Skip to main content

Advertisement

SpringerLink
Log in

Prediction of development of hepatocellular carcinoma using a new scoring system involving virtual touch quantification in patients with chronic liver diseases

  • Original Article—Liver, Pancreas, and Biliary Tract
  • Published:
Journal of Gastroenterology Aims and scope Submit manuscript

Abstract

Background

This study aimed to establish a new scoring system that combined several risk factors, including virtual touch quantification (VTQ) values and fasting plasma glucose (FPG) levels, for predicting the development of hepatocellular carcinoma (HCC) in patients with chronic liver disease.

Methods

A total of 1808 chronic liver disease patients who underwent VTQ measurement were analyzed. Risk factors for developing HCC were selected by multivariate Cox proportional hazards models.

Results

VTQ (>1.33 m/s), FPG (≥110 mg/dl), sex (male), age (≥55 years), and α-fetoprotein (AFP) level (≥5 ng/ml) were independently selected as risk factors for HCC development by multivariate analysis. Using these parameters, we established a new scoring system (0 to 5 points), based on VTQ, FPG, sex, age, and AFP level, named VFMAP. As compared with the low VFMAP score group (0 or 1 point), the hazard ratio for the incidence of HCC was 17.37 [95 % confidence interval (CI), 2.35–128.40] in the intermediate-score group (2 or 3 points) and 66.82 (95 % CI, 9.01–495.80) in the high-score group (4 or 5 points). The area under the receiver operating characteristic curve of the VFMAP score for predicting HCC development within 5 years was 0.82 (95 % CI, 0.76–0.87), indicating a moderate diagnostic value. A VFMAP cutoff value of 3 excluded HCC within 5 years with a high negative predictive value (98.2 %).

Conclusion

The VFMAP score accurately predicted HCC in patients with chronic liver disease.

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

Similar content being viewed by others

Abbreviations

HCC:

Hepatocellular carcinoma

HBV:

Hepatitis B

HCV:

Hepatitis C

AFP:

α-Fetoprotein

ARFI:

Acoustic radiation force impulse

US:

Ultrasound

VTQ:

Virtual Touch Quantification

m/s:

Meters/second

FPG:

Fasting plasma glucose

HbA1c:

Hemoglobin A1c

ROI:

Region of interest

CT:

Computed tomography

MRI:

Magnetic resonance imaging

AST:

Aspartate aminotransferase

APRI:

Aspartate aminotransferase/platelet ratio index

ROC:

Receiver operating characteristic

AUC:

Area under the receiver-operating characteristic curve

PPV:

Positive predictive value

NPV:

Negative predictive value

AIC:

Akaike’s information criterion

CI:

Confidence interval

ALT:

Alanine aminotransferase

GGT:

γ-glutamyltransferase

References

  1. El-Serag HB. Epidemiology of hepatocellular carcinoma in USA. Hepatol Res. 2007;37:S88–94.

    Article  PubMed  Google Scholar 

  2. Tsukuma H, Hiyama T, Tanaka S, et al. Risk factors for hepatocellular carcinoma among patients with chronic liver disease. N Engl J Med. 1993;328:1797–801.

    Article  CAS  PubMed  Google Scholar 

  3. Chang KC, Wu YY, Hung CH, et al. Clinical-guide risk prediction of hepatocellular carcinoma development in chronic hepatitis C patients after interferon-based therapy. Br J Cancer. 2013;109:2481–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Oze T, Hiramatsu N, Yakushijin T, et al. Osaka Liver Forum. Post-treatment levels of α-fetoprotein predict incidence of hepatocellular carcinoma after interferon therapy. Clin Gastroenterol Hepatol. 2014;12:1186–95.

    Article  CAS  PubMed  Google Scholar 

  5. Yamada R, Hiramatsu N, Oze T, et al. Osaka Liver Forum. Impact of alpha-fetoprotein on hepatocellular carcinoma development during entecavir treatment of chronic hepatitis B virus infection. J Gastroenterol. 2015;50:785–94.

    Article  CAS  PubMed  Google Scholar 

  6. Zhang DY, Friedman SL. Fibrosis-dependent mechanisms of hepatocarcinogenesis. Hepatology. 2012;56:769–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Castéra L, Nègre I, Samii K, et al. Pain experienced during percutaneous liver biopsy. Hepatology. 1999;30:1529–30.

    Article  PubMed  Google Scholar 

  8. Friedrich-Rust M, Wunder K, Kriener S, et al. Liver fibrosis in viral hepatitis: noninvasive assessment with acoustic radiation force impulse imaging versus transient elastography. Radiology. 2009;252:595–604.

    Article  PubMed  Google Scholar 

  9. Lupsor M, Badea R, Stefanescu H, et al. Performance of a new elastographic method (ARFI technology) compared to unidimensional transient elastography in the noninvasive assessment of chronic hepatitis C. Preliminary results. J Gastrointestin Liver Dis. 2009;18:303–10.

