Skip to main content

Radiomics analysis of [18F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma

European Journal of Nuclear Medicine and Molecular Imaging volume 48, pages 2599–2614 (2021)Cite this article

Abstract

As a reliable preoperative predictor for microvascular invasion (MVI) and disease-free survival (DFS) is lacking, we developed a radiomics nomogram of [18F]FDG PET/CT to predict MVI status and DFS in patients with very-early- and early-stage (BCLC 0, BCLC A) hepatocellular carcinoma (HCC).

Methods

Patients (N = 80) with BCLC0-A HCC who underwent [18F]FDG PET/CT before surgery were enrolled in this retrospective study and were randomized to a training cohort and a validation cohort. Texture features from patients obtained using Lifex software in the training cohort were subjected to LASSO regression to select the most useful predictive features of MVI and DFS. Then, the radiomics nomogram was constructed using the radiomics signature and clinical features and further validated.

Results

To predict MVI, the [18F]FDG PET/CT radiomics signature consisted of five texture features from the PET and six texture features from CT. The signature was significantly associated with MVI status in the training cohort (P = 0.001). None of the clinical features was independent predictors for MVI status (P > 0.05). The area under the curve value of the M-PET/CT model was 0.891 (95% CI: 0.799–0.984) in the training cohort and showed good discrimination and calibration. To predict DFS, the [18F]FDG PET/CT radiomics nomogram (D-PET/CT model) and a clinicopathologic nomogram were built in the training cohort. The D-PET/CT model, which integrated the D-PET/CT radiomics signature with INR and TB, provided better predictive performance (C-index: 0.831, 95% CI: 0.761–0.900) and larger net benefits than the simple clinical model, as determined by decision curve analyses.

Conclusion

The newly developed [18F]FDG PET/CT radiomics signature was an independent biomarker for the estimation of MVI and DFS in patients with very-early- and early-stage HCC. Moreover, PET/CT nomogram, which incorporated the radiomics signature of [18F]FDG PET/CT and clinical risk factors in patients with very-early- and early-stage HCC, performed better for individualized DFS estimation, which might enable a step forward in precise 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
Fig. 4
Fig. 5
Fig. 6

Data availability

All data were transparent. The data used in the current study are available from the corresponding authors on reasonable request.

References

  1. Marrero JA, Welling T. Modern diagnosis and management of hepatocellular carcinoma. Clin Liver Dis. 2009;13:233–47. https://doi.org/10.1016/j.cld.2009.02.007.

    Article  PubMed  Google Scholar 

  2. Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet. 2018;391:1301–14. https://doi.org/10.1016/S0140-6736(18)30010-2.

    Article  PubMed  Google Scholar 

  3. Banerjee S, Wang DS, Kim HJ, Sirlin CB, Chan MG, Korn RL, et al. A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology. 2015;62:792–800. https://doi.org/10.1002/hep.27877.

    Article  PubMed  Google Scholar 

  4. Bruix J, Gores GJ, Mazzaferro V. Hepatocellular carcinoma: clinical frontiers and perspectives. Gut. 2014;63:844–55. https://doi.org/10.1136/gutjnl-2013-306627.

    CAS  Article  PubMed  Google Scholar 

  5. Lin DC, Mayakonda A, Dinh HQ, Huang P, Lin L, Liu X, et al. Genomic and epigenomic heterogeneity of hepatocellular carcinoma. Cancer Res. 2017;77:2255–65. https://doi.org/10.1158/0008-5472.CAN-16-2822.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Lu LC, Hsu CH, Hsu C, Cheng AL. Tumor heterogeneity in hepatocellular carcinoma: facing the challenges. Liver Cancer. 2016;5:128–38. https://doi.org/10.1159/000367754.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Miyata R, Tanimoto A, Wakabayashi G, Shimazu M, Nakatsuka S, Mukai M, et al. Accuracy of preoperative prediction of microinvasion of portal vein in hepatocellular carcinoma using superparamagnetic iron oxide-enhanced magnetic resonance imaging and computed tomography during hepatic angiography. J Gastroenterol. 2006;41:987–95. https://doi.org/10.1007/s00535-006-1890-2.

