80.0%, respectively. Peritumoral enhancement correlated with the CTNNB1 mutation with accuracy, sensitivity, and specificity of 69.8%, 47.0% and 84.6%, respectively.

Conclusion

An infiltrative tumor margin on MRI correlated with TP53 mutation and peritumoral enhancement correlated with CTNNB1 mutation in HCC. Absence of these MRI features are potential negative predictors of the respective HCC genetic subtypes that have implications for prognosis and treatment response.

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References

  1. H. Sung et al., "Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries," CA: A Cancer Journal for Clinicians, vol. 71, no. 3, pp. 209-249, 2021, https://doi.org/10.3322/caac.21660.

    Article  PubMed  Google Scholar 

  2. R. L. Siegel, K. D. Miller, H. E. Fuchs, and A. Jemal, "Cancer Statistics, 2021," CA: A Cancer Journal for Clinicians, vol. 71, no. 1, pp. 7-33, 2021, https://doi.org/10.3322/caac.21654.

    Article  PubMed  Google Scholar 

  3. R. Dhanasekaran, J. C. Nault, L. R. Roberts, and J. Zucman-Rossi, "Genomic Medicine and Implications for Hepatocellular Carcinoma Prevention and Therapy," Gastroenterology, vol. 156, no. 2, pp. 492-509, Jan 2019, https://doi.org/10.1053/j.gastro.2018.11.001.

    Article  CAS  PubMed  Google Scholar 

  4. X. Ding et al., "Genomic and Epigenomic Features of Primary and Recurrent Hepatocellular Carcinomas," Gastroenterology, vol. 157, no. 6, pp. 1630–1645 e6, Dec 2019, https://doi.org/10.1053/j.gastro.2019.09.005.

  5. w. b. e. Cancer Genome Atlas Research Network. Electronic address and N. Cancer Genome Atlas Research, "Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma," Cell, vol. 169, no. 7, pp. 1327–1341 e23, Jun 15 2017, https://doi.org/10.1016/j.cell.2017.05.046.

  6. V. Kancherla et al., "Genomic Analysis Revealed New Oncogenic Signatures in TP53-Mutant Hepatocellular Carcinoma," Front Genet, vol. 9, p. 2, 2018, https://doi.org/10.3389/fgene.2018.00002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. J. Calderaro, M. Ziol, V. Paradis, and J. Zucman-Rossi, "Molecular and histological correlations in liver cancer," J Hepatol, vol. 71, no. 3, pp. 616-630, Sep 2019, https://doi.org/10.1016/j.jhep.2019.06.001.

    Article  CAS  PubMed  Google Scholar 

  8. G. Zhu et al., "Mutant p53 in Cancer Progression and Targeted Therapies," (in English), Frontiers in Oncology, Review vol. 10, 2020-November-06 2020, https://doi.org/10.3389/fonc.2020.595187.

  9. P. Zhan, Y. N. Ji, and L. K. Yu, "TP53 mutation is associated with a poor outcome for patients with hepatocellular carcinoma: evidence from a meta-analysis," (in eng), Hepatobiliary Surg Nutr, vol. 2, no. 5, pp. 260-5, Oct 2013, https://doi.org/10.3978/j.issn.2304-3881.2013.07.06.

    Article  PubMed  PubMed Central  Google Scholar 

  10. D. Sia et al., "Identification of an Immune-specific Class of Hepatocellular Carcinoma, Based on Molecular Features," (in eng), Gastroenterology, vol. 153, no. 3, pp. 812-826, Sep 2017, https://doi.org/10.1053/j.gastro.2017.06.007.

    Article  CAS  PubMed  Google Scholar 

  11. X. Xiao, H. Mo, and K. Tu, "CTNNB1 mutation suppresses infiltration of immune cells in hepatocellular carcinoma through miRNA-mediated regulation of chemokine expression," (in eng), Int Immunopharmacol, vol. 89, no. Pt A, p. 107043, Dec 2020, doi: https://doi.org/10.1016/j.intimp.2020.107043.