    PubMed  Google Scholar 

  10. Takahashi H, Ono N, Eguchi Y, et al. Evaluation of acoustic radiation force impulse elastography for fibrosis staging of chronic liver disease: a pilot study. Liver Int. 2010;30:538–45.

    Article  PubMed  Google Scholar 

  11. Friedrich-Rust M, Nierhoff J, Lupsor M, et al. Performance of acoustic radiation force impulse imaging for the staging of liver fibrosis: a pooled meta-analysis. J Viral Hepat. 2012;19:e212–9.

    Article  CAS  PubMed  Google Scholar 

  12. Kircheis G, Sagir A, Vogt C, et al. Evaluation of acoustic radiation force impulse imaging for determination of liver stiffness using transient elastography as a reference. World J Gastroenterol. 2012;18:1077–84.

    Article  PubMed  PubMed Central  Google Scholar 

  13. International Diabetes Federation. Diabetes atlas. International Diabetes Federation, 2013.

  14. Giovannucci E, Harlan DM, Archer MC, et al. Diabetes and cancer: a consensus report. Diabetes Care. 2010;33:1674–85.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Elkrief L, Chouinard P, Bendersky N, et al. Diabetes mellitus is an independent prognostic factor for major liver-related outcomes in patients with cirrhosis and chronic hepatitis C. Hepatology. 2014;60:823–31.

    Article  CAS  PubMed  Google Scholar 

  16. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(Suppl 1):S62–9.

    Article  PubMed Central  Google Scholar 

  17. The Japan Society of Hepatology. Surveillance algorithm and diagnostic algorithm for hepatocellular carcinoma: Clinical Practice Guidelines for Hepatocellular Carcinoma. Hepatology Res 2010; 40 Supplement s1: 6–7.

  18. Shen L, Li JQ, Zeng MD, et al. Correlation between ultrasonographic and pathologic diagnosis of liver fibrosis due to chronic virus hepatitis. World J Gastroenterol. 2006;12:1292–5.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Iacobellis A, Fusilli S, Mangia A, et al. Ultrasonographic and biochemical parameters in the non-invasive evaluation of liver fibrosis in hepatitis C virus chronic hepatitis. Aliment Pharmacol Ther. 2005;22:769–74.

    Article  CAS  PubMed  Google Scholar 

  20. Caturelli E, Castellano L, Fusilli S, et al. Coarse nodular US pattern in hepatic cirrhosis: risk for hepatocellular carcinoma. Radiology. 2003;226:691–7.

    Article  PubMed  Google Scholar 

  21. Wai CT, Greenson JK, Fontana RJ, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38:518–26.

    Article  PubMed  Google Scholar 

  22. Akobeng AK. Understanding diagnostic tests 3: receiver operating characteristic curves. Acta Paediatr. 2007;96:644–7.

    Article  PubMed  Google Scholar 

  23. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–5.

    Article  CAS  PubMed  Google Scholar 

  24. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240:1285–93.

    Article  CAS  PubMed  Google Scholar 

  25. Yamaoka K, Nakagawa T, Uno T. Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J Pharmacokinet Biopharm. 1978;6:165–75.

    Article  CAS  PubMed  Google Scholar 

  26. Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48:452–8.

    Article  CAS  PubMed  Google Scholar 

  27. American Diabetes Association. Standards of medical care in diabetes–2012. Diabetes Care. 2012;35(Suppl 1):S11–63.

    Google Scholar 

  28. Sporea I, Bota S, Peck-Radosavljevic M, et al. Acoustic radiation force impulse elastography for fibrosis evaluation in patients with chronic hepatitis C: an international multicenter study. Eur J Radiol. 2012;81:4112–8.

    Article  PubMed  Google Scholar 

  29. Akima T, Tamano M, Hiraishi H. Liver stiffness measured by transient elastography is a predictor of hepatocellular carcinoma development in viral hepatitis. Hepatol Res. 2011;41:965–70.

    Article  PubMed  Google Scholar 

  30. Jung KS, Kim SU, Ahn SH, et al. Risk assessment of hepatitis B virus-related hepatocellular carcinoma development using liver stiffness measurement (FibroScan). Hepatology. 2011;53:885–94.

    Article  CAS  PubMed  Google Scholar 

  31. Wang HM, Hung CH, Lu SN, et al. Liver stiffness measurement as an alternative to fibrotic stage in risk assessment of hepatocellular carcinoma incidence for chronic hepatitis C patients. Liver Int. 2013;33:756–61.

    Article  CAS  PubMed  Google Scholar 

  32. Masuzaki R, Tateishi R, Yoshida H, et al. Prospective risk assessment for hepatocellular carcinoma development in patients with chronic hepatitis C by transient elastography. Hepatology. 2009;49:1954–61.

    Article  PubMed  Google Scholar 

  33. Singh S, Fujii LL, Murad MH, et al. Liver stiffness is associated with risk of decompensation, liver cancer, and death in patients with chronic liver diseases: a systematic review and meta-analysis. Clin Gastroenterol Hepatol. 2013;11:1573–84.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sporea I, Sirli R, Bota S, et al. Is ARFI elastography reliable for predicting fibrosis severity in chronic HCV hepatitis? World J Radiol. 2011;28(3):188–93.

    Article  Google Scholar 

  35. Rizzo L, Calvaruso V, Cacopardo B, et al. Comparison of transient elastography and acoustic radiation force impulse for non-invasive staging of liver fibrosis in patients with chronic hepatitis C. Am J Gastroenterol. 2011;106:2112–20.

    Article  CAS  PubMed  Google Scholar 

  36. Tsilidis KK, Kasimis JC, Lopez DS, et al. Type 2 diabetes and cancer: umbrella review of meta-analyses of observational studies. BMJ. 2015;350:g7607.

    Article  PubMed  Google Scholar 

  37. de Groot M, Anderson R, Freedland KE, et al. Association of depression and diabetes complications: a meta-analysis. Psychosom Med. 2001;63:619–30.

    Article  PubMed  Google Scholar 

  38. Koh WP, Wang R, Jin A, et al. Diabetes mellitus and risk of hepatocellular carcinoma: findings from the Singapore Chinese Health Study. Br J Cancer. 2013;108:1182–8.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Aizawa N, Enomoto H, Imanishi H, et al. Elevation of the glycated albumin to glycated hemoglobin ratio during the progression of hepatitis C virus related liver fibrosis. World J Hepatol. 2012;4:11–7.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hosaka T, Suzuki F, Kobayashi M, et al. Long-term entecavir treatment reduces hepatocellular carcinoma incidence in patients with hepatitis B virus infection. Hepatology. 2013;58:98–107.

    Article  CAS  PubMed  Google Scholar 

  41. Kumada T, Toyoda H, Tada T, et al. Effect of nucleos(t)ide analogue therapy on hepatocarcinogenesis in chronic hepatitis B patients: a propensity score analysis. J Hepatol. 2013;58:427–33.

    Article  CAS  PubMed  Google Scholar 

  42. Kasahara A, Hayashi N, Mochizuki K, et al. Risk factors for hepatocellular carcinoma and its incidence after interferon treatment in patients with chronic hepatitis C. Osaka Liver Disease Study Group. Hepatology. 1998;27:1394–402.

    CAS  Google Scholar 

Download references

Acknowledgments

We thank Masahiro Yoshida at Ultrasound Imaging Center, Hyogo College of Medicine, for his valuable help with obtaining ultrasound examinations. This work was supported by a Grant-in-Aid for Researchers, Hyogo College of Medicine, 2014, and a Grants-in-Aid for Scientific Research (C) 15K09029 (JSPS KAKENHI).

Author information

Authors and Affiliations

  1. Division of Hepatobiliary and Pancreatic Disease, Department of Internal Medicine, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan

    Tomoko Aoki, Hiroko Iijima, Takashi Nishimura, Chikage Nakano, Kyohei Kishino, Yoshihiro Shimono, Kazunori Yoh, Ryo Takata, Akio Ishii, Tomoyuki Takashima, Yoshiyuki Sakai, Nobuhiro Aizawa, Hiroki Nishikawa, Naoto Ikeda, Yoshinori Iwata, Hirayuki Enomoto & Shuhei Nishiguchi

  2. Hyogo College of Medicine, Ultrasound Imaging Center, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan

    Tomoko Aoki, Hiroko Iijima, Takashi Nishimura & Chikage Nakano

  3. Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, 4-86 Minaminokawa, Ogaki, Gifu, 503-8502, Japan

    Toshifumi Tada & Takashi Kumada

  4. Department of Surgical Pathology, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan

    Seiichi Hirota

  5. Department of Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan

    Jiro Fujimoto

Authors
  1. Tomoko Aoki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiroko Iijima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Toshifumi Tada
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Takashi Kumada
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Takashi Nishimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chikage Nakano
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kyohei Kishino
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yoshihiro Shimono
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kazunori Yoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ryo Takata
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Akio Ishii
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Tomoyuki Takashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yoshiyuki Sakai
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Nobuhiro Aizawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Hiroki Nishikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Naoto Ikeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yoshinori Iwata
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Hirayuki Enomoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Seiichi Hirota
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jiro Fujimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Shuhei Nishiguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroko Iijima.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Financial support

This work was supported by a Grant-in-Aid for Researchers, Hyogo College of Medicine, 2014, and a Grants-in-Aid for Scientific Research (C) 15K09029 (JSPS KAKENHI).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PPTX 87 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aoki, T., Iijima, H., Tada, T. et al. Prediction of development of hepatocellular carcinoma using a new scoring system involving virtual touch quantification in patients with chronic liver diseases. J Gastroenterol 52, 104–112 (2017). https://doi.org/10.1007/s00535-016-1228-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00535-016-1228-7

Keywords

Search

Navigation