    Article  PubMed  Google Scholar 

  8. Adachi E, Maeda T, Kajiyama K, Kinukawa N, Matsumata T, Sugimachi K, et al. Factors correlated with portal venous invasion by hepatocellular carcinoma: univariate and multivariate analyses of 232 resected cases without preoperative treatments. Cancer. 1996;77:2022–31. https://doi.org/10.1002/(SICI)1097-0142(19960515)77:10<2022::AID-CNCR9>3.0.CO;2-S.

    CAS  Article  PubMed  Google Scholar 

  9. Marsh JW, Dvorchik I, Bonham CA, Iwatsuki S. Is the pathologic TNM staging system for patients with hepatoma predictive of outcome? Cancer. 2000;88:538–43. https://doi.org/10.1002/(sici)1097-0142(20000201)88:3<538::aid-cncr7>3.0.co;2-h.

    CAS  Article  PubMed  Google Scholar 

  10. Jonas S, Bechstein WO, Steinmuller T, Herrmann M, Radke C, Berg T, et al. Vascular invasion and histopathologic grading determine outcome after liver transplantation for hepatocellular carcinoma in cirrhosis. Hepatology. 2001;33:1080–6. https://doi.org/10.1053/jhep.2001.23561.

    CAS  Article  PubMed  Google Scholar 

  11. Renzulli M, Brocchi S, Cucchetti A, Mazzotti F, Mosconi C, Sportoletti C, et al. Can current preoperative imaging be used to detect microvascular invasion of hepatocellular carcinoma? Radiology. 2016;279:432–42. https://doi.org/10.1148/radiol.2015150998.

    Article  PubMed  Google Scholar 

  12. Hu HT, Wang Z, Huang XW, Chen SL, Zheng X, Ruan SM, et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol. 2019;29:2890–901. https://doi.org/10.1007/s00330-018-5797-0.

    Article  PubMed  Google Scholar 

  13. Chandarana H, Robinson E, Hajdu CH, Drozhinin L, Babb JS, Taouli B. Microvascular invasion in hepatocellular carcinoma: is it predictable with pretransplant MRI? AJR Am J Roentgenol. 2011;196:1083–9. https://doi.org/10.2214/AJR.10.4720.

    Article  PubMed  Google Scholar 

  14. Lee JD, Yun M, Lee JM, Choi Y, Choi YH, Kim JS, et al. Analysis of gene expression profiles of hepatocellular carcinomas with regard to 18F-fluorodeoxyglucose uptake pattern on positron emission tomography. Eur J Nucl Med Mol Imaging. 2004;31:1621–30. https://doi.org/10.1007/s00259-004-1602-1.

    CAS  Article  PubMed  Google Scholar 

  15. Lee JD, Yang WI, Park YN, Kim KS, Choi JS, Yun M, et al. Different glucose uptake and glycolytic mechanisms between hepatocellular carcinoma and intrahepatic mass-forming cholangiocarcinoma with increased (18)F-FDG uptake. J Nucl Med. 2005;46:1753–9.

    CAS  PubMed  Google Scholar 

  16. Li YC, Yang CS, Zhou WL, Li HS, Han YJ, Wang QS, et al. Low glucose metabolism in hepatocellular carcinoma with GPC3 expression. World J Gastroenterol. 2018;24:494–503. https://doi.org/10.3748/wjg.v24.i4.494.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Lim C, Salloum C, Chalaye J, Lahat E, Costentin CE, Osseis M, et al. 18F-FDG PET/CT predicts microvascular invasion and early recurrence after liver resection for hepatocellular carcinoma: a prospective observational study. HPB. 2019;21:739–47. https://doi.org/10.1016/j.hpb.2018.10.007.

    Article  PubMed  Google Scholar 

  18. Hyun SH, Eo JS, Song BI, Lee JW, Na SJ, Hong IK, et al. Preoperative prediction of microvascular invasion of hepatocellular carcinoma using (18)F-FDG PET/CT: a multicenter retrospective cohort study. Eur J Nucl Med Mol Imaging. 2018;45:720–6. https://doi.org/10.1007/s00259-017-3880-4.

    CAS  Article  PubMed  Google Scholar 

  19. Moon SH, Kim J, Joung JG, Cha H, Park WY, Ahn JS, et al. Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur J Nucl Med Mol Imaging. 2019;46:446–54. https://doi.org/10.1007/s00259-018-4138-5.

    CAS  Article  PubMed  Google Scholar 

  20. Roayaie S, Blume IN, Thung SN, Guido M, Fiel MI, Hiotis S, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology. 2009;137:850–5. https://doi.org/10.1053/j.gastro.2009.06.003.

    Article  PubMed  Google Scholar 

  21. 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;70:1133–44. https://doi.org/10.1016/j.jhep.2019.02.023.

    Article  PubMed  Google Scholar 

  22. Nioche C, Orlhac F, Boughdad S, Reuze S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786–9. https://doi.org/10.1158/0008-5472.CAN-18-0125.

    CAS  Article  PubMed  Google Scholar 

  23. Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 2018;19:1180–91. https://doi.org/10.1016/S1470-2045(18)30413-3.

    CAS  Article  PubMed  Google Scholar 

  24. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10:7252–9. https://doi.org/10.1158/1078-0432.CCR-04-0713.

    CAS  Article  PubMed  Google Scholar 

  25. Mlynarsky L, Menachem Y, Shibolet O. Treatment of hepatocellular carcinoma: steps forward but still a long way to go. World J Hepatol. 2015;7:566–74. https://doi.org/10.4254/wjh.v7.i3.566.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kobayashi T, Aikata H, Honda F, Nakano N, Nakamura Y, Hatooka M, et al. Preoperative fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography for prediction of microvascular invasion in small hepatocellular carcinoma. J Comput Assist Tomogr. 2016;40:524–30. https://doi.org/10.1097/RCT.0000000000000405.

    Article  PubMed  Google Scholar 

  27. Ahn SY, Lee JM, Joo I, Lee ES, Lee SJ, Cheon GJ, et al. Prediction of microvascular invasion of hepatocellular carcinoma using gadoxetic acid-enhanced MR and (18)F-FDG PET/CT. Abdom Imaging. 2015;40:843–51. https://doi.org/10.1007/s00261-014-0256-0.

    Article  PubMed  Google Scholar 

  28. Cheung TT, Chan SC, Ho CL, Chok KS, Chan AC, Sharr WW, et al. Can positron emission tomography with the dual tracers [11 C]acetate and [18 F]fludeoxyglucose predict microvascular invasion in hepatocellular carcinoma? Liver Transpl. 2011;17:1218–25. https://doi.org/10.1002/lt.22362.

    Article  PubMed  Google Scholar 

  29. Ji GW, Zhu FP, Xu Q, Wang K, Wu MY, Tang WW, et al. Radiomic features at contrast-enhanced CT predict recurrence in early stage hepatocellular carcinoma: a multi-institutional study. Radiology. 2020;191470. https://doi.org/10.1148/radiol.2020191470.

  30. Yang L, Gu D, Wei J, Yang C, Rao S, Wang W, et al. A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer. 2019;8:373–86. https://doi.org/10.1159/000494099.

    CAS  Article  PubMed  Google Scholar 

  31. De Angelis R, Sant M, Coleman MP, Francisci S, Baili P, Pierannunzio D, et al. Cancer survival in Europe 1999-2007 by country and age: results of EUROCARE--5-a population-based study. Lancet Oncol. 2014;15:23–34. https://doi.org/10.1016/S1470-2045(13)70546-1.

    Article  PubMed  Google Scholar 

  32. Fujiwara N, Friedman SL, Goossens N, Hoshida Y. Risk factors and prevention of hepatocellular carcinoma in the era of precision medicine. J Hepatol. 2018;68:526–49. https://doi.org/10.1016/j.jhep.2017.09.016.

    Article  PubMed  Google Scholar 

  33. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. https://doi.org/10.1038/ncomms5006.

    CAS  Article  Google Scholar 

  34. Jiang Y, Yuan Q, Lv W, Xi S, Huang W, Sun Z, et al. Radiomic signature of (18)F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits. Theranostics. 2018;8:5915–28. https://doi.org/10.7150/thno.28018.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kim JD, Cho EJ, Ahn C, Park SK, Choi JY, Lee HC, et al. A model to predict 1-month risk of transplant or death in hepatitis A-related acute liver failure. Hepatology. 2019;70:621–9. https://doi.org/10.1002/hep.30262.

    CAS  Article  PubMed  Google Scholar 

  36. Chen HS, Joo DJ, Shaheen M, Li Y, Wang Y, Yang J, et al. Randomized trial of spheroid reservoir bioartificial liver in porcine model of posthepatectomy liver failure. Hepatology. 2019;69:329–42. https://doi.org/10.1002/hep.30184.

    CAS  Article  PubMed  Google Scholar 

  37. Ampuero J, Jimeno C, Quiles R, Rosales JM, Llerena S, Palomo N, et al. Impact of comorbidities on patient outcomes after interferon-free therapy-induced viral eradication in hepatitis C. J Hepatol. 2018;68:940–8. https://doi.org/10.1016/j.jhep.2017.12.019.

    Article  PubMed  Google Scholar 

Download references

Funding

This study was supported financially by the Science and Technology Program of Guangzhou (201604020094) and the Joint Logistic Support Force Project (CWH17J023).

Author information

Author notes

  1. Youcai Li and Yin Zhang contributed equally to this work.

Affiliations

  1. Department of Nuclear Medicine, The First Affiliated Hospital of Guangzhou Medical University, No. 151, Yanjiang Road, Yuexiu District, Guangzhou, 510000, Guangdong, China

    Youcai Li, Qi Fang, Xiaoyao Zhang, Peng Hou & Xinlu Wang

  2. Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, Guangdong Province, China

    Yin Zhang & Hubing Wu

Authors
  1. Youcai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoyao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peng Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hubing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinlu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Hubing Wu, Xinlu Wang

Methodology: Youcai Li, Yin Zhang

Formal analysis and investigation: Youcai Li, Yin Zhang, Qi Fang, Xiaoyao Zhang, Peng Hou, Hubing Wu, Xinlu Wang

Writing-original draft preparation: Youcai Li, Yin Zhang

Writing-review and editing: Hubing Wu, Xinlu Wang

Funding acquisition: Xinlu Wang

Supervision: Hubing Wu, Xinlu Wang

All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Hubing Wu or Xinlu Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

Ethical approval was waived by the local Ethics Committee of Guangzhou Medical University and Southern Medical University in view of the retrospective nature of the study.

Consent to participate

Due to the retrospective nature of the study, informed consent was not obtained in the study.

Code availability

The code used in the current study is available from the corresponding authors on reasonable request.

Additional information

Publisher’s note

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

This article is part of the Topical Collection on Hematology

Supplementary information

ESM 1

(DOCX 1348 kb).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Zhang, Y., Fang, Q. et al. Radiomics analysis of [18F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma. Eur J Nucl Med Mol Imaging 48, 2599–2614 (2021). https://doi.org/10.1007/s00259-020-05119-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-020-05119-9

Keywords

  • Hepatocellular carcinoma
  • MVI
  • DFS
  • Radiomics
  • [18F]FDG PET/CT model