  12. R. Pinyol, D. Sia, and J. M. Llovet, "Immune Exclusion-Wnt/CTNNB1 Class Predicts Resistance to Immunotherapies in HCC," Clin Cancer Res, vol. 25, no. 7, pp. 2021-2023, Apr 1 2019, https://doi.org/10.1158/1078-0432.CCR-18-3778.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Z. Wang et al., "beta-catenin mutation is correlated with a favorable prognosis in patients with hepatocellular carcinoma," Mol Clin Oncol, vol. 3, no. 4, pp. 936-940, Jul 2015, https://doi.org/10.3892/mco.2015.569.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. S. Lee et al., "CT and MRI Liver Imaging Reporting and Data System Version 2018 for Hepatocellular Carcinoma: A Systematic Review With Meta-Analysis," J Am Coll Radiol, vol. 17, no. 10, pp. 1199-1206, Oct 2020, https://doi.org/10.1016/j.jacr.2020.06.005.

    Article  PubMed  Google Scholar 

  15. V. Chernyak et al., "Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients," Radiology, vol. 289, no. 3, pp. 816-830, Dec 2018, https://doi.org/10.1148/radiol.2018181494.

    Article  PubMed  Google Scholar 

  16. M. Cerny et al., "LI-RADS for MR Imaging Diagnosis of Hepatocellular Carcinoma: Performance of Major and Ancillary Features," Radiology, vol. 288, no. 1, pp. 118-128, Jul 2018, https://doi.org/10.1148/radiol.2018171678.

    Article  PubMed  Google Scholar 

  17. K. M. Elsayes et al., "LI-RADS: a conceptual and historical review from its beginning to its recent integration into AASLD clinical practice guidance," J Hepatocell Carcinoma, vol. 6, pp. 49-69, 2019, https://doi.org/10.2147/JHC.S186239.

    Article  PubMed  PubMed Central  Google Scholar 

  18. F. P. Russo, A. Imondi, E. N. Lynch, and F. Farinati, "When and how should we perform a biopsy for HCC in patients with liver cirrhosis in 2018? A review," (in eng), Dig Liver Dis, vol. 50, no. 7, pp. 640-646, Jul 2018, https://doi.org/10.1016/j.dld.2018.03.014.

    Article  PubMed  Google Scholar 

  19. L. D. Tommaso et al., "Role of liver biops in hepatocellular carcinoma," World J Gastroenterol, vol. 25, no. 40, pp. 6041-6052, 2019.

    Article  PubMed  PubMed Central  Google Scholar 

  20. S. Y. Yim and J. S. Lee, "An Overview of the Genomic Characterization of Hepatocellular Carcinoma," J Hepatocell Carcinoma, vol. 8, pp. 1077-1088, 2021, https://doi.org/10.2147/JHC.S270533.

    Article  PubMed  PubMed Central  Google Scholar 

  21. M. Cerny et al., "LI-RADS Version 2018 Ancillary Features at MRI," Radiographics, vol. 38, no. 7, pp. 1973-2001, Nov-Dec 2018, https://doi.org/10.1148/rg.2018180052.

    Article  PubMed  Google Scholar 

  22. A. C. o. Radiology. CT/MRI LI-RADS v2018 Core

  23. H. Montemarano, Lonergan, G.J., Bulas, D.I., Selby, D.M., "Pancreatoblastoma: imaging findings in 10 patients and review of the literature," Radiology, vol. 214, pp. 476–482, 2000.

  24. Y. Kawamura et al., "Heterogeneous Type 4 Enhancement of Hepatocellular Carcinoma on Dynamic CT Is Associated With Tumor Recurrence After Radiofrequency Ablation," American Journal of Roentgenology, vol. 197, no. 4, pp. W665-W673, 2011/10/01 2011, https://doi.org/10.2214/AJR.11.6843.

  25. J. Wei et al., "Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study," Cancers (Basel), vol. 13, no. 10, May 14 2021, https://doi.org/10.3390/cancers13102368.

  26. N. Horvat, S. Monti, B. C. Oliveira, C. C. T. Rocha, R. G. Giancipoli, and L. Mannelli, "State of the art in magnetic resonance imaging of hepatocellular carcinoma," Radiol Oncol, vol. 52, no. 4, pp. 353-364, Nov 26 2018, https://doi.org/10.2478/raon-2018-0044.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Q. Wang et al., "CT and MRI imaging and interpretation of hepatic arterioportal shunts," Transl Gastroenterol Hepatol, vol. 4, p. 34, 2019, https://doi.org/10.21037/tgh.2019.05.05.

    Article  PubMed  PubMed Central  Google Scholar 

  28. M. L. McHugh, "Interrater reliability: the kappa statistic," (in eng), Biochem Med (Zagreb), vol. 22, no. 3, pp. 276-82, 2012.

    Article  PubMed  Google Scholar 

  29. X. Meng, D. A. Franklin, J. Dong, and Y. Zhang, "MDM2-p53 pathway in hepatocellular carcinoma," Cancer Res, vol. 74, no. 24, pp. 7161-7, Dec 15 2014, https://doi.org/10.1158/0008-5472.CAN-14-1446.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. R. Chen, M. Zhao, Y. An, D. Liu, Q. Tang, and G. Teng, "A Prognostic Gene Signature for Hepatocellular Carcinoma," Front Oncol, vol. 12, p. 841530, 2022, https://doi.org/10.3389/fonc.2022.841530.

    Article  PubMed  PubMed Central  Google Scholar 

  31. V. Cuccurullo, G. D. Di Stasio, G. Mazzarella, and G. L. Cascini, "Microvascular Invasion in HCC: The Molecular Imaging Perspective," Contrast Media Mol Imaging, vol. 2018, p. 9487938, 2018, https://doi.org/10.1155/2018/9487938.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. M. Li et al., "Corona Enhancement and Mosaic Architecture for Prognosis and Selection Between of Liver Resection Versus Transcatheter Arterial Chemoembolization in Single Hepatocellular Carcinomas >5 cm Without Extrahepatic Metastases: An Imaging-Based Retrospective Study," Medicine (Baltimore), vol. 95, no. 2, p. e2458, Jan 2016, https://doi.org/10.1097/MD.0000000000002458.

    Article  CAS  PubMed  Google Scholar 

  33. H. Kim et al., "Can microvessel invasion of hepatocellular carcinoma be predicted by pre-operative MRI?," European Radiology, vol. 19, no. 7, pp. 1744–1751, 2009/07/01 2009, https://doi.org/10.1007/s00330-009-1331-8.

  34. A. Nishie et al., "Radiological detectability of minute hepatic venous invasion in hepatocellular carcinoma," (in eng), Eur J Radiol, vol. 70, no. 3, pp. 517-24, Jun 2009, https://doi.org/10.1016/j.ejrad.2008.02.021.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors acknowledge the DFCI Oncology Data Retrieval System (OncDRS) for the aggregation, management, and delivery of the clinical and operational research data used in this project. The content is solely the responsibility of the authors. The authors would like to thank Andy Shi, PhD for statistical advice.

Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Xiaoyang Liu, Yang Guo & Paul B. Shyn

  2. Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, Toronto, ON, Canada

    Xiaoyang Liu

  3. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Lei Zhao

  4. Department of Pathology, Yale New Haven Hospital, Yale Medical School, New Haven, CT, USA

    Joseph Misdraji

  5. Harvard Medical School, Boston, MA, USA

    Tina Kapur

  6. Department of Medicine, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Thomas A. Abrams

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Correspondence to Xiaoyang Liu.

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Liu, X., Guo, Y., Zhao, L. et al. Can MRI features predict clinically relevant hepatocellular carcinoma genetic subtypes?. Abdom Radiol 48, 1955–1964 (2023). https://doi.org/10.1007/s00261-023-03876-3